{"id":"b82a068a-49eb-47b6-9131-5816b6692ddb","entityType":"agent","slug":"clawhub-dannyshmueli-agent-analytics-autoresearch","name":"agent-analytics-autoresearch","canonicalUrl":"https://www.xpersona.co/agent/clawhub-dannyshmueli-agent-analytics-autoresearch","canonicalPath":"/agent/clawhub-dannyshmueli-agent-analytics-autoresearch","generatedAt":"2026-10-11T07:41:11.464Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-11T04:34:44.155Z","emptyReason":null},"description":"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API. Skill: agent-analytics-autoresearch Owner: dannyshmueli Summary: Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API. Tags:","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. 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Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\n\nTags: ab-testing:1.0.3, analytics:1.0.10, autoresearch:1.0.3, experiments:1.0.3, growth:1.0.10, latest:1.0.12, research:1.0.10\n\nVersion history:\n\nv1.0.12 | 2026-10-09T11:57:45.276Z | user\n\nUpdate official Agent Analytics CLI examples to 0.5.37.\n\nv1.0.11 | 2026-10-09T10:08:32.131Z | user\n\nPin the published CLI 0.5.36 in analytics collection workflows.\n\nv1.0.10 | 2026-10-09T05:56:48.354Z | user\n\nPin snapshot commands and templates to CLI 0.5.35 country reporting release.\n\nv1.0.9 | 2026-05-09T19:57:32.985Z | user\n\nUse canonical Agent-Analytics/skills repository URL.\n\nv1.0.8 | 2026-05-09T19:55:06.884Z | user\n\nAlign autoresearch skill with the redesigned Agent Analytics skills package.\n\nv1.0.7 | 2026-05-01T16:47:01.606Z | user\n\nPin autoresearch examples and scripts to @agent-analytics/cli@0.5.28.\n\nv1.0.6 | 2026-04-19T20:51:45.675Z | user\n\nInclude the autoresearch results header template in ClawHub installs.\n\nv1.0.5 | 2026-04-19T20:49:03.775Z | user\n\nAlign autoresearch helper scripts with the pinned Agent Analytics CLI.\n\nv1.0.4 | 2026-04-19T20:46:48.882Z | user\n\nTeach self-improving project context: read project context before analysis, save durable activation/event/glossary learning, and skip stale observations.\n\nv1.0.3 | 2026-04-16T06:08:24.250Z | user\n\nUse @agent-analytics/cli@0.5.14 for snapshot collection and keep the growth-loop skill aligned with the current detached-login CLI release.\n\nv1.0.1 | 2026-04-15T21:09:06.511Z | user\n\nPin Agent Analytics CLI data collection commands to @agent-analytics/cli@0.5.12.\n\nv1.0.0 | 2026-04-15T20:58:12.310Z | user\n\nInitial autoresearch growth-loop skill with templates and Agent Analytics snapshot helpers\n\nArchive index:\n\nArchive v1.0.12: 9 files, 10252 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), references/results-header.txt (92b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), skill-card.md (2160b), SKILL.md (7817b), _meta.json (148b)\n\nFile v1.0.12:SKILL.md\n\n---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.12\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/Agent-Analytics/skills\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapshot, fold `project_context` into the product truth and metric definitions, and keep activation/event meaning separate per project or domain. After a human correction, scanner result, completed experiment, or repeated measured finding, update context only with durable product truth. Save activation definitions, event meanings, stable goals, and confirmed interpretations; skip weekly numbers, temporary spikes, pasted reports, PII, and unconfirmed guesses.\n\n## Quick Start\n\nIf the user already has a repo or run folder, work there. Otherwise initialize a run:\n\n```bash\nbash /scripts/init_autoresearch_run.sh homepage-signup\n```\n\nThen fill `brief.md`, collect or paste data, and run the loop:\n\n```text\nRead brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review.\n```\n\nWhen using Agent Analytics, collect a snapshot:\n\n```bash\nbash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click\n```\n\nIf `` is not obvious in the runtime, read the script from this skill's `scripts/` folder and run an equivalent local command.\n\n## References\n\nLoad these files only when needed:\n\n- `references/program.md` - exact loop instructions.\n- `references/brief-template.md` - project brief template.\n- `references/final-variants-template.md` - final output template.\n- `references/results-header.txt` - exact `results.tsv` header.\n\n## Loop Shape\n\n### Inner Autoresearch Loop\n\n1. Define the surface, control, audience, product truth, metric, proxy, and guardrails.\n2. Collect or read a dated analytics snapshot.\n3. Summarize useful signals and data limitations.\n4. Generate candidate A.\n5. Critique A harshly for genericness, drift, unsupported claims, weak conversion intent, and competitor-sayable language.\n6. Write candidate B from the critique.\n7. Synthesize AB from the strongest parts of A and B.\n8. Blind-rank A, B, and AB with Borda scoring.\n9. Append one TSV-safe row to `results.tsv`.\n10. Repeat several rounds.\n11. Write `final_variants.md` with two distinct variants and the recommended experiment shape.\n\n### Outer Experiment Loop\n\nOnly run this phase when the user explicitly approves implementation or experiment setup.\n\n1. Implement the approved variant or variants in the target product surface.\n2. Create the experiment with a control and the approved candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect real behavior for the requested window.\n5. Pull experiment results, screenshots or changed-copy notes, funnel movement, guardrails, and data limitations into a new snapshot.\n6. Start the next inner autoresearch loop from that measured evidence.\n\nThe outer loop prevents the LLM panel from becoming the final judge. LLMs generate and criticize, humans approve risk, and users decide what worked.\n\n## Agent Analytics Snapshot\n\nUse the official CLI when collecting live Agent Analytics data:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.37 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.37 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.37 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.37 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.37 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.37 experiments list \"$PROJECT_SLUG\"\n```\n\nIf login is needed, prefer the regular `agent-analytics` skill's browser approval or detached login guidance.\n\nBefore interpreting the snapshot, also read the compact project memory:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.37 context get \"$PROJECT_SLUG\"\n```\n\nIf the autoresearch run reveals durable product truth that should guide future analytics, use the regular `agent-analytics` skill's project context workflow to read the existing context, merge the compact update, and write it back. Do not store raw round notes or time-bound metric values as project context.\n\n## Scoring\n\nUse Borda scoring:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nJudge by:\n\n- specificity to the product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with analytics data\n- respect for guardrails\n\n## Output\n\n`final_variants.md` must include:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n- clear note that the experiment has not been wired yet\n\nOnly create or wire an experiment after explicit human approval.\n\nFile v1.0.12:_meta.json\n\n{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.12\",\n \"publishedAt\": 1791547065276\n}\n\nFile v1.0.12:references/brief-template.md\n\n# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.37 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.37 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.37 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.37 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.37 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.37 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.37 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project list\n```\n\n## Live Data Snapshot\n\nSummary:\n\n-\n\nPrimary event:\n\n-\n\nProxy event:\n\n-\n\nGuardrails:\n\n-\n\nData limitations:\n\n-\n\n## Drift Constraints\n\nCandidates must not:\n\n-\n\nCandidates should:\n\n-\n\n## Judging Rubric\n\nRank candidates by:\n\n- specificity to this product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with available analytics data\n\nFile v1.0.12:references/final-variants-template.md\n\n# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step\n\nFile v1.0.12:references/program.md\n\n# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project described in `brief.md`.\n\nDo not change product code while running the loop. Produce reviewable copy artifacts first.\n\nDefault mode is review-only. Only move into implementation, experiment creation, or measurement after the user explicitly approves that next phase.\n\n## Setup\n\n1. Read `brief.md` fully.\n2. Treat `brief.md` as the source of truth for the project, audience, surface, control, metrics, analytics data, and drift constraints.\n3. If `results.tsv` does not exist, create it with:\n\n```tsv\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n```\n\n4. If `final_variants.md` exists, overwrite it only at the end of a completed loop.\n\n## Data Brief\n\nBefore generating copy, collect or read the data described in `brief.md`.\n\nThe data source can be Agent Analytics, another analytics CLI, an API, SQL, CSV, exported reports, product logs, or manually supplied data.\n\nRules:\n\n- Never invent numbers.\n- If a command fails, record the failure.\n- Treat missing data as missing data, not zero data.\n- Separate primary outcome data from proxy and guardrail data.\n- If data is sparse, say so and treat it as weak signal.\n\n## Scope\n\nDuring the loop, edit only:\n\n- `results.tsv`\n- `final_variants.md`\n- optional scratch notes\n\nDo not edit the live site, app, product code, or experiment setup until the variants have been reviewed.\n\n## Product Truth\n\nEvery candidate must preserve the product truth from `brief.md`.\n\nPenalize:\n\n- generic category language\n- copy a competitor could say word for word\n- unsupported claims\n- drift away from the real product value\n- clickbait that weakens primary conversion intent\n- changes that ignore the current control's strengths\n\nReward:\n\n- specificity\n- clear audience fit\n- concrete user outcome\n- stronger primary-event intent\n- honest use of the available data\n- language only this product could credibly say\n\n## Loop\n\nRun at least 5 rounds unless `brief.md` specifies a different count.\n\nEach round has four phases.\n\n### 1. Candidate A\n\nFor round 1, candidate A is your first new hypothesis based on the brief, control, and data.\n\nFor later rounds, candidate A is the previous round winner.\n\nInclude the editable parts named in the brief, usually headline, subheadline, CTA, supporting copy, and hypothesis.\n\n### 2. Critique\n\nCritique candidate A harshly:\n\n- what is generic\n- what a competitor could say\n- where value is unclear\n- where copy drifts from product truth\n- whether primary-event intent is strong enough\n- whether the control is clearer\n\n### 3. Candidate B And Synthesis AB\n\nWrite candidate B from the critique as if starting fresh. It may use the brief and critique, but it should not be a mild rewrite of A.\n\nThen write candidate AB by combining the strongest parts of A and B. Do not average them into bland middle copy.\n\n### 4. Blind Borda Ranking\n\nBlind-rank A, B, and AB. To simulate blind judging, anonymize them as `option_1`, `option_2`, and `option_3` in a different order each round before scoring.\n\nScore:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nUse the rubric in `brief.md`. If none exists, rank by specificity, clarity, primary-event intent, product truth, low competitor-sayable language, and fit with analytics data.\n\nAppend one row to `results.tsv` after each round. Keep rationale short and TSV-safe.\n\nWinner becomes candidate A for the next round.\n\n## Final Selection\n\nAfter the final round, choose the two strongest distinct candidates. They should not be tiny wording variations of each other.\n\nWrite `final_variants.md` with:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n\nEnd with a clear note that the experiment has not been wired yet.\n\n## Approved Outer Experiment Loop\n\nRun this section only if the user explicitly asks you to implement or wire the approved experiment.\n\n1. Implement the approved variant or variants in the product surface named in `brief.md`.\n2. Create the experiment with the recommended control and candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect behavior for the requested window.\n5. Pull the experiment results into a new dated data snapshot, including:\n - winning and losing variants\n - primary metric movement\n - proxy metric movement\n - guardrail movement\n - screenshots or changed-copy notes\n - data limitations\n6. Start the next autoresearch loop from that measured evidence.\n\nThe LLM loop generates pressure. The outer experiment loop decides what survived contact with users.\n\nFile v1.0.12:references/results-header.txt\n\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n\nFile v1.0.12:skill-card.md\n\n## Description:\n\nUses analytics and product constraints to propose, critique, and rank two review-ready growth experiment variants.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dannyshmueli](https://clawhub.ai/user/dannyshmueli)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nGrowth teams, product managers, and developers use this skill to turn analytics and a product brief into two ranked, review-ready A/B test variants for landing pages, onboarding, or pricing. Live changes require explicit approval.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Analytics snapshots may contain sensitive project or user data.\n\nMitigation: Use least-privilege analytics access and review local snapshots before sharing or committing them.\n\nRisk: Proposed copy or an experiment may be inaccurate or change live product behavior.\n\nMitigation: Review claims and guardrails, and require explicit approval before changing product copy or wiring an experiment.\n\n## Reference(s):\n\n- [ClawHub skill release](https://clawhub.ai/dannyshmueli/skills/agent-analytics-autoresearch)\n- [Autoresearch Growth template](https://github.com/Agent-Analytics/autoresearch-growth)\n- [Agent Analytics](https://agentanalytics.sh)\n- [Agent Analytics skill](https://github.com/Agent-Analytics/skills/tree/main/skills/agent-analytics)\n- [Growth loop reference](references/program.md)\n\n## Skill Output:\n\n**Output Type(s):** [Markdown, Text, Shell commands, Code]\n\n**Output Format:** [Markdown variant brief and tab-separated round results; optional implementation after approval]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Two ranked variants with exact copy, rationale, risks, experiment shape, and data limitations; no live experiment by default.]\n\n## Skill Version(s):\n\n1.0.12 (source: ClawHub release metadata and skill frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.11: 9 files, 10371 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), references/results-header.txt (92b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), skill-card.md (2340b), SKILL.md (7817b), _meta.json (148b)\n\nFile v1.0.11:SKILL.md\n\n---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.11\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/Agent-Analytics/skills\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapshot, fold `project_context` into the product truth and metric definitions, and keep activation/event meaning separate per project or domain. After a human correction, scanner result, completed experiment, or repeated measured finding, update context only with durable product truth. Save activation definitions, event meanings, stable goals, and confirmed interpretations; skip weekly numbers, temporary spikes, pasted reports, PII, and unconfirmed guesses.\n\n## Quick Start\n\nIf the user already has a repo or run folder, work there. Otherwise initialize a run:\n\n```bash\nbash /scripts/init_autoresearch_run.sh homepage-signup\n```\n\nThen fill `brief.md`, collect or paste data, and run the loop:\n\n```text\nRead brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review.\n```\n\nWhen using Agent Analytics, collect a snapshot:\n\n```bash\nbash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click\n```\n\nIf `` is not obvious in the runtime, read the script from this skill's `scripts/` folder and run an equivalent local command.\n\n## References\n\nLoad these files only when needed:\n\n- `references/program.md` - exact loop instructions.\n- `references/brief-template.md` - project brief template.\n- `references/final-variants-template.md` - final output template.\n- `references/results-header.txt` - exact `results.tsv` header.\n\n## Loop Shape\n\n### Inner Autoresearch Loop\n\n1. Define the surface, control, audience, product truth, metric, proxy, and guardrails.\n2. Collect or read a dated analytics snapshot.\n3. Summarize useful signals and data limitations.\n4. Generate candidate A.\n5. Critique A harshly for genericness, drift, unsupported claims, weak conversion intent, and competitor-sayable language.\n6. Write candidate B from the critique.\n7. Synthesize AB from the strongest parts of A and B.\n8. Blind-rank A, B, and AB with Borda scoring.\n9. Append one TSV-safe row to `results.tsv`.\n10. Repeat several rounds.\n11. Write `final_variants.md` with two distinct variants and the recommended experiment shape.\n\n### Outer Experiment Loop\n\nOnly run this phase when the user explicitly approves implementation or experiment setup.\n\n1. Implement the approved variant or variants in the target product surface.\n2. Create the experiment with a control and the approved candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect real behavior for the requested window.\n5. Pull experiment results, screenshots or changed-copy notes, funnel movement, guardrails, and data limitations into a new snapshot.\n6. Start the next inner autoresearch loop from that measured evidence.\n\nThe outer loop prevents the LLM panel from becoming the final judge. LLMs generate and criticize, humans approve risk, and users decide what worked.\n\n## Agent Analytics Snapshot\n\nUse the official CLI when collecting live Agent Analytics data:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.36 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.36 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.36 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.36 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.36 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.36 experiments list \"$PROJECT_SLUG\"\n```\n\nIf login is needed, prefer the regular `agent-analytics` skill's browser approval or detached login guidance.\n\nBefore interpreting the snapshot, also read the compact project memory:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.36 context get \"$PROJECT_SLUG\"\n```\n\nIf the autoresearch run reveals durable product truth that should guide future analytics, use the regular `agent-analytics` skill's project context workflow to read the existing context, merge the compact update, and write it back. Do not store raw round notes or time-bound metric values as project context.\n\n## Scoring\n\nUse Borda scoring:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nJudge by:\n\n- specificity to the product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with analytics data\n- respect for guardrails\n\n## Output\n\n`final_variants.md` must include:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n- clear note that the experiment has not been wired yet\n\nOnly create or wire an experiment after explicit human approval.\n\nFile v1.0.11:_meta.json\n\n{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.11\",\n \"publishedAt\": 1791540512131\n}\n\nFile v1.0.11:references/brief-template.md\n\n# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.36 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.36 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.36 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.36 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.36 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.36 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.36 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project list\n```\n\n## Live Data Snapshot\n\nSummary:\n\n-\n\nPrimary event:\n\n-\n\nProxy event:\n\n-\n\nGuardrails:\n\n-\n\nData limitations:\n\n-\n\n## Drift Constraints\n\nCandidates must not:\n\n-\n\nCandidates should:\n\n-\n\n## Judging Rubric\n\nRank candidates by:\n\n- specificity to this product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with available analytics data\n\nFile v1.0.11:references/final-variants-template.md\n\n# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step\n\nFile v1.0.11:references/program.md\n\n# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project described in `brief.md`.\n\nDo not change product code while running the loop. Produce reviewable copy artifacts first.\n\nDefault mode is review-only. Only move into implementation, experiment creation, or measurement after the user explicitly approves that next phase.\n\n## Setup\n\n1. Read `brief.md` fully.\n2. Treat `brief.md` as the source of truth for the project, audience, surface, control, metrics, analytics data, and drift constraints.\n3. If `results.tsv` does not exist, create it with:\n\n```tsv\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n```\n\n4. If `final_variants.md` exists, overwrite it only at the end of a completed loop.\n\n## Data Brief\n\nBefore generating copy, collect or read the data described in `brief.md`.\n\nThe data source can be Agent Analytics, another analytics CLI, an API, SQL, CSV, exported reports, product logs, or manually supplied data.\n\nRules:\n\n- Never invent numbers.\n- If a command fails, record the failure.\n- Treat missing data as missing data, not zero data.\n- Separate primary outcome data from proxy and guardrail data.\n- If data is sparse, say so and treat it as weak signal.\n\n## Scope\n\nDuring the loop, edit only:\n\n- `results.tsv`\n- `final_variants.md`\n- optional scratch notes\n\nDo not edit the live site, app, product code, or experiment setup until the variants have been reviewed.\n\n## Product Truth\n\nEvery candidate must preserve the product truth from `brief.md`.\n\nPenalize:\n\n- generic category language\n- copy a competitor could say word for word\n- unsupported claims\n- drift away from the real product value\n- clickbait that weakens primary conversion intent\n- changes that ignore the current control's strengths\n\nReward:\n\n- specificity\n- clear audience fit\n- concrete user outcome\n- stronger primary-event intent\n- honest use of the available data\n- language only this product could credibly say\n\n## Loop\n\nRun at least 5 rounds unless `brief.md` specifies a different count.\n\nEach round has four phases.\n\n### 1. Candidate A\n\nFor round 1, candidate A is your first new hypothesis based on the brief, control, and data.\n\nFor later rounds, candidate A is the previous round winner.\n\nInclude the editable parts named in the brief, usually headline, subheadline, CTA, supporting copy, and hypothesis.\n\n### 2. Critique\n\nCritique candidate A harshly:\n\n- what is generic\n- what a competitor could say\n- where value is unclear\n- where copy drifts from product truth\n- whether primary-event intent is strong enough\n- whether the control is clearer\n\n### 3. Candidate B And Synthesis AB\n\nWrite candidate B from the critique as if starting fresh. It may use the brief and critique, but it should not be a mild rewrite of A.\n\nThen write candidate AB by combining the strongest parts of A and B. Do not average them into bland middle copy.\n\n### 4. Blind Borda Ranking\n\nBlind-rank A, B, and AB. To simulate blind judging, anonymize them as `option_1`, `option_2`, and `option_3` in a different order each round before scoring.\n\nScore:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nUse the rubric in `brief.md`. If none exists, rank by specificity, clarity, primary-event intent, product truth, low competitor-sayable language, and fit with analytics data.\n\nAppend one row to `results.tsv` after each round. Keep rationale short and TSV-safe.\n\nWinner becomes candidate A for the next round.\n\n## Final Selection\n\nAfter the final round, choose the two strongest distinct candidates. They should not be tiny wording variations of each other.\n\nWrite `final_variants.md` with:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n\nEnd with a clear note that the experiment has not been wired yet.\n\n## Approved Outer Experiment Loop\n\nRun this section only if the user explicitly asks you to implement or wire the approved experiment.\n\n1. Implement the approved variant or variants in the product surface named in `brief.md`.\n2. Create the experiment with the recommended control and candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect behavior for the requested window.\n5. Pull the experiment results into a new dated data snapshot, including:\n - winning and losing variants\n - primary metric movement\n - proxy metric movement\n - guardrail movement\n - screenshots or changed-copy notes\n - data limitations\n6. Start the next autoresearch loop from that measured evidence.\n\nThe LLM loop generates pressure. The outer experiment loop decides what survived contact with users.\n\nFile v1.0.11:references/results-header.txt\n\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n\nFile v1.0.11:skill-card.md\n\n## Description:\n\nRuns an analytics-informed growth loop that drafts, critiques, and ranks copy variants for review before an A/B test.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dannyshmueli](https://clawhub.ai/user/dannyshmueli)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nGrowth teams, marketers, and developers use this skill to turn project analytics or supplied reports into two review-ready landing-page, onboarding, pricing, or conversion-copy variants. Implementation and experiment setup require explicit approval.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Saved analytics snapshots or local research files may contain sensitive event or experiment information.\n\nMitigation: Review the generated data directory and limit access or retention before sharing results.\n\nRisk: Implementing proposed copy or creating an experiment could change a live product.\n\nMitigation: Keep the default run review-only and require explicit approval before product-code changes or experiment setup.\n\nRisk: Sparse or failed analytics collection can make variant recommendations unreliable.\n\nMitigation: Record command failures and data limitations; do not treat missing data as zero or invent metrics.\n\n## Reference(s):\n\n- [ClawHub skill release](https://clawhub.ai/dannyshmueli/skills/agent-analytics-autoresearch)\n- [Autoresearch Growth template](https://github.com/Agent-Analytics/autoresearch-growth)\n- [Agent Analytics](https://agentanalytics.sh)\n- [Growth loop instructions](references/program.md)\n- [Project brief template](references/brief-template.md)\n\n## Skill Output:\n\n**Output Type(s):** [Markdown, Text, Guidance]\n\n**Output Format:** [Markdown variant proposals and tab-separated round results]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Produces two distinct copy variants, rationales, risks, and an experiment recommendation; may save dated analytics snapshots locally.]\n\n## Skill Version(s):\n\n1.0.11 (source: server-resolved release metadata and skill frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.10: 9 files, 10210 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), references/results-header.txt (92b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), skill-card.md (1989b), SKILL.md (7817b), _meta.json (148b)\n\nFile v1.0.10:SKILL.md\n\n---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.10\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/Agent-Analytics/skills\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapshot, fold `project_context` into the product truth and metric definitions, and keep activation/event meaning separate per project or domain. After a human correction, scanner result, completed experiment, or repeated measured finding, update context only with durable product truth. Save activation definitions, event meanings, stable goals, and confirmed interpretations; skip weekly numbers, temporary spikes, pasted reports, PII, and unconfirmed guesses.\n\n## Quick Start\n\nIf the user already has a repo or run folder, work there. Otherwise initialize a run:\n\n```bash\nbash /scripts/init_autoresearch_run.sh homepage-signup\n```\n\nThen fill `brief.md`, collect or paste data, and run the loop:\n\n```text\nRead brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review.\n```\n\nWhen using Agent Analytics, collect a snapshot:\n\n```bash\nbash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click\n```\n\nIf `` is not obvious in the runtime, read the script from this skill's `scripts/` folder and run an equivalent local command.\n\n## References\n\nLoad these files only when needed:\n\n- `references/program.md` - exact loop instructions.\n- `references/brief-template.md` - project brief template.\n- `references/final-variants-template.md` - final output template.\n- `references/results-header.txt` - exact `results.tsv` header.\n\n## Loop Shape\n\n### Inner Autoresearch Loop\n\n1. Define the surface, control, audience, product truth, metric, proxy, and guardrails.\n2. Collect or read a dated analytics snapshot.\n3. Summarize useful signals and data limitations.\n4. Generate candidate A.\n5. Critique A harshly for genericness, drift, unsupported claims, weak conversion intent, and competitor-sayable language.\n6. Write candidate B from the critique.\n7. Synthesize AB from the strongest parts of A and B.\n8. Blind-rank A, B, and AB with Borda scoring.\n9. Append one TSV-safe row to `results.tsv`.\n10. Repeat several rounds.\n11. Write `final_variants.md` with two distinct variants and the recommended experiment shape.\n\n### Outer Experiment Loop\n\nOnly run this phase when the user explicitly approves implementation or experiment setup.\n\n1. Implement the approved variant or variants in the target product surface.\n2. Create the experiment with a control and the approved candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect real behavior for the requested window.\n5. Pull experiment results, screenshots or changed-copy notes, funnel movement, guardrails, and data limitations into a new snapshot.\n6. Start the next inner autoresearch loop from that measured evidence.\n\nThe outer loop prevents the LLM panel from becoming the final judge. LLMs generate and criticize, humans approve risk, and users decide what worked.\n\n## Agent Analytics Snapshot\n\nUse the official CLI when collecting live Agent Analytics data:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.35 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.35 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.35 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.35 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.35 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.35 experiments list \"$PROJECT_SLUG\"\n```\n\nIf login is needed, prefer the regular `agent-analytics` skill's browser approval or detached login guidance.\n\nBefore interpreting the snapshot, also read the compact project memory:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.35 context get \"$PROJECT_SLUG\"\n```\n\nIf the autoresearch run reveals durable product truth that should guide future analytics, use the regular `agent-analytics` skill's project context workflow to read the existing context, merge the compact update, and write it back. Do not store raw round notes or time-bound metric values as project context.\n\n## Scoring\n\nUse Borda scoring:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nJudge by:\n\n- specificity to the product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with analytics data\n- respect for guardrails\n\n## Output\n\n`final_variants.md` must include:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n- clear note that the experiment has not been wired yet\n\nOnly create or wire an experiment after explicit human approval.\n\nFile v1.0.10:_meta.json\n\n{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.10\",\n \"publishedAt\": 1791525408354\n}\n\nFile v1.0.10:references/brief-template.md\n\n# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.35 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.35 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.35 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.35 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.35 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.35 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.35 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project list\n```\n\n## Live Data Snapshot\n\nSummary:\n\n-\n\nPrimary event:\n\n-\n\nProxy event:\n\n-\n\nGuardrails:\n\n-\n\nData limitations:\n\n-\n\n## Drift Constraints\n\nCandidates must not:\n\n-\n\nCandidates should:\n\n-\n\n## Judging Rubric\n\nRank candidates by:\n\n- specificity to this product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with available analytics data\n\nFile v1.0.10:references/final-variants-template.md\n\n# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step\n\nFile v1.0.10:references/program.md\n\n# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project described in `brief.md`.\n\nDo not change product code while running the loop. Produce reviewable copy artifacts first.\n\nDefault mode is review-only. Only move into implementation, experiment creation, or measurement after the user explicitly approves that next phase.\n\n## Setup\n\n1. Read `brief.md` fully.\n2. Treat `brief.md` as the source of truth for the project, audience, surface, control, metrics, analytics data, and drift constraints.\n3. If `results.tsv` does not exist, create it with:\n\n```tsv\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n```\n\n4. If `final_variants.md` exists, overwrite it only at the end of a completed loop.\n\n## Data Brief\n\nBefore generating copy, collect or read the data described in `brief.md`.\n\nThe data source can be Agent Analytics, another analytics CLI, an API, SQL, CSV, exported reports, product logs, or manually supplied data.\n\nRules:\n\n- Never invent numbers.\n- If a command fails, record the failure.\n- Treat missing data as missing data, not zero data.\n- Separate primary outcome data from proxy and guardrail data.\n- If data is sparse, say so and treat it as weak signal.\n\n## Scope\n\nDuring the loop, edit only:\n\n- `results.tsv`\n- `final_variants.md`\n- optional scratch notes\n\nDo not edit the live site, app, product code, or experiment setup until the variants have been reviewed.\n\n## Product Truth\n\nEvery candidate must preserve the product truth from `brief.md`.\n\nPenalize:\n\n- generic category language\n- copy a competitor could say word for word\n- unsupported claims\n- drift away from the real product value\n- clickbait that weakens primary conversion intent\n- changes that ignore the current control's strengths\n\nReward:\n\n- specificity\n- clear audience fit\n- concrete user outcome\n- stronger primary-event intent\n- honest use of the available data\n- language only this product could credibly say\n\n## Loop\n\nRun at least 5 rounds unless `brief.md` specifies a different count.\n\nEach round has four phases.\n\n### 1. Candidate A\n\nFor round 1, candidate A is your first new hypothesis based on the brief, control, and data.\n\nFor later rounds, candidate A is the previous round winner.\n\nInclude the editable parts named in the brief, usually headline, subheadline, CTA, supporting copy, and hypothesis.\n\n### 2. Critique\n\nCritique candidate A harshly:\n\n- what is generic\n- what a competitor could say\n- where value is unclear\n- where copy drifts from product truth\n- whether primary-event intent is strong enough\n- whether the control is clearer\n\n### 3. Candidate B And Synthesis AB\n\nWrite candidate B from the critique as if starting fresh. It may use the brief and critique, but it should not be a mild rewrite of A.\n\nThen write candidate AB by combining the strongest parts of A and B. Do not average them into bland middle copy.\n\n### 4. Blind Borda Ranking\n\nBlind-rank A, B, and AB. To simulate blind judging, anonymize them as `option_1`, `option_2`, and `option_3` in a different order each round before scoring.\n\nScore:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nUse the rubric in `brief.md`. If none exists, rank by specificity, clarity, primary-event intent, product truth, low competitor-sayable language, and fit with analytics data.\n\nAppend one row to `results.tsv` after each round. Keep rationale short and TSV-safe.\n\nWinner becomes candidate A for the next round.\n\n## Final Selection\n\nAfter the final round, choose the two strongest distinct candidates. They should not be tiny wording variations of each other.\n\nWrite `final_variants.md` with:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n\nEnd with a clear note that the experiment has not been wired yet.\n\n## Approved Outer Experiment Loop\n\nRun this section only if the user explicitly asks you to implement or wire the approved experiment.\n\n1. Implement the approved variant or variants in the product surface named in `brief.md`.\n2. Create the experiment with the recommended control and candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect behavior for the requested window.\n5. Pull the experiment results into a new dated data snapshot, including:\n - winning and losing variants\n - primary metric movement\n - proxy metric movement\n - guardrail movement\n - screenshots or changed-copy notes\n - data limitations\n6. Start the next autoresearch loop from that measured evidence.\n\nThe LLM loop generates pressure. The outer experiment loop decides what survived contact with users.\n\nFile v1.0.10:references/results-header.txt\n\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n\nFile v1.0.10:skill-card.md\n\n## Description:\n\nRuns a data-informed growth loop to propose and rank two review-ready A/B test variants for landing pages, onboarding, or pricing.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dannyshmueli](https://clawhub.ai/user/dannyshmueli)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nGrowth teams, marketers, and developers use analytics and product briefs to draft, critique, and rank copy variants for a human-reviewed A/B test.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Analytics snapshots saved locally may contain sensitive project data.\n\nMitigation: Review the chosen analytics source and restrict access to locally saved snapshots.\n\nRisk: Applying draft copy or changing experiment setup before review may affect the live product.\n\nMitigation: Keep the default review-only flow; obtain explicit approval before product edits, experiment setup, or project-context updates.\n\n## Reference(s):\n\n- [ClawHub skill release](https://clawhub.ai/dannyshmueli/skills/agent-analytics-autoresearch)\n- [Agent Analytics](https://agentanalytics.sh)\n- [Autoresearch growth loop](references/program.md)\n- [Growth loop brief template](references/brief-template.md)\n- [Final variants template](references/final-variants-template.md)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown]\n\n**Output Format:** [Markdown copy variants and rationale, with a tab-separated round log and optional local analytics snapshots]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Two distinct review-ready variants; no live experiment is created by default.]\n\n## Skill Version(s):\n\n1.0.10 (source: ClawHub release metadata and skill frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.9: 9 files, 10489 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), references/results-header.txt (92b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), skill-card.md (2793b), SKILL.md (7816b), _meta.json (147b)\n\nFile v1.0.9:SKILL.md\n\n---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.9\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/Agent-Analytics/skills\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapshot, fold `project_context` into the product truth and metric definitions, and keep activation/event meaning separate per project or domain. After a human correction, scanner result, completed experiment, or repeated measured finding, update context only with durable product truth. Save activation definitions, event meanings, stable goals, and confirmed interpretations; skip weekly numbers, temporary spikes, pasted reports, PII, and unconfirmed guesses.\n\n## Quick Start\n\nIf the user already has a repo or run folder, work there. Otherwise initialize a run:\n\n```bash\nbash /scripts/init_autoresearch_run.sh homepage-signup\n```\n\nThen fill `brief.md`, collect or paste data, and run the loop:\n\n```text\nRead brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review.\n```\n\nWhen using Agent Analytics, collect a snapshot:\n\n```bash\nbash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click\n```\n\nIf `` is not obvious in the runtime, read the script from this skill's `scripts/` folder and run an equivalent local command.\n\n## References\n\nLoad these files only when needed:\n\n- `references/program.md` - exact loop instructions.\n- `references/brief-template.md` - project brief template.\n- `references/final-variants-template.md` - final output template.\n- `references/results-header.txt` - exact `results.tsv` header.\n\n## Loop Shape\n\n### Inner Autoresearch Loop\n\n1. Define the surface, control, audience, product truth, metric, proxy, and guardrails.\n2. Collect or read a dated analytics snapshot.\n3. Summarize useful signals and data limitations.\n4. Generate candidate A.\n5. Critique A harshly for genericness, drift, unsupported claims, weak conversion intent, and competitor-sayable language.\n6. Write candidate B from the critique.\n7. Synthesize AB from the strongest parts of A and B.\n8. Blind-rank A, B, and AB with Borda scoring.\n9. Append one TSV-safe row to `results.tsv`.\n10. Repeat several rounds.\n11. Write `final_variants.md` with two distinct variants and the recommended experiment shape.\n\n### Outer Experiment Loop\n\nOnly run this phase when the user explicitly approves implementation or experiment setup.\n\n1. Implement the approved variant or variants in the target product surface.\n2. Create the experiment with a control and the approved candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect real behavior for the requested window.\n5. Pull experiment results, screenshots or changed-copy notes, funnel movement, guardrails, and data limitations into a new snapshot.\n6. Start the next inner autoresearch loop from that measured evidence.\n\nThe outer loop prevents the LLM panel from becoming the final judge. LLMs generate and criticize, humans approve risk, and users decide what worked.\n\n## Agent Analytics Snapshot\n\nUse the official CLI when collecting live Agent Analytics data:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.31 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.31 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.31 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.31 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.31 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.31 experiments list \"$PROJECT_SLUG\"\n```\n\nIf login is needed, prefer the regular `agent-analytics` skill's browser approval or detached login guidance.\n\nBefore interpreting the snapshot, also read the compact project memory:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.31 context get \"$PROJECT_SLUG\"\n```\n\nIf the autoresearch run reveals durable product truth that should guide future analytics, use the regular `agent-analytics` skill's project context workflow to read the existing context, merge the compact update, and write it back. Do not store raw round notes or time-bound metric values as project context.\n\n## Scoring\n\nUse Borda scoring:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nJudge by:\n\n- specificity to the product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with analytics data\n- respect for guardrails\n\n## Output\n\n`final_variants.md` must include:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n- clear note that the experiment has not been wired yet\n\nOnly create or wire an experiment after explicit human approval.\n\nFile v1.0.9:_meta.json\n\n{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.9\",\n \"publishedAt\": 1778356652985\n}\n\nFile v1.0.9:references/brief-template.md\n\n# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.31 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.31 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.31 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.31 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.31 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.31 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.31 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project list\n```\n\n## Live Data Snapshot\n\nSummary:\n\n-\n\nPrimary event:\n\n-\n\nProxy event:\n\n-\n\nGuardrails:\n\n-\n\nData limitations:\n\n-\n\n## Drift Constraints\n\nCandidates must not:\n\n-\n\nCandidates should:\n\n-\n\n## Judging Rubric\n\nRank candidates by:\n\n- specificity to this product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with available analytics data\n\nFile v1.0.9:references/final-variants-template.md\n\n# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step\n\nFile v1.0.9:references/program.md\n\n# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project described in `brief.md`.\n\nDo not change product code while running the loop. Produce reviewable copy artifacts first.\n\nDefault mode is review-only. Only move into implementation, experiment creation, or measurement after the user explicitly approves that next phase.\n\n## Setup\n\n1. Read `brief.md` fully.\n2. Treat `brief.md` as the source of truth for the project, audience, surface, control, metrics, analytics data, and drift constraints.\n3. If `results.tsv` does not exist, create it with:\n\n```tsv\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n```\n\n4. If `final_variants.md` exists, overwrite it only at the end of a completed loop.\n\n## Data Brief\n\nBefore generating copy, collect or read the data described in `brief.md`.\n\nThe data source can be Agent Analytics, another analytics CLI, an API, SQL, CSV, exported reports, product logs, or manually supplied data.\n\nRules:\n\n- Never invent numbers.\n- If a command fails, record the failure.\n- Treat missing data as missing data, not zero data.\n- Separate primary outcome data from proxy and guardrail data.\n- If data is sparse, say so and treat it as weak signal.\n\n## Scope\n\nDuring the loop, edit only:\n\n- `results.tsv`\n- `final_variants.md`\n- optional scratch notes\n\nDo not edit the live site, app, product code, or experiment setup until the variants have been reviewed.\n\n## Product Truth\n\nEvery candidate must preserve the product truth from `brief.md`.\n\nPenalize:\n\n- generic category language\n- copy a competitor could say word for word\n- unsupported claims\n- drift away from the real product value\n- clickbait that weakens primary conversion intent\n- changes that ignore the current control's strengths\n\nReward:\n\n- specificity\n- clear audience fit\n- concrete user outcome\n- stronger primary-event intent\n- honest use of the available data\n- language only this product could credibly say\n\n## Loop\n\nRun at least 5 rounds unless `brief.md` specifies a different count.\n\nEach round has four phases.\n\n### 1. Candidate A\n\nFor round 1, candidate A is your first new hypothesis based on the brief, control, and data.\n\nFor later rounds, candidate A is the previous round winner.\n\nInclude the editable parts named in the brief, usually headline, subheadline, CTA, supporting copy, and hypothesis.\n\n### 2. Critique\n\nCritique candidate A harshly:\n\n- what is generic\n- what a competitor could say\n- where value is unclear\n- where copy drifts from product truth\n- whether primary-event intent is strong enough\n- whether the control is clearer\n\n### 3. Candidate B And Synthesis AB\n\nWrite candidate B from the critique as if starting fresh. It may use the brief and critique, but it should not be a mild rewrite of A.\n\nThen write candidate AB by combining the strongest parts of A and B. Do not average them into bland middle copy.\n\n### 4. Blind Borda Ranking\n\nBlind-rank A, B, and AB. To simulate blind judging, anonymize them as `option_1`, `option_2`, and `option_3` in a different order each round before scoring.\n\nScore:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nUse the rubric in `brief.md`. If none exists, rank by specificity, clarity, primary-event intent, product truth, low competitor-sayable language, and fit with analytics data.\n\nAppend one row to `results.tsv` after each round. Keep rationale short and TSV-safe.\n\nWinner becomes candidate A for the next round.\n\n## Final Selection\n\nAfter the final round, choose the two strongest distinct candidates. They should not be tiny wording variations of each other.\n\nWrite `final_variants.md` with:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n\nEnd with a clear note that the experiment has not been wired yet.\n\n## Approved Outer Experiment Loop\n\nRun this section only if the user explicitly asks you to implement or wire the approved experiment.\n\n1. Implement the approved variant or variants in the product surface named in `brief.md`.\n2. Create the experiment with the recommended control and candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect behavior for the requested window.\n5. Pull the experiment results into a new dated data snapshot, including:\n - winning and losing variants\n - primary metric movement\n - proxy metric movement\n - guardrail movement\n - screenshots or changed-copy notes\n - data limitations\n6. Start the next autoresearch loop from that measured evidence.\n\nThe LLM loop generates pressure. The outer experiment loop decides what survived contact with users.\n\nFile v1.0.9:references/results-header.txt\n\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n\nFile v1.0.9:skill-card.md\n\n## Description:\n\nRun an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dannyshmueli](https://clawhub.ai/user/dannyshmueli)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and growth teams use this skill to run a review-first autoresearch loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, and other experiment candidates. It helps collect or read analytics evidence, generate and critique variants, blind-rank candidates, and produce review-ready A/B test artifacts before any implementation.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The analytics snapshot workflow executes an npm CLI at runtime.\n\nMitigation: Run it in a restricted environment with narrowly scoped credentials and review the command before execution.\n\nRisk: Untrusted project slugs or event names can contribute to unsafe file-writing behavior in the snapshot script.\n\nMitigation: Use only trusted slugs and event names, validate event names, or use fixed output filenames before broad use.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/dannyshmueli/skills/agent-analytics-autoresearch)\n- [Agent Analytics](https://agentanalytics.sh)\n- [Agent Analytics Skills Repository](https://github.com/Agent-Analytics/skills)\n- [Autoresearch Growth Template](https://github.com/Agent-Analytics/autoresearch-growth)\n- [Regular Agent Analytics Skill](https://github.com/Agent-Analytics/skills/tree/main/skills/agent-analytics)\n- [Autoresearch Growth Loop](references/program.md)\n- [Growth Loop Brief Template](references/brief-template.md)\n- [Final Variants Template](references/final-variants-template.md)\n- [Results Header](references/results-header.txt)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown, TSV, and shell commands]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Produces reviewable experiment artifacts such as results.tsv and final_variants.md; production changes require explicit human approval.]\n\n## Skill Version(s):\n\n1.0.9 (source: server release metadata and skill frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.8: 8 files, 9103 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), references/results-header.txt (92b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), SKILL.md (7816b), _meta.json (147b)\n\nFile v1.0.8:SKILL.md\n\n---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.8\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/agent-analytics/skills\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapshot, fold `project_context` into the product truth and metric definitions, and keep activation/event meaning separate per project or domain. After a human correction, scanner result, completed experiment, or repeated measured finding, update context only with durable product truth. Save activation definitions, event meanings, stable goals, and confirmed interpretations; skip weekly numbers, temporary spikes, pasted reports, PII, and unconfirmed guesses.\n\n## Quick Start\n\nIf the user already has a repo or run folder, work there. Otherwise initialize a run:\n\n```bash\nbash /scripts/init_autoresearch_run.sh homepage-signup\n```\n\nThen fill `brief.md`, collect or paste data, and run the loop:\n\n```text\nRead brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review.\n```\n\nWhen using Agent Analytics, collect a snapshot:\n\n```bash\nbash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click\n```\n\nIf `` is not obvious in the runtime, read the script from this skill's `scripts/` folder and run an equivalent local command.\n\n## References\n\nLoad these files only when needed:\n\n- `references/program.md` - exact loop instructions.\n- `references/brief-template.md` - project brief template.\n- `references/final-variants-template.md` - final output template.\n- `references/results-header.txt` - exact `results.tsv` header.\n\n## Loop Shape\n\n### Inner Autoresearch Loop\n\n1. Define the surface, control, audience, product truth, metric, proxy, and guardrails.\n2. Collect or read a dated analytics snapshot.\n3. Summarize useful signals and data limitations.\n4. Generate candidate A.\n5. Critique A harshly for genericness, drift, unsupported claims, weak conversion intent, and competitor-sayable language.\n6. Write candidate B from the critique.\n7. Synthesize AB from the strongest parts of A and B.\n8. Blind-rank A, B, and AB with Borda scoring.\n9. Append one TSV-safe row to `results.tsv`.\n10. Repeat several rounds.\n11. Write `final_variants.md` with two distinct variants and the recommended experiment shape.\n\n### Outer Experiment Loop\n\nOnly run this phase when the user explicitly approves implementation or experiment setup.\n\n1. Implement the approved variant or variants in the target product surface.\n2. Create the experiment with a control and the approved candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect real behavior for the requested window.\n5. Pull experiment results, screenshots or changed-copy notes, funnel movement, guardrails, and data limitations into a new snapshot.\n6. Start the next inner autoresearch loop from that measured evidence.\n\nThe outer loop prevents the LLM panel from becoming the final judge. LLMs generate and criticize, humans approve risk, and users decide what worked.\n\n## Agent Analytics Snapshot\n\nUse the official CLI when collecting live Agent Analytics data:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.31 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.31 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.31 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.31 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.31 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.31 experiments list \"$PROJECT_SLUG\"\n```\n\nIf login is needed, prefer the regular `agent-analytics` skill's browser approval or detached login guidance.\n\nBefore interpreting the snapshot, also read the compact project memory:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.31 context get \"$PROJECT_SLUG\"\n```\n\nIf the autoresearch run reveals durable product truth that should guide future analytics, use the regular `agent-analytics` skill's project context workflow to read the existing context, merge the compact update, and write it back. Do not store raw round notes or time-bound metric values as project context.\n\n## Scoring\n\nUse Borda scoring:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nJudge by:\n\n- specificity to the product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with analytics data\n- respect for guardrails\n\n## Output\n\n`final_variants.md` must include:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n- clear note that the experiment has not been wired yet\n\nOnly create or wire an experiment after explicit human approval.\n\nFile v1.0.8:_meta.json\n\n{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.8\",\n \"publishedAt\": 1778356506884\n}\n\nFile v1.0.8:references/brief-template.md\n\n# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.31 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.31 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.31 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.31 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.31 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.31 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.31 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project list\n```\n\n## Live Data Snapshot\n\nSummary:\n\n-\n\nPrimary event:\n\n-\n\nProxy event:\n\n-\n\nGuardrails:\n\n-\n\nData limitations:\n\n-\n\n## Drift Constraints\n\nCandidates must not:\n\n-\n\nCandidates should:\n\n-\n\n## Judging Rubric\n\nRank candidates by:\n\n- specificity to this product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with available analytics data\n\nFile v1.0.8:references/final-variants-template.md\n\n# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step\n\nFile v1.0.8:references/program.md\n\n# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project described in `brief.md`.\n\nDo not change product code while running the loop. Produce reviewable copy artifacts first.\n\nDefault mode is review-only. Only move into implementation, experiment creation, or measurement after the user explicitly approves that next phase.\n\n## Setup\n\n1. Read `brief.md` fully.\n2. Treat `brief.md` as the source of truth for the project, audience, surface, control, metrics, analytics data, and drift constraints.\n3. If `results.tsv` does not exist, create it with:\n\n```tsv\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n```\n\n4. If `final_variants.md` exists, overwrite it only at the end of a completed loop.\n\n## Data Brief\n\nBefore generating copy, collect or read the data described in `brief.md`.\n\nThe data source can be Agent Analytics, another analytics CLI, an API, SQL, CSV, exported reports, product logs, or manually supplied data.\n\nRules:\n\n- Never invent numbers.\n- If a command fails, record the failure.\n- Treat missing data as missing data, not zero data.\n- Separate primary outcome data from proxy and guardrail data.\n- If data is sparse, say so and treat it as weak signal.\n\n## Scope\n\nDuring the loop, edit only:\n\n- `results.tsv`\n- `final_variants.md`\n- optional scratch notes\n\nDo not edit the live site, app, product code, or experiment setup until the variants have been reviewed.\n\n## Product Truth\n\nEvery candidate must preserve the product truth from `brief.md`.\n\nPenalize:\n\n- generic category language\n- copy a competitor could say word for word\n- unsupported claims\n- drift away from the real product value\n- clickbait that weakens primary conversion intent\n- changes that ignore the current control's strengths\n\nReward:\n\n- specificity\n- clear audience fit\n- concrete user outcome\n- stronger primary-event intent\n- honest use of the available data\n- language only this product could credibly say\n\n## Loop\n\nRun at least 5 rounds unless `brief.md` specifies a different count.\n\nEach round has four phases.\n\n### 1. Candidate A\n\nFor round 1, candidate A is your first new hypothesis based on the brief, control, and data.\n\nFor later rounds, candidate A is the previous round winner.\n\nInclude the editable parts named in the brief, usually headline, subheadline, CTA, supporting copy, and hypothesis.\n\n### 2. Critique\n\nCritique candidate A harshly:\n\n- what is generic\n- what a competitor could say\n- where value is unclear\n- where copy drifts from product truth\n- whether primary-event intent is strong enough\n- whether the control is clearer\n\n### 3. Candidate B And Synthesis AB\n\nWrite candidate B from the critique as if starting fresh. It may use the brief and critique, but it should not be a mild rewrite of A.\n\nThen write candidate AB by combining the strongest parts of A and B. Do not average them into bland middle copy.\n\n### 4. Blind Borda Ranking\n\nBlind-rank A, B, and AB. To simulate blind judging, anonymize them as `option_1`, `option_2`, and `option_3` in a different order each round before scoring.\n\nScore:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nUse the rubric in `brief.md`. If none exists, rank by specificity, clarity, primary-event intent, product truth, low competitor-sayable language, and fit with analytics data.\n\nAppend one row to `results.tsv` after each round. Keep rationale short and TSV-safe.\n\nWinner becomes candidate A for the next round.\n\n## Final Selection\n\nAfter the final round, choose the two strongest distinct candidates. They should not be tiny wording variations of each other.\n\nWrite `final_variants.md` with:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n\nEnd with a clear note that the experiment has not been wired yet.\n\n## Approved Outer Experiment Loop\n\nRun this section only if the user explicitly asks you to implement or wire the approved experiment.\n\n1. Implement the approved variant or variants in the product surface named in `brief.md`.\n2. Create the experiment with the recommended control and candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect behavior for the requested window.\n5. Pull the experiment results into a new dated data snapshot, including:\n - winning and losing variants\n - primary metric movement\n - proxy metric movement\n - guardrail movement\n - screenshots or changed-copy notes\n - data limitations\n6. Start the next autoresearch loop from that measured evidence.\n\nThe LLM loop generates pressure. The outer experiment loop decides what survived contact with users.\n\nFile v1.0.8:references/results-header.txt\n\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n\nArchive v1.0.7: 8 files, 9101 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), references/results-header.txt (92b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), SKILL.md (7846b), _meta.json (147b)\n\nFile v1.0.7:SKILL.md\n\n---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.7\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/Agent-Analytics/agent-analytics-skill\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapshot, fold `project_context` into the product truth and metric definitions, and keep activation/event meaning separate per project or domain. After a human correction, scanner result, completed experiment, or repeated measured finding, update context only with durable product truth. Save activation definitions, event meanings, stable goals, and confirmed interpretations; skip weekly numbers, temporary spikes, pasted reports, PII, and unconfirmed guesses.\n\n## Quick Start\n\nIf the user already has a repo or run folder, work there. Otherwise initialize a run:\n\n```bash\nbash /scripts/init_autoresearch_run.sh homepage-signup\n```\n\nThen fill `brief.md`, collect or paste data, and run the loop:\n\n```text\nRead brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review.\n```\n\nWhen using Agent Analytics, collect a snapshot:\n\n```bash\nbash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click\n```\n\nIf `` is not obvious in the runtime, read the script from this skill's `scripts/` folder and run an equivalent local command.\n\n## References\n\nLoad these files only when needed:\n\n- `references/program.md` - exact loop instructions.\n- `references/brief-template.md` - project brief template.\n- `references/final-variants-template.md` - final output template.\n- `references/results-header.txt` - exact `results.tsv` header.\n\n## Loop Shape\n\n### Inner Autoresearch Loop\n\n1. Define the surface, control, audience, product truth, metric, proxy, and guardrails.\n2. Collect or read a dated analytics snapshot.\n3. Summarize useful signals and data limitations.\n4. Generate candidate A.\n5. Critique A harshly for genericness, drift, unsupported claims, weak conversion intent, and competitor-sayable language.\n6. Write candidate B from the critique.\n7. Synthesize AB from the strongest parts of A and B.\n8. Blind-rank A, B, and AB with Borda scoring.\n9. Append one TSV-safe row to `results.tsv`.\n10. Repeat several rounds.\n11. Write `final_variants.md` with two distinct variants and the recommended experiment shape.\n\n### Outer Experiment Loop\n\nOnly run this phase when the user explicitly approves implementation or experiment setup.\n\n1. Implement the approved variant or variants in the target product surface.\n2. Create the experiment with a control and the approved candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect real behavior for the requested window.\n5. Pull experiment results, screenshots or changed-copy notes, funnel movement, guardrails, and data limitations into a new snapshot.\n6. Start the next inner autoresearch loop from that measured evidence.\n\nThe outer loop prevents the LLM panel from becoming the final judge. LLMs generate and criticize, humans approve risk, and users decide what worked.\n\n## Agent Analytics Snapshot\n\nUse the official CLI when collecting live Agent Analytics data:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.28 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.28 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.28 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.28 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.28 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.28 experiments list \"$PROJECT_SLUG\"\n```\n\nIf login is needed, prefer the regular `agent-analytics` skill's browser approval or detached login guidance.\n\nBefore interpreting the snapshot, also read the compact project memory:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.28 context get \"$PROJECT_SLUG\"\n```\n\nIf the autoresearch run reveals durable product truth that should guide future analytics, use the regular `agent-analytics` skill's project context workflow to read the existing context, merge the compact update, and write it back. Do not store raw round notes or time-bound metric values as project context.\n\n## Scoring\n\nUse Borda scoring:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nJudge by:\n\n- specificity to the product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with analytics data\n- respect for guardrails\n\n## Output\n\n`final_variants.md` must include:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n- clear note that the experiment has not been wired yet\n\nOnly create or wire an experiment after explicit human approval.\n\nFile v1.0.7:_meta.json\n\n{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.7\",\n \"publishedAt\": 1777654021606\n}\n\nFile v1.0.7:references/brief-template.md\n\n# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.28 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.28 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.28 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.28 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.28 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.28 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.28 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project list\n```\n\n## Live Data Snapshot\n\nSummary:\n\n-\n\nPrimary event:\n\n-\n\nProxy event:\n\n-\n\nGuardrails:\n\n-\n\nData limitations:\n\n-\n\n## Drift Constraints\n\nCandidates must not:\n\n-\n\nCandidates should:\n\n-\n\n## Judging Rubric\n\nRank candidates by:\n\n- specificity to this product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with available analytics data\n\nFile v1.0.7:references/final-variants-template.md\n\n# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step\n\nFile v1.0.7:references/program.md\n\n# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project described in `brief.md`.\n\nDo not change product code while running the loop. Produce reviewable copy artifacts first.\n\nDefault mode is review-only. Only move into implementation, experiment creation, or measurement after the user explicitly approves that next phase.\n\n## Setup\n\n1. Read `brief.md` fully.\n2. Treat `brief.md` as the source of truth for the project, audience, surface, control, metrics, analytics data, and drift constraints.\n3. If `results.tsv` does not exist, create it with:\n\n```tsv\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n```\n\n4. If `final_variants.md` exists, overwrite it only at the end of a completed loop.\n\n## Data Brief\n\nBefore generating copy, collect or read the data described in `brief.md`.\n\nThe data source can be Agent Analytics, another analytics CLI, an API, SQL, CSV, exported reports, product logs, or manually supplied data.\n\nRules:\n\n- Never invent numbers.\n- If a command fails, record the failure.\n- Treat missing data as missing data, not zero data.\n- Separate primary outcome data from proxy and guardrail data.\n- If data is sparse, say so and treat it as weak signal.\n\n## Scope\n\nDuring the loop, edit only:\n\n- `results.tsv`\n- `final_variants.md`\n- optional scratch notes\n\nDo not edit the live site, app, product code, or experiment setup until the variants have been reviewed.\n\n## Product Truth\n\nEvery candidate must preserve the product truth from `brief.md`.\n\nPenalize:\n\n- generic category language\n- copy a competitor could say word for word\n- unsupported claims\n- drift away from the real product value\n- clickbait that weakens primary conversion intent\n- changes that ignore the current control's strengths\n\nReward:\n\n- specificity\n- clear audience fit\n- concrete user outcome\n- stronger primary-event intent\n- honest use of the available data\n- language only this product could credibly say\n\n## Loop\n\nRun at least 5 rounds unless `brief.md` specifies a different count.\n\nEach round has four phases.\n\n### 1. Candidate A\n\nFor round 1, candidate A is your first new hypothesis based on the brief, control, and data.\n\nFor later rounds, candidate A is the previous round winner.\n\nInclude the editable parts named in the brief, usually headline, subheadline, CTA, supporting copy, and hypothesis.\n\n### 2. Critique\n\nCritique candidate A harshly:\n\n- what is generic\n- what a competitor could say\n- where value is unclear\n- where copy drifts from product truth\n- whether primary-event intent is strong enough\n- whether the control is clearer\n\n### 3. Candidate B And Synthesis AB\n\nWrite candidate B from the critique as if starting fresh. It may use the brief and critique, but it should not be a mild rewrite of A.\n\nThen write candidate AB by combining the strongest parts of A and B. Do not average them into bland middle copy.\n\n### 4. Blind Borda Ranking\n\nBlind-rank A, B, and AB. To simulate blind judging, anonymize them as `option_1`, `option_2`, and `option_3` in a different order each round before scoring.\n\nScore:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nUse the rubric in `brief.md`. If none exists, rank by specificity, clarity, primary-event intent, product truth, low competitor-sayable language, and fit with analytics data.\n\nAppend one row to `results.tsv` after each round. Keep rationale short and TSV-safe.\n\nWinner becomes candidate A for the next round.\n\n## Final Selection\n\nAfter the final round, choose the two strongest distinct candidates. They should not be tiny wording variations of each other.\n\nWrite `final_variants.md` with:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n\nEnd with a clear note that the experiment has not been wired yet.\n\n## Approved Outer Experiment Loop\n\nRun this section only if the user explicitly asks you to implement or wire the approved experiment.\n\n1. Implement the approved variant or variants in the product surface named in `brief.md`.\n2. Create the experiment with the recommended control and candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect behavior for the requested window.\n5. Pull the experiment results into a new dated data snapshot, including:\n - winning and losing variants\n - primary metric movement\n - proxy metric movement\n - guardrail movement\n - screenshots or changed-copy notes\n - data limitations\n6. Start the next autoresearch loop from that measured evidence.\n\nThe LLM loop generates pressure. The outer experiment loop decides what survived contact with users.\n\nFile v1.0.7:references/results-header.txt\n\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n\nArchive v1.0.6: 8 files, 9101 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), references/results-header.txt (92b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), SKILL.md (7846b), _meta.json (147b)\n\nFile v1.0.6:SKILL.md\n\n---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.6\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/Agent-Analytics/agent-analytics-skill\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapshot, fold `project_context` into the product truth and metric definitions, and keep activation/event meaning separate per project or domain. After a human correction, scanner result, completed experiment, or repeated measured finding, update context only with durable product truth. Save activation definitions, event meanings, stable goals, and confirmed interpretations; skip weekly numbers, temporary spikes, pasted reports, PII, and unconfirmed guesses.\n\n## Quick Start\n\nIf the user already has a repo or run folder, work there. Otherwise initialize a run:\n\n```bash\nbash /scripts/init_autoresearch_run.sh homepage-signup\n```\n\nThen fill `brief.md`, collect or paste data, and run the loop:\n\n```text\nRead brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review.\n```\n\nWhen using Agent Analytics, collect a snapshot:\n\n```bash\nbash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click\n```\n\nIf `` is not obvious in the runtime, read the script from this skill's `scripts/` folder and run an equivalent local command.\n\n## References\n\nLoad these files only when needed:\n\n- `references/program.md` - exact loop instructions.\n- `references/brief-template.md` - project brief template.\n- `references/final-variants-template.md` - final output template.\n- `references/results-header.txt` - exact `results.tsv` header.\n\n## Loop Shape\n\n### Inner Autoresearch Loop\n\n1. Define the surface, control, audience, product truth, metric, proxy, and guardrails.\n2. Collect or read a dated analytics snapshot.\n3. Summarize useful signals and data limitations.\n4. Generate candidate A.\n5. Critique A harshly for genericness, drift, unsupported claims, weak conversion intent, and competitor-sayable language.\n6. Write candidate B from the critique.\n7. Synthesize AB from the strongest parts of A and B.\n8. Blind-rank A, B, and AB with Borda scoring.\n9. Append one TSV-safe row to `results.tsv`.\n10. Repeat several rounds.\n11. Write `final_variants.md` with two distinct variants and the recommended experiment shape.\n\n### Outer Experiment Loop\n\nOnly run this phase when the user explicitly approves implementation or experiment setup.\n\n1. Implement the approved variant or variants in the target product surface.\n2. Create the experiment with a control and the approved candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect real behavior for the requested window.\n5. Pull experiment results, screenshots or changed-copy notes, funnel movement, guardrails, and data limitations into a new snapshot.\n6. Start the next inner autoresearch loop from that measured evidence.\n\nThe outer loop prevents the LLM panel from becoming the final judge. LLMs generate and criticize, humans approve risk, and users decide what worked.\n\n## Agent Analytics Snapshot\n\nUse the official CLI when collecting live Agent Analytics data:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.20 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.20 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.20 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.20 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.20 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.20 experiments list \"$PROJECT_SLUG\"\n```\n\nIf login is needed, prefer the regular `agent-analytics` skill's browser approval or detached login guidance.\n\nBefore interpreting the snapshot, also read the compact project memory:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.20 context get \"$PROJECT_SLUG\"\n```\n\nIf the autoresearch run reveals durable product truth that should guide future analytics, use the regular `agent-analytics` skill's project context workflow to read the existing context, merge the compact update, and write it back. Do not store raw round notes or time-bound metric values as project context.\n\n## Scoring\n\nUse Borda scoring:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nJudge by:\n\n- specificity to the product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with analytics data\n- respect for guardrails\n\n## Output\n\n`final_variants.md` must include:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n- clear note that the experiment has not been wired yet\n\nOnly create or wire an experiment after explicit human approval.\n\nFile v1.0.6:_meta.json\n\n{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.6\",\n \"publishedAt\": 1776631905675\n}\n\nFile v1.0.6:references/brief-template.md\n\n# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.20 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.20 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.20 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.20 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.20 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.20 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.20 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project list\n```\n\n## Live Data Snapshot\n\nSummary:\n\n-\n\nPrimary event:\n\n-\n\nProxy event:\n\n-\n\nGuardrails:\n\n-\n\nData limitations:\n\n-\n\n## Drift Constraints\n\nCandidates must not:\n\n-\n\nCandidates should:\n\n-\n\n## Judging Rubric\n\nRank candidates by:\n\n- specificity to this product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with available analytics data\n\nFile v1.0.6:references/final-variants-template.md\n\n# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step\n\nFile v1.0.6:references/program.md\n\n# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project described in `brief.md`.\n\nDo not change product code while running the loop. Produce reviewable copy artifacts first.\n\nDefault mode is review-only. Only move into implementation, experiment creation, or measurement after the user explicitly approves that next phase.\n\n## Setup\n\n1. Read `brief.md` fully.\n2. Treat `brief.md` as the source of truth for the project, audience, surface, control, metrics, analytics data, and drift constraints.\n3. If `results.tsv` does not exist, create it with:\n\n```tsv\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n```\n\n4. If `final_variants.md` exists, overwrite it only at the end of a completed loop.\n\n## Data Brief\n\nBefore generating copy, collect or read the data described in `brief.md`.\n\nThe data source can be Agent Analytics, another analytics CLI, an API, SQL, CSV, exported reports, product logs, or manually supplied data.\n\nRules:\n\n- Never invent numbers.\n- If a command fails, record the failure.\n- Treat missing data as missing data, not zero data.\n- Separate primary outcome data from proxy and guardrail data.\n- If data is sparse, say so and treat it as weak signal.\n\n## Scope\n\nDuring the loop, edit only:\n\n- `results.tsv`\n- `final_variants.md`\n- optional scratch notes\n\nDo not edit the live site, app, product code, or experiment setup until the variants have been reviewed.\n\n## Product Truth\n\nEvery candidate must preserve the product truth from `brief.md`.\n\nPenalize:\n\n- generic category language\n- copy a competitor could say word for word\n- unsupported claims\n- drift away from the real product value\n- clickbait that weakens primary conversion intent\n- changes that ignore the current control's strengths\n\nReward:\n\n- specificity\n- clear audience fit\n- concrete user outcome\n- stronger primary-event intent\n- honest use of the available data\n- language only this product could credibly say\n\n## Loop\n\nRun at least 5 rounds unless `brief.md` specifies a different count.\n\nEach round has four phases.\n\n### 1. Candidate A\n\nFor round 1, candidate A is your first new hypothesis based on the brief, control, and data.\n\nFor later rounds, candidate A is the previous round winner.\n\nInclude the editable parts named in the brief, usually headline, subheadline, CTA, supporting copy, and hypothesis.\n\n### 2. Critique\n\nCritique candidate A harshly:\n\n- what is generic\n- what a competitor could say\n- where value is unclear\n- where copy drifts from product truth\n- whether primary-event intent is strong enough\n- whether the control is clearer\n\n### 3. Candidate B And Synthesis AB\n\nWrite candidate B from the critique as if starting fresh. It may use the brief and critique, but it should not be a mild rewrite of A.\n\nThen write candidate AB by combining the strongest parts of A and B. Do not average them into bland middle copy.\n\n### 4. Blind Borda Ranking\n\nBlind-rank A, B, and AB. To simulate blind judging, anonymize them as `option_1`, `option_2`, and `option_3` in a different order each round before scoring.\n\nScore:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nUse the rubric in `brief.md`. If none exists, rank by specificity, clarity, primary-event intent, product truth, low competitor-sayable language, and fit with analytics data.\n\nAppend one row to `results.tsv` after each round. Keep rationale short and TSV-safe.\n\nWinner becomes candidate A for the next round.\n\n## Final Selection\n\nAfter the final round, choose the two strongest distinct candidates. They should not be tiny wording variations of each other.\n\nWrite `final_variants.md` with:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n\nEnd with a clear note that the experiment has not been wired yet.\n\n## Approved Outer Experiment Loop\n\nRun this section only if the user explicitly asks you to implement or wire the approved experiment.\n\n1. Implement the approved variant or variants in the product surface named in `brief.md`.\n2. Create the experiment with the recommended control and candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect behavior for the requested window.\n5. Pull the experiment results into a new dated data snapshot, including:\n - winning and losing variants\n - primary metric movement\n - proxy metric movement\n - guardrail movement\n - screenshots or changed-copy notes\n - data limitations\n6. Start the next autoresearch loop from that measured evidence.\n\nThe LLM loop generates pressure. The outer experiment loop decides what survived contact with users.\n\nFile v1.0.6:references/results-header.txt\n\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n\nArchive v1.0.5: 7 files, 8886 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), SKILL.md (7846b), _meta.json (147b)\n\nFile v1.0.5:SKILL.md\n\n---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.5\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/Agent-Analytics/agent-analytics-skill\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapshot, fold `project_context` into the product truth and metric definitions, and keep activation/event meaning separate per project or domain. After a human correction, scanner result, completed experiment, or repeated measured finding, update context only with durable product truth. Save activation definitions, event meanings, stable goals, and confirmed interpretations; skip weekly numbers, temporary spikes, pasted reports, PII, and unconfirmed guesses.\n\n## Quick Start\n\nIf the user already has a repo or run folder, work there. Otherwise initialize a run:\n\n```bash\nbash /scripts/init_autoresearch_run.sh homepage-signup\n```\n\nThen fill `brief.md`, collect or paste data, and run the loop:\n\n```text\nRead brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review.\n```\n\nWhen using Agent Analytics, collect a snapshot:\n\n```bash\nbash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click\n```\n\nIf `` is not obvious in the runtime, read the script from this skill's `scripts/` folder and run an equivalent local command.\n\n## References\n\nLoad these files only when needed:\n\n- `references/program.md` - exact loop instructions.\n- `references/brief-template.md` - project brief template.\n- `references/final-variants-template.md` - final output template.\n- `references/results-header.tsv` - exact `results.tsv` header.\n\n## Loop Shape\n\n### Inner Autoresearch Loop\n\n1. Define the surface, control, audience, product truth, metric, proxy, and guardrails.\n2. Collect or read a dated analytics snapshot.\n3. Summarize useful signals and data limitations.\n4. Generate candidate A.\n5. Critique A harshly for genericness, drift, unsupported claims, weak conversion intent, and competitor-sayable language.\n6. Write candidate B from the critique.\n7. Synthesize AB from the strongest parts of A and B.\n8. Blind-rank A, B, and AB with Borda scoring.\n9. Append one TSV-safe row to `results.tsv`.\n10. Repeat several rounds.\n11. Write `final_variants.md` with two distinct variants and the recommended experiment shape.\n\n### Outer Experiment Loop\n\nOnly run this phase when the user explicitly approves implementation or experiment setup.\n\n1. Implement the approved variant or variants in the target product surface.\n2. Create the experiment with a control and the approved candidate variants.\n3. Verify tracking for the primary metric, proxy metric, and guardrails.\n4. Let the experiment collect real behavior for the requested window.\n5. Pull experiment results, screenshots or changed-copy notes, funnel movement, guardrails, and data limitations into a new snapshot.\n6. Start the next inner autoresearch loop from that measured evidence.\n\nThe outer loop prevents the LLM panel from becoming the final judge. LLMs generate and criticize, humans approve risk, and users decide what worked.\n\n## Agent Analytics Snapshot\n\nUse the official CLI when collecting live Agent Analytics data:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.20 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.20 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.20 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.20 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.20 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.20 experiments list \"$PROJECT_SLUG\"\n```\n\nIf login is needed, prefer the regular `agent-analytics` skill's browser approval or detached login guidance.\n\nBefore interpreting the snapshot, also read the compact project memory:\n\n```bash\nnpx --yes @agent-analytics/cli@0.5.20 context get \"$PROJECT_SLUG\"\n```\n\nIf the autoresearch run reveals durable product truth that should guide future analytics, use the regular `agent-analytics` skill's project context workflow to read the existing context, merge the compact update, and write it back. Do not store raw round notes or time-bound metric values as project context.\n\n## Scoring\n\nUse Borda scoring:\n\n- first place: 2 points\n- second place: 1 point\n- third place: 0 points\n\nJudge by:\n\n- specificity to the product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with analytics data\n- respect for guardrails\n\n## Output\n\n`final_variants.md` must include:\n\n- candidate_1\n- candidate_2\n- exact changed copy\n- rationale\n- risks\n- recommended experiment name\n- experiment shape\n- data limitations\n- clear note that the experiment has not been wired yet\n\nOnly create or wire an experiment after explicit human approval.\n\nFile v1.0.5:_meta.json\n\n{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.5\",\n \"publishedAt\": 1776631743775\n}\n\nFile v1.0.5:references/brief-template.md\n\n# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.20 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.20 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.20 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.20 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.20 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.20 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.20 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project list\n```\n\n## Live Data Snapshot\n\nSummary:\n\n-\n\nPrimary event:\n\n-\n\nProxy event:\n\n-\n\nGuardrails:\n\n-\n\nData limitations:\n\n-\n\n## Drift Constraints\n\nCandidates must not:\n\n-\n\nCandidates should:\n\n-\n\n## Judging Rubric\n\nRank candidates by:\n\n- specificity to this product\n- clarity for the target audience\n- likely primary-event intent\n- preservation of product truth\n- low competitor-sayable language\n- fit with available analytics data\n\nFile v1.0.5:references/final-variants-template.md\n\n# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step\n\nFile v1.0.5:references/program.md\n\n# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project de\n\nArchive v1.0.4: 7 files, 8886 bytes\n\nFiles: references/brief-template.md (3424b), references/final-variants-template.md (631b), references/program.md (4814b), scripts/collect_agent_analytics_snapshot.sh (1542b), scripts/init_autoresearch_run.sh (590b), SKILL.md (7846b), _meta.json (147b)\n\nArchive v1.0.3: 7 files, 8486 bytes\n\nFiles: references/brief-template.md (3382b), references/final-variants-template.md (631b), references/program.md (4814b), scripts/collect_agent_analytics_snapshot.sh (1506b), scripts/init_autoresearch_run.sh (590b), SKILL.md (6719b), _meta.json (147b)","readmeExcerpt":"Skill: agent-analytics-autoresearch Owner: dannyshmueli Summary: Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API. Tags: ","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"bash /scripts/init_autoresearch_run.sh homepage-signup"},{"language":"text","snippet":"Read brief.md and run the autoresearch growth loop. Use the latest data snapshot. Run 5 rounds. Append one row per round to results.tsv and write final_variants.md with two distinct variants for review."},{"language":"bash","snippet":"bash /scripts/collect_agent_analytics_snapshot.sh my-site signup cta_click"},{"language":"bash","snippet":"npx --yes @agent-analytics/cli@0.5.37 insights \"$PROJECT_SLUG\" --period 7d\nnpx --yes @agent-analytics/cli@0.5.37 pages \"$PROJECT_SLUG\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.37 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nnpx --yes @agent-analytics/cli@0.5.37 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.37 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nnpx --yes @agent-analytics/cli@0.5.37 experiments list \"$PROJECT_SLUG\""},{"language":"bash","snippet":"npx --yes @agent-analytics/cli@0.5.37 context get \"$PROJECT_SLUG\""},{"language":"text","snippet":"variants: control,candidate_1,candidate_2\ngoal: \nproxy: "}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: agent-analytics-autoresearch\ndescription: \"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API.\"\nversion: 1.0.12\nauthor: dannyshmueli\nlicense: MIT\nrepository: https://github.com/Agent-Analytics/skills\nhomepage: https://agentanalytics.sh\ncompatibility: Requires a coding agent that can read and write local files. Agent Analytics data collection requires npx and browser approval or detached login. The loop can also run from pasted reports, SQL output, CSV exports, or existing analytics files.\ntags:\n - analytics\n - autoresearch\n - growth\n - experiments\n - ab-testing\n - landing-pages\nprovides:\n - capability: autoresearch\n - capability: ab-testing\n - capability: growth-experiments\n - capability: landing-page-optimization\nmetadata:\n openclaw:\n requires:\n anyBins:\n - npx\n---\n\n# Agent Analytics Autoresearch\n\nUse this skill when the user wants a data-informed growth loop for landing pages, onboarding, pricing, CTAs, signup, checkout, activation, or other experiment candidates.\n\nThis skill is based on:\n\n- Autoresearch Growth template: \n- Agent Analytics: \n- Regular Agent Analytics skill: \n\nUse the regular `agent-analytics` skill for general setup, tracking installation, ad hoc reporting, and normal experiment operations. Use this skill for structured variant generation and judging from a project brief plus analytics data.\n\n## Core Rule\n\nDo not edit production copy, product code, or live experiment setup while running the loop unless the user explicitly asks. Produce reviewable artifacts first.\n\nDefault mode is review-only: generate variants, log rounds, and write `final_variants.md`.\n\nAfter explicit human approval, continue into the outer experiment loop when requested: implement the approved variant or variants, create the experiment, run it, measure it with Agent Analytics or another analytics source, save the results as the next snapshot, and start the next autoresearch run from evidence.\n\n## Inputs\n\nThe loop needs:\n\n- target surface\n- current control copy\n- product truth\n- audience\n- primary metric\n- proxy metric\n- guardrails\n- analytics snapshot or data brief\n- drift constraints\n\nAgent Analytics is preferred, but not required. Accept any evidence source: Agent Analytics CLI/API, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, dashboard screenshots summarized by the user, or hand-written notes.\n\nWhen Agent Analytics is the evidence source, use project context as the self-improving product memory for the loop. Read `context get ` before collecting a snapsh"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn7caxjvqk9fengp67p290smnn800sv9\",\n \"slug\": \"agent-analytics-autoresearch\",\n \"version\": \"1.0.12\",\n \"publishedAt\": 1791547065276\n}"},{"path":"references/brief-template.md","content":"# Growth Loop Brief\n\n## Target\n\n- Project:\n- Surface:\n- Public URL:\n- Local source file or copy source:\n- Primary metric:\n- Proxy metric:\n- Guardrail metrics:\n- Recommended experiment name:\n- Variant shape:\n\n```text\nvariants: control,candidate_1,candidate_2\ngoal: \nproxy: \n```\n\n## Product Truth\n\nDescribe what the product is, who it serves, and what must remain true in every candidate.\n\nInclude:\n\n- core promise\n- target audience\n- strongest differentiator\n- language the product should own\n- claims the product can support\n- claims the product should not make\n\n## Audience\n\nPrimary audience:\n\n-\n\nPain:\n\n-\n\nDesired action:\n\n-\n\n## Current Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Analytics Commands Or Data\n\nList commands, API calls, SQL queries, exports, or pasted data the agent should use before generating variants.\n\nAgent Analytics CLI example:\n\n```bash\n# Run once if this machine or agent runtime is not logged in.\nnpx --yes @agent-analytics/cli@0.5.37 login\n\nPROJECT_SLUG=\nPRIMARY_EVENT=\nPROXY_EVENT=\nRUN_DATE=$(date +%F)\n\nmkdir -p \"data/$RUN_DATE\"\n\n# Keep collecting the full snapshot even if one analytics command fails.\n# Failed commands write their error output and exit code into the saved file.\nrun_snapshot_command() {\n output_file=\"$1\"\n shift\n set +e\n \"$@\" > \"$output_file\" 2>&1\n command_status=$?\n set -e\n perl -i -pe 's/\\e\\[[0-9;]*m//g' \"$output_file\"\n if [ \"$command_status\" -ne 0 ]; then\n printf '\\ncommand_exit_code: %s\\n' \"$command_status\" >> \"$output_file\"\n fi\n}\n\nrun_snapshot_command \"data/$RUN_DATE/insights.txt\" npx --yes @agent-analytics/cli@0.5.37 insights \"$PROJECT_SLUG\" --period 7d\nrun_snapshot_command \"data/$RUN_DATE/pages.txt\" npx --yes @agent-analytics/cli@0.5.37 pages \"$PROJECT_SLUG\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/funnel.txt\" npx --yes @agent-analytics/cli@0.5.37 funnel \"$PROJECT_SLUG\" --steps \"page_view,$PROXY_EVENT,$PRIMARY_EVENT\" --since 7d\nrun_snapshot_command \"data/$RUN_DATE/${PROXY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.37 events \"$PROJECT_SLUG\" --event \"$PROXY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/${PRIMARY_EVENT}-events.txt\" npx --yes @agent-analytics/cli@0.5.37 events \"$PROJECT_SLUG\" --event \"$PRIMARY_EVENT\" --days 7 --limit 50\nrun_snapshot_command \"data/$RUN_DATE/experiments.txt\" npx --yes @agent-analytics/cli@0.5.37 experiments list \"$PROJECT_SLUG\"\n```\n\nGeneric placeholders:\n\n```bash\n summary --project --period 7d\n pages --project --period 7d\n events --project --event --period 7d\n events --project --event --period 7d\n funnel --project --steps \"page_view,,\"\n experiments --project li"},{"path":"references/final-variants-template.md","content":"# Final Variants\n\n## Target Experiment\n\n```text\nexperiment:\nvariants: control,candidate_1,candidate_2\ngoal:\nproxy:\n```\n\nThe experiment has not been wired yet.\n\n## Control\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\n## Candidate 1\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Candidate 2\n\nHeadline:\n\n```text\n\n```\n\nSubheadline:\n\n```text\n\n```\n\nPrimary CTA:\n\n```text\n\n```\n\nSupporting copy:\n\n```text\n\n```\n\nHypothesis:\n\nRisks:\n\n## Why These Two\n\n## Data Notes\n\n## Next Step"},{"path":"references/program.md","content":"# Autoresearch Growth Loop\n\nYou are running an instruction-driven growth loop. Produce two high-quality variants that can be tested against the current control for the project described in `brief.md`.\n\nDo not change product code while running the loop. Produce reviewable copy artifacts first.\n\nDefault mode is review-only. Only move into implementation, experiment creation, or measurement after the user explicitly approves that next phase.\n\n## Setup\n\n1. Read `brief.md` fully.\n2. Treat `brief.md` as the source of truth for the project, audience, surface, control, metrics, analytics data, and drift constraints.\n3. If `results.tsv` does not exist, create it with:\n\n```tsv\nround\tcandidate_a\tcandidate_b\tcandidate_ab\twinner\tborda_a\tborda_b\tborda_ab\tstatus\trationale\n```\n\n4. If `final_variants.md` exists, overwrite it only at the end of a completed loop.\n\n## Data Brief\n\nBefore generating copy, collect or read the data described in `brief.md`.\n\nThe data source can be Agent Analytics, another analytics CLI, an API, SQL, CSV, exported reports, product logs, or manually supplied data.\n\nRules:\n\n- Never invent numbers.\n- If a command fails, record the failure.\n- Treat missing data as missing data, not zero data.\n- Separate primary outcome data from proxy and guardrail data.\n- If data is sparse, say so and treat it as weak signal.\n\n## Scope\n\nDuring the loop, edit only:\n\n- `results.tsv`\n- `final_variants.md`\n- optional scratch notes\n\nDo not edit the live site, app, product code, or experiment setup until the variants have been reviewed.\n\n## Product Truth\n\nEvery candidate must preserve the product truth from `brief.md`.\n\nPenalize:\n\n- generic category language\n- copy a competitor could say word for word\n- unsupported claims\n- drift away from the real product value\n- clickbait that weakens primary conversion intent\n- changes that ignore the current control's strengths\n\nReward:\n\n- specificity\n- clear audience fit\n- concrete user outcome\n- stronger primary-event intent\n- honest use of the available data\n- language only this product could credibly say\n\n## Loop\n\nRun at least 5 rounds unless `brief.md` specifies a different count.\n\nEach round has four phases.\n\n### 1. Candidate A\n\nFor round 1, candidate A is your first new hypothesis based on the brief, control, and data.\n\nFor later rounds, candidate A is the previous round winner.\n\nInclude the editable parts named in the brief, usually headline, subheadline, CTA, supporting copy, and hypothesis.\n\n### 2. Critique\n\nCritique candidate A harshly:\n\n- what is generic\n- what a competitor could say\n- where value is unclear\n- where copy drifts from product truth\n- whether primary-event intent is strong enough\n- whether the control is clearer\n\n### 3. Candidate B And Synthesis AB\n\nWrite candidate B from the critique as if starting fresh. It may use the brief and critique, but it should not be a mild rewrite of A.\n\nThen write candidate AB by combining the strongest parts of A and B. Do not average them into bland middle copy.\n\n### 4."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API. Skill: agent-analytics-autoresearch Owner: dannyshmueli Summary: Run an autoresearch-style growth loop for landing pages, onboarding, pricing, and experiment candidates. Collect or read analytics snapshots, preserve product truth, generate/critique/synthesize variants, blind-rank with Borda scoring, and output two review-ready A/B test variants. Works with any analytics data; best with Agent Analytics CLI/API. 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