{"id":"7b343d91-c9d8-46dd-8eb0-c5a4b8f4eb33","entityType":"agent","slug":"clawhub-veeicwgy-devtool-answer-monitor","name":"DevTool Answer Monitor","canonicalUrl":"https://www.xpersona.co/agent/clawhub-veeicwgy-devtool-answer-monitor","canonicalPath":"/agent/clawhub-veeicwgy-devtool-answer-monitor","generatedAt":"2026-10-10T10:48:30.052Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T04:55:45.290Z","emptyReason":null},"description":"Use when the user wants to monitor how ChatGPT, Claude, Gemini, and other LLMs describe a developer tool, API, SDK, or open-source project. DevTool Answer Mo...","descriptionLabel":"Source description","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.7K downloads reported by the source. Last updated 10/10/2026.","installCommand":"clawhub skill install s17b190tf85c34fs78qx3mh12n83g7at:devtool-answer-monitor","sourceUrl":"https://clawhub.ai/veeicwgy/devtool-answer-monitor","homepage":"https://clawhub.ai/veeicwgy/skills/devtool-answer-monitor","primaryLinks":[{"label":"View on ClawHub","url":"https://clawhub.ai/veeicwgy/devtool-answer-monitor","kind":"source"},{"label":"Homepage","url":"https://clawhub.ai/veeicwgy/skills/devtool-answer-monitor","kind":"homepage"}],"safetyScore":84,"overallRank":62,"popularityScore":64,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"DevTool Answer Monitor technical dossier on Xpersona with agent coverage, OPENCLEW support, and live trust metadata."},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-10-10T04:55:45.290Z","emptyReason":null},"protocols":[{"protocol":"OPENCLEW","label":"OpenClaw","status":"self-declared","notes":"Declared in the public agent profile."}],"capabilities":[],"verifiedCount":0,"selfDeclaredCount":1,"capabilityMatrix":{"rows":[{"key":"OPENCLEW","type":"protocol","support":"unknown","confidenceSource":"profile","notes":"Listed on profile"}],"flattenedTokens":"protocol:OPENCLEW|unknown|profile"}},"adoption":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T04:55:45.290Z","emptyReason":null},"stars":null,"forks":null,"downloads":1671,"packageName":null,"latestVersion":"0.3.0","tractionLabel":"1.7K downloads"},"release":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T04:55:45.227Z","emptyReason":null},"lastUpdatedAt":"2026-10-10T04:55:45.290Z","lastCrawledAt":"2026-10-10T04:55:45.227Z","lastIndexedAt":null,"nextCrawlAt":"2026-10-11T04:55:45.227Z","lastVerifiedAt":null,"highlights":[{"version":"0.3.0","createdAt":"2026-04-23T07:36:46.206Z","changelog":"Rename the project to DevTool Answer Monitor, add a zero-install demo viewer, and publish public benchmark stories.","fileCount":114,"zipByteSize":116116}]},"execution":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":"clawhub skill install s17b190tf85c34fs78qx3mh12n83g7at:devtool-answer-monitor","setupComplexity":"low","setupSteps":["Install using `clawhub skill install s17b190tf85c34fs78qx3mh12n83g7at:devtool-answer-monitor` in an isolated environment before connecting it to live workloads.","No published capability contract is available yet, so validate auth and request/response behavior manually.","Review the upstream CLAWHUB listing at https://clawhub.ai/veeicwgy/devtool-answer-monitor before using production credentials."],"contract":{"contractStatus":"missing","authModes":[],"requires":[],"forbidden":[],"supportsMcp":false,"supportsA2a":false,"supportsStreaming":false,"inputSchemaRef":null,"outputSchemaRef":null,"dataRegion":null,"contractUpdatedAt":null,"sourceUpdatedAt":null,"freshnessSeconds":null},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"CLAWHUB","generatedAt":"2026-10-10T10:48:30.051Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-veeicwgy-devtool-answer-monitor/trust"}},"reliability":{"evidence":{"source":"runtime-metrics","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No trust, reliability, or runtime telemetry is available."},"trust":{"status":"unavailable","handshakeStatus":"UNKNOWN","verificationFreshnessHours":null,"reputationScore":null,"p95LatencyMs":null,"successRate30d":null,"fallbackRate":null,"attempts30d":null,"trustUpdatedAt":null,"trustConfidence":"unknown","sourceUpdatedAt":null,"freshnessSeconds":null},"decisionGuardrails":{"doNotUseIf":["Contract metadata is missing or unavailable for deterministic execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T04:55:45.290Z","emptyReason":null},"readme":"Skill: DevTool Answer Monitor\n\nOwner: veeicwgy\n\nSummary: Use when the user wants to monitor how ChatGPT, Claude, Gemini, and other LLMs describe a developer tool, API, SDK, or open-source project. DevTool Answer Mo...\n\nTags: latest:0.3.0\n\nVersion history:\n\nv0.3.0 | 2026-04-23T07:36:46.206Z | user\n\nRename the project to DevTool Answer Monitor, add a zero-install demo viewer, and publish public benchmark stories.\n\nArchive index:\n\nArchive v0.3.0: 114 files, 116116 bytes\n\nFiles: ai_visibility/__init__.py (22b), ai_visibility/__main__.py (79b), ai_visibility/cli.py (53b), assets/logo.svg (2290b), assets/mineru-before-after.svg (5307b), benchmark/mineru-public-benchmark.md (2167b), benchmark/README.md (682b), benchmark/sciverse-api-public-benchmark.md (1868b), CHANGELOG.md (3788b), CONTRIBUTING.md (941b), data/leaderboards/model_leaderboard.csv (454b), data/leaderboards/model_leaderboard.md (281b), data/manual.multi.sample.json (1663b), data/manual.sample.json (836b), data/models.multi.sample.json (411b), data/models.sample.json (2159b), data/query-pools/fastapi-open-source-library-example.json (810b), data/query-pools/langfuse-developer-tool-example.json (859b), data/query-pools/mineru-example.json (1463b), data/query-pools/posthog-saas-example.json (851b), data/query-pools/sciverse-api-integration-example.json (4020b), data/repair-validations/mineru-competitor-fix-case.json (794b), data/repair-validations/mineru-error-fix-case.json (635b), data/repair-validations/mineru-outdated-fix-case.json (625b), data/repair-validations/sample-repair-validation.json (856b), data/runs/demo-api-run/run_manifest.json (215b), data/runs/demo-run/run_manifest.json (211b), data/runs/multi-model-demo/summary.json (989b), data/runs/repair-t14-run/summary.json (670b), data/runs/repair-t14-run/weekly_report.md (106b), data/runs/repair-t7-run/summary.json (786b), data/runs/repair-t7-run/weekly_report.md (105b), data/runs/sample-run/metrics.csv (342b), data/runs/sample-run/run_manifest.json (273b), data/runs/sample-run/summary.json (1747b), data/runs/sample-run/weekly_report.md (932b), data/runs/sciverse-sample-run/metrics.csv (399b), data/runs/sciverse-sample-run/run_manifest.json (289b), data/runs/sciverse-sample-run/summary.json (2238b), data/runs/sciverse-sample-run/weekly_report.md (1113b), devtool_answer_monitor/__init__.py (22b), devtool_answer_monitor/__main__.py (79b), devtool_answer_monitor/cli.py (53b), docs_execution_design.md (5152b), docs_release_design.md (4903b), docs_release_readiness_audit.md (3060b), docs_upgrade_audit.md (2395b), docs/activation-metrics.md (2404b), docs/data/demo-metrics.json (7927b), docs/demo.css (4714b), docs/demo.js (4766b), docs/for-beginners.md (2857b), docs/getting-started.md (6419b), docs/index.html (2785b), docs/metric-definition.md (2165b), examples/mineru-case-study.md (2682b), geo_monitor/__init__.py (22b), geo_monitor/__main__.py (79b), geo_monitor/cli.py (2552b), install.sh (488b), manifest.json (634b), notebooks/README.md (633b), playbooks/agent-readiness.md (4194b), playbooks/content-platform-map.md (3139b), playbooks/developer-tool-surface-priority.md (4286b), playbooks/keyword-strategy.md (3708b), playbooks/model-datasources.md (3201b), playbooks/monitoring-system.md (3704b), playbooks/negative-fix-sop.md (3531b), playbooks/scientific-product-visibility.md (4262b), playbooks/seo-geo-claude-skills-integration.md (2828b), playbooks/visibility-workflow-architecture.md (3397b), query-pools/mineru-example.json (2429b), quickstart.sh (1352b), README.md (8150b), README.zh-CN.md (5985b), release-notes/v0.2.0.md (564b), release-notes/v0.2.1.md (813b), release-notes/v0.2.2.md (507b), release-notes/v0.2.3.md (405b)\n\nFile v0.3.0:SKILL.md\n\n---\nname: devtool-answer-monitor\ndescription: >\n  Use when the user wants to monitor how ChatGPT, Claude, Gemini, and other LLMs describe a developer tool, API, SDK, or open-source project. DevTool Answer Monitor is the companion skill for the devtool-answer-monitor repo and covers query pool design, four-metric monitoring, model-specific content placement, content checks, negative-answer repair, activation analysis, and T+7 or T+14 regression validation.\nlicense: MIT\nallowed-tools: Read\nmetadata:\n  openclaw:\n    emoji: \"📈\"\n    author: \"veeicwgy\"\n    homepage: \"https://github.com/veeicwgy/devtool-answer-monitor\"\n    requires:\n      env:\n        - OPENAI_API_KEY\n        - OPENAI_BASE_URL\n      bins:\n        - python3\n        - bash\n    primaryEnv: OPENAI_API_KEY\n    env:\n      - name: OPENAI_API_KEY\n        description: \"Optional provider API key for API collection mode only. Quickstart replay and manual paste mode do not need it.\"\n        required: false\n        sensitive: true\n      - name: OPENAI_BASE_URL\n        description: \"Optional OpenAI-compatible gateway URL for multi-provider API collection mode.\"\n        required: false\n        sensitive: false\n---\n\n# Monitor What LLMs Say Before Users Choose Your Dev Tool\n\nUse this skill as the **main visibility workflow router** for developer tools and open-source products.\n\n**Brand:** DevTool Answer Monitor\n\n**Companion repo:** [`devtool-answer-monitor`](https://github.com/veeicwgy/devtool-answer-monitor)\n\nUse this when you want an agent to help you monitor how LLMs describe your product, build a reusable query pool, diagnose negative or outdated answers, and plan what to fix next.\n\n## Safety First\n\n- Treat this root skill as a **read-only workflow router**.\n- Default to `quickstart replay` or `manual paste mode` when you only need examples or scoring help.\n- Do not ask users to paste API keys into chat. If API collection mode is needed, tell them to configure local environment variables themselves and then hand off execution to `visibility-monitor`.\n- Review local scripts such as `install.sh`, `quickstart.sh`, and the selected runner before executing shell commands.\n\n## Start Here\n\nCopy one of these prompts to begin:\n\n- `Analyze how ChatGPT and Claude describe my API docs`\n- `Build a developer-tool answer monitoring query pool for my SDK`\n- `Find negative or outdated LLM claims about my project`\n\n## 30-Second Result\n\n**Typical input**\n\n- product truth such as a README, docs, changelog, integrations, or positioning page\n- answer evidence such as copied model answers, screenshots, or cited URLs\n- scope such as target models, languages, regions, or a repeated query set\n\n**What this skill returns**\n\n- a reusable query pool\n- raw evidence and a score draft plan\n- a monitoring summary and report outline\n- a repair backlog with T+7 or T+14 validation points\n\n**Companion demo and sample outputs**\n\n- Zero-install demo: [sample-run viewer](https://cdn.jsdelivr.net/gh/veeicwgy/devtool-answer-monitor@main/docs/index.html)\n- Public benchmarks: [MinerU story](https://github.com/veeicwgy/devtool-answer-monitor/blob/main/benchmark/mineru-public-benchmark.md) and [Sciverse API story](https://github.com/veeicwgy/devtool-answer-monitor/blob/main/benchmark/sciverse-api-public-benchmark.md)\n- Sample outputs: [leaderboard snapshot](https://github.com/veeicwgy/devtool-answer-monitor/blob/main/assets/leaderboard-sample.png) and [repair trend snapshot](https://github.com/veeicwgy/devtool-answer-monitor/blob/main/assets/repair-trend-sample.png)\n\n## Trigger\n\nUse this skill when the task is any of the following:\n\n1. generate a visibility query matrix and Query Pool from product truth;\n2. monitor how multiple LLMs mention, recommend, or misunderstand a product;\n3. plan model-specific content placement based on datasource patterns;\n4. check whether a draft page, FAQ, changelog, or case study is ready to influence model answers;\n5. repair wrong, negative, outdated, or competitor-only answers;\n6. verify whether a repair action improved metrics at T+7 or T+14;\n7. help a user choose between quickstart replay, manual paste mode, and API collection mode.\n\n## Beginner Routing\n\nWhen the user is new to the repository, route them in this order.\n\n| Situation | Next step |\n|---|---|\n| Needs environment check first | open `docs/getting-started.md` and review the environment check section |\n| Wants environment-free first run | open `docs/index.html` or `docs/for-beginners.md` |\n| Wants a short explanation first | open `docs/for-beginners.md` |\n| Wants deeper onboarding | open `docs/getting-started.md` |\n| Wants the English repository overview | open `README.md` |\n| Wants the Chinese repository overview | open `README.zh-CN.md` |\n\n## Visibility Strategy\n\nAlways keep the workflow in this order:\n\n| Stage | Goal |\n|---|---|\n| Query design | turn product truth into scenario matrix, three-layer keywords, and Query Pool seeds |\n| Monitoring | score mention, positive mention, capability accuracy, and ecosystem accuracy |\n| Placement | map each target model to likely datasource channels and publication surfaces |\n| Repair | classify bad answers into information error, negative evaluation, outdated information, or competitor insertion |\n| Activation | analyze whether answers help a user install, integrate, or invoke the product |\n| Regression | compare follow-up runs and check whether metrics improved after action |\n\n## Mode Selection\n\nChoose the execution mode before running monitoring.\n\n| Mode | Use when | Typical inputs |\n|---|---|---|\n| Quickstart replay | user wants the fastest first run without API setup | sample model config + sample manual responses |\n| Manual paste mode | user already has copied answers from chat tools | Query Pool + manual response JSON |\n| API collection mode | user wants repeatable real monitoring | Query Pool + model config + locally configured provider env vars |\n\n## Input Contract\n\nPrepare as many of the following as possible before execution.\n\n| Input | Examples |\n|---|---|\n| Product truth | README, docs, changelog, integrations, positioning |\n| Answer evidence | raw answers, screenshots, copied responses, cited links |\n| Monitoring scope | models, languages, regions, dates, repeated query set |\n| Publishing targets | docs, blog, GitHub, Q&A, partner channels |\n\n## Workflow Router\n\nChoose the next sub-skill according to the user's immediate need.\n\n| Situation | Next Skill |\n|---|---|\n| Need query design and scenario clustering | `visibility-query-matrix` |\n| Need weekly monitoring, evidence logging, report output, or shell execution after explicit user approval | `visibility-monitor` |\n| Need pre-publish content QA | `visibility-content-check` |\n| Need to repair bad answers and define regression checks | `visibility-repair` |\n\n## Required Reading Order\n\nFor a full program, read these repository documents in sequence:\n\n1. `playbooks/visibility-workflow-architecture.md`\n2. `playbooks/keyword-strategy.md`\n3. `playbooks/monitoring-system.md`\n4. `playbooks/model-datasources.md`\n5. `playbooks/content-platform-map.md`\n6. `playbooks/negative-fix-sop.md`\n\n## Output Contract\n\nAlways preserve the following outputs.\n\n| Output | Description |\n|---|---|\n| Query foundation | scenario matrix, keyword layers, Query Pool |\n| Monitoring outputs | raw evidence, score draft, summary, report, leaderboard or overview |\n| Action plan | content placement priorities and repair backlog |\n| Regression record | T+7 and T+14 comparisons after key fixes |\n\n## Positioning\n\nDevTool Answer Monitor is the skill layer for the `devtool-answer-monitor` repo.\n\n- Use the **repo** when you want runnable demos, scripts, and report artifacts.\n- Use the **skill** when you want an agent-guided workflow for monitoring, repair, and regression planning.\n\n## Handoff Rules\n\nAt the end of each run, preserve:\n\n1. which product was optimized;\n2. which models and languages were in scope;\n3. which queries are reused in weekly tracking;\n4. what the top three visibility weaknesses are;\n5. what actions are already completed and what still needs validation.\n\nFile v0.3.0:skills/visibility-content-check/SKILL.md\n\n---\nname: visibility-content-check\ndescription: >\n  Use when the user wants to quality-check a draft before publishing it to improve AI visibility. Covers citation friendliness, Q&A structure, factual density, entity clarity, authority signals, and release readiness for developer-tool or open-source content.\n---\n\n# visibility-content-check\n\nUse this skill to decide whether a draft is ready to influence LLM answers.\n\n## Trigger\n\nUse this skill before publishing a tutorial, comparison article, FAQ page, changelog summary, or product page update.\n\n## Outputs\n\n| Output | Description |\n|---|---|\n| Visibility readiness verdict | go, revise, or block |\n| Issue list | missing signals, weak structure, entity ambiguity, thin evidence |\n| Revision priorities | top fixes before publish |\n\n## Next Best Skill\n\nIf the draft is blocked because of negative framing or wrong facts, use `visibility-repair`.\n\nFile v0.3.0:skills/visibility-monitor/SKILL.md\n\n---\nname: visibility-monitor\ndescription: >\n  Use when the user wants to run or design AI visibility monitoring for a developer tool, API, SDK, or open-source project. Covers Query Pool execution, evidence logging, four-metric scoring, weekly reporting, anomaly detection, and action prioritization across multiple LLMs and languages.\nallowed-tools: Read, Write, Edit, Bash\nmetadata:\n  openclaw:\n    author: \"veeicwgy\"\n    homepage: \"https://github.com/veeicwgy/devtool-answer-monitor\"\n    requires:\n      env:\n        - OPENAI_API_KEY\n        - OPENAI_BASE_URL\n      bins:\n        - python3\n        - bash\n    primaryEnv: OPENAI_API_KEY\n    env:\n      - name: OPENAI_API_KEY\n        description: \"Optional provider API key for API collection mode. Not needed for quickstart replay or manual paste mode.\"\n        required: false\n        sensitive: true\n      - name: OPENAI_BASE_URL\n        description: \"Optional OpenAI-compatible gateway URL for multi-provider monitoring.\"\n        required: false\n        sensitive: false\n---\n\n# visibility-monitor\n\nUse this skill to turn repeated model checks into a consistent visibility monitoring workflow.\n\n## Safety\n\n- Use `quickstart replay` or `manual paste mode` first when the user does not need live API calls.\n- Keep provider keys in local shell environment variables. Do not ask users to paste secrets into chat.\n- Inspect `install.sh`, `quickstart.sh`, and the selected runner before executing Bash commands.\n\n## Trigger\n\nUse this skill when the user already has, or is ready to create, a Query Pool and wants to know:\n\n1. whether the product is being mentioned;\n2. whether mentions are positive, neutral, or negative;\n3. whether the model understands the product's capabilities;\n4. whether the model understands the product's ecosystem and integrations.\n\n## Quick Start\n\n### Zero-key paths first\n\n- `bash quickstart.sh` replays sample data and does not require provider keys.\n- `python -m devtool_answer_monitor run --manual-responses ...` scores copied answers without live API calls.\n\n### Choose the right runner\n\n| Runner | API Type | Works with | Use when |\n|---|---|---|---|\n| `scripts/run_monitor.py` | Responses API | OpenAI models only | GPT-4o, GPT-4.1 and variants |\n| `scripts/run_chat_completions.py` | Chat Completions API | Any OpenAI-compatible provider | Claude, Gemini, DeepSeek, Qwen, MiniMax, GLM, or any gateway |\n\n**For multi-model coverage across providers, always use `run_chat_completions.py`.**\n\n```bash\npython scripts/run_chat_completions.py \\\n    --query-pool data/query-pools/mineru-example.json \\\n    --model-config data/models.sample.json \\\n    --out-dir data/runs/my-run\n```\n\nBefore running the command above, verify that `OPENAI_API_KEY` is configured in the local shell. `OPENAI_BASE_URL` is optional and only needed for gateways or proxies.\n\nThen annotate scores and generate the report:\n\n```bash\npython -m devtool_answer_monitor report \\\n    --input data/runs/my-run/raw_responses.jsonl \\\n    --output-dir data/runs/my-run\n```\n\n## Outputs\n\n| Output | Description |\n|---|---|\n| Monitoring summary | overall run snapshot |\n| Four-metric score table | mention, positive mention, capability, ecosystem |\n| Anomaly list | urgent issues by model and query |\n| Action backlog | next content, repair, and verification moves |\n\n## Next Best Skill\n\nIf the main issue is missing or weak content, use `visibility-content-check`.\n\nIf the main issue is negative or wrong answers, use `visibility-repair`.\n\nFile v0.3.0:skills/visibility-query-matrix/SKILL.md\n\n---\nname: visibility-query-matrix\ndescription: >\n  Use when the user wants to turn a product description into a visibility query matrix and Query Pool. Covers task-scenario mapping, multilingual keyword expansion, three-level clustering, competitor comparison queries, and model-validation loops for developer tools and open-source projects.\n---\n\n# visibility-query-matrix\n\nUse this skill to build the query foundation for visibility monitoring and content planning.\n\n## Trigger\n\nUse this skill when the user has product context but does not yet have a robust Query Pool.\n\n## Outputs\n\n| Output | Description |\n|---|---|\n| Scenario matrix | user jobs to bilingual query language |\n| Three-layer keyword set | core, scenario, and long-tail clusters |\n| Query Pool seeds | reusable monitoring prompts |\n| Validation notes | where the brand is missing or weak |\n\n## Next Best Skill\n\nAfter the Query Pool is ready, use `visibility-monitor`.\n\nFile v0.3.0:skills/visibility-repair/SKILL.md\n\n---\nname: visibility-repair\ndescription: >\n  Use when the user has a wrong, negative, outdated, or competitor-only LLM answer and needs a structured repair plan. Covers negative-type classification, source tracing, authority coverage, channel-specific repair actions, and regression checks.\n---\n\n# visibility-repair\n\nUse this skill to turn a bad model answer into a repair backlog instead of reacting ad hoc.\n\n## Trigger\n\nUse this skill when an LLM answer contains any of the following:\n\n1. factual error;\n2. negative recommendation against the product;\n3. outdated product description;\n4. competitor-only recommendation in a strategic query.\n\n## Outputs\n\n| Output | Description |\n|---|---|\n| Problem type | one of the four negative categories |\n| Repair actions | source fixes, content moves, authority pages, feedback actions |\n| Regression plan | repeat checks for the affected queries |\n\n## Next Best Skill\n\nIf the next task is to quality-check the newly repaired content, use `visibility-content-check`.\n\nFile v0.3.0:benchmark/README.md\n\n# Public Benchmarks\n\nThis folder contains public, version-controlled benchmark stories for the repository.\n\n## Available pages\n\n| Page | Focus |\n|---|---|\n| [MinerU public benchmark](mineru-public-benchmark.md) | Baseline -> T+7 -> T+14 repair loop for a developer tool |\n| [Sciverse API public benchmark](sciverse-api-public-benchmark.md) | Public snapshot for a scientific API and agent workflow |\n\n## Why these pages exist\n\nThey give new visitors something stronger than a feature list:\n\n- a concrete product name\n- a concrete metric set\n- a concrete artifact trail\n- a concrete repair story\n\nThat makes the repository easier to understand, easier to share, and easier to trust.\n\nFile v0.3.0:notebooks/README.md\n\n# Reader Guide for Non-Engineering Teammates\n\n如果你不打算直接运行脚本，可以按下面顺序阅读仓库产物：\n\n| 先看什么 | 路径 | 用途 |\n|---|---|---|\n| 周报 | `data/runs/sample-run/weekly_report.md` | 先快速理解本周结论 |\n| 指标摘要 | `data/runs/sample-run/summary.json` | 再看结构化数值 |\n| Leaderboard | `data/leaderboards/model_leaderboard.md` | 看模型维度趋势 |\n| Repair validation | `data/repair-validations/*.json` | 看修复动作如何验证 |\n\n后续如果要扩展为 notebook 或 dashboard，可以直接读取 `summary.json` 与 `metrics.csv` 作为数据源。\n\nFile v0.3.0:README.md\n\n![DevTool Answer Monitor](assets/logo.svg)\n\n# DevTool Answer Monitor\n\n> Track what LLMs say before users choose your developer tool.\n\n[![CI](https://github.com/veeicwgy/devtool-answer-monitor/actions/workflows/ci.yml/badge.svg)](https://github.com/veeicwgy/devtool-answer-monitor/actions/workflows/ci.yml)\n![Release](https://img.shields.io/github/v/release/veeicwgy/devtool-answer-monitor)\n![Python](https://img.shields.io/badge/python-3.11-blue)\n![License](https://img.shields.io/github/license/veeicwgy/devtool-answer-monitor)\n\n[Open the zero-install demo](https://cdn.jsdelivr.net/gh/veeicwgy/devtool-answer-monitor@main/docs/index.html) · [MinerU benchmark](benchmark/mineru-public-benchmark.md) · [Sciverse API benchmark](benchmark/sciverse-api-public-benchmark.md)\n\n<p align=\"center\">\n  <img src=\"assets/mineru-before-after.svg\" alt=\"MinerU before and after repair loop metrics\" width=\"31%\" />\n  <img src=\"assets/leaderboard-sample.png\" alt=\"Leaderboard sample\" width=\"31%\" />\n  <img src=\"assets/repair-trend-sample.png\" alt=\"Repair trend sample\" width=\"31%\" />\n</p>\n\n**DevTool Answer Monitor** is a reproducible monitoring and repair workflow for developer tools, APIs, SDKs, and open-source projects.\nIt connects **query design, answer monitoring, four-metric scoring, repair loops, activation analysis, and T+7/T+14 regression checks** into one practical system.\n\n## Why people star this repo\n\n- It ships a **zero-install sample viewer** so you can inspect real output before cloning anything.\n- It keeps **versioned benchmark artifacts** for MinerU and Sciverse API instead of vague marketing claims.\n- It gives you a **repair loop with visible deltas** so you can show whether docs and positioning changes actually improved model answers.\n\n> If this is the kind of workflow you want to keep on your radar, star the repo. New benchmarks, query pools, and viewer updates land here first.\n\n## Zero-install demo\n\nOpen the public sample-run viewer:\n\n- Demo: [jsDelivr static viewer](https://cdn.jsdelivr.net/gh/veeicwgy/devtool-answer-monitor@main/docs/index.html)\n- What it shows: MinerU baseline vs T+7 vs T+14, Sciverse API funnel-stage slices, top repair candidates, and stage-level gaps\n- What it does not need: API keys, package installs, or local setup\n\n## Public benchmark stories\n\n- [MinerU public benchmark](benchmark/mineru-public-benchmark.md): a real repair-loop story with baseline, T+7, and T+14 metrics\n- [Sciverse API public benchmark](benchmark/sciverse-api-public-benchmark.md): a public snapshot for awareness, selection, activation, integration, and agent scenarios\n\n## 30-second path\n\nIf you want a local run after the browser demo, follow this exact order.\n\n```bash\ngit clone https://github.com/veeicwgy/devtool-answer-monitor.git\ncd devtool-answer-monitor\nbash install.sh\nmake doctor\nbash quickstart.sh\n```\n\n## What you get first\n\n| Output | Path | Why it matters |\n|---|---|---|\n| Raw responses | `data/runs/quickstart-run/raw_responses.jsonl` | Review multi-model answer evidence |\n| Score draft | `data/runs/quickstart-run/score_draft.jsonl` | Start manual review and annotation |\n| Weekly report snapshot | `data/runs/sample-run/weekly_report.md` | See the report format a team can consume |\n| MinerU repair loop | `data/runs/repair-t7-run/summary.json` and `data/runs/repair-t14-run/summary.json` | Inspect a versioned before-and-after story |\n| Sciverse sample summary | `data/runs/sciverse-sample-run/summary.json` | See a scientific API sample with funnel-stage slices |\n| Leaderboard snapshot | `assets/leaderboard-sample.png` | Understand the default multi-model comparison |\n| Repair trend snapshot | `assets/repair-trend-sample.png` | See how follow-up runs can be visualized over time |\n\n> `quickstart.sh` creates a fresh `quickstart-run` with raw evidence, then replays built-in sample summaries to generate report and chart snapshots.\n\n## Choose your first outcome\n\n| Goal | Start here | Why |\n|---|---|---|\n| Improve mention and recommendation quality | `data/query-pools/mineru-example.json` + `docs/metric-definition.md` | Baseline the 4 core metrics first |\n| Improve downloads and installs | `docs/activation-metrics.md` + `playbooks/developer-tool-surface-priority.md` | Add actionability and source-surface prioritization |\n| Improve API calls and agent invocations | `playbooks/agent-readiness.md` + `data/query-pools/sciverse-api-integration-example.json` | Focus on integration and agent-selection queries |\n| Improve visibility for scientific products | `playbooks/scientific-product-visibility.md` | Use a product model tuned for MinerU, Sciverse API, and research workflows |\n\n## Which mode should you choose\n\n| Your situation | Recommended mode | Entry point |\n|---|---|---|\n| No API key yet and you only want to see the workflow | Quickstart replay | `bash quickstart.sh` |\n| You already copied answers from external chat products and want to score them | Manual paste mode | `python -m devtool_answer_monitor run --manual-responses ...` |\n| You want real, repeatable, multi-model monitoring | API collection mode | `python -m devtool_answer_monitor run --query-pool ... --model-config ...` |\n\n## Core commands\n\n| Command | What it does |\n|---|---|\n| `bash install.sh` | Creates `.venv` and installs dependencies |\n| `make doctor` | Checks Python, dependencies, sample files, and output directories |\n| `bash quickstart.sh` | Runs the zero-API-cost beginner demo |\n| `make demo-data` | Rebuilds the zero-install sample viewer data |\n| `make sample-report` | Rebuilds the MinerU sample report and chart assets |\n| `make sample-report-sciverse` | Rebuilds the Sciverse API sample summary and weekly report |\n| `make sample-reports` | Rebuilds both default sample report packages |\n| `python -m devtool_answer_monitor run ...` | Runs custom query-pool monitoring |\n\n> Compatibility note: `python -m ai_visibility` and `python -m geo_monitor` remain supported for existing automation.\n\n## Default sample inputs\n\n| File | Purpose |\n|---|---|\n| `data/query-pools/mineru-example.json` | Default query-pool sample for developer tools |\n| `data/query-pools/sciverse-api-integration-example.json` | Scientific API and agent workflow query-pool sample |\n| `data/models.sample.json` | Minimal single-model config |\n| `data/models.multi.sample.json` | Default multi-model config |\n| `data/manual.sample.json` | Minimal manual-response sample |\n| `data/manual.multi.sample.json` | Multi-model manual-response sample |\n| `data/runs/sample-run/summary.json` | MinerU baseline sample summary |\n| `data/runs/repair-t7-run/summary.json` | MinerU T+7 repair summary |\n| `data/runs/repair-t14-run/summary.json` | MinerU T+14 repair summary |\n| `data/runs/sciverse-sample-run/summary.json` | Complete Sciverse API sample summary |\n\n## Docs\n\n- 5-minute beginner path: [`docs/for-beginners.md`](docs/for-beginners.md)\n- Long-form onboarding: [`docs/getting-started.md`](docs/getting-started.md)\n- Metric definition: [`docs/metric-definition.md`](docs/metric-definition.md)\n- Activation metrics: [`docs/activation-metrics.md`](docs/activation-metrics.md)\n- Agent readiness: [`playbooks/agent-readiness.md`](playbooks/agent-readiness.md)\n- Developer-tool surface priority: [`playbooks/developer-tool-surface-priority.md`](playbooks/developer-tool-surface-priority.md)\n- Scientific product visibility: [`playbooks/scientific-product-visibility.md`](playbooks/scientific-product-visibility.md)\n- Benchmark index: [`benchmark/README.md`](benchmark/README.md)\n- MinerU example case: [`examples/mineru-case-study.md`](examples/mineru-case-study.md)\n\n## Repository positioning\n\nThink of this repository as:\n\n> **Answer observability for developer tools**\n>\n> It focuses on **monitoring, scoring, repair, activation, and regression**, not on generic marketing copy generation.\n\n## Contributing\n\nContributions are welcome.\n\nUseful contributions include:\n\n- new query-pool examples\n- benchmark cases\n- sample-run viewer improvements\n- runner improvements\n- report improvements\n- schema and validation improvements\n- documentation and onboarding fixes\n\nSee [`CONTRIBUTING.md`](CONTRIBUTING.md) for details.\n\n## License\n\nMIT\n\nFile v0.3.0:_meta.json\n\n{\n  \"ownerId\": \"kn7afa7n64em19qyp1eprppnc5831tm5\",\n  \"slug\": \"devtool-answer-monitor\",\n  \"version\": \"0.3.0\",\n  \"publishedAt\": 1776929806206\n}\n\nFile v0.3.0:skills/visibility-content-check/references/quality-gate.md\n\n# AI Visibility Quality Gate\n\nA draft should usually be blocked if it has any of these issues:\n\n1. no clear product definition in the opening;\n2. no authority signal such as version, official links, or package page;\n3. no extractable Q&A, table, or code example;\n4. entity confusion between product, organization, repository, and service.\n\nFile v0.3.0:skills/visibility-monitor/references/report-template.md\n\n# DevTool Answer Monitor Weekly Report Template\n\n| Model | Mention Rate | Positive Mention Rate | Capability Accuracy | Ecosystem Accuracy | Top Issue |\n|---|---|---|---|---|---|\n| Example | 20% | 80% | 70% | 40% | Missing ecosystem references |\n\nFile v0.3.0:skills/visibility-query-matrix/references/matrix-template.md\n\n# Scenario Matrix Template\n\n| User Job | Chinese Query Language | English Query Language | Priority |\n|---|---|---|---|\n| Example | 开源 PDF 解析工具 | open source PDF parser | P0 |","readmeExcerpt":"Skill: DevTool Answer Monitor Owner: veeicwgy Summary: Use when the user wants to monitor how ChatGPT, Claude, Gemini, and other LLMs describe a developer tool, API, SDK, or open-source project. DevTool Answer Mo... Tags: latest:0.3.0 Version history: v0.3.0 | 2026-04-23T07:36:46.206Z | user Rename the project to DevTool Answer Monitor, add a zero-install demo viewer, and publish public benchmark stories. Archive ind","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"python scripts/run_chat_completions.py \\\n    --query-pool data/query-pools/mineru-example.json \\\n    --model-config data/models.sample.json \\\n    --out-dir data/runs/my-run"},{"language":"bash","snippet":"python -m devtool_answer_monitor report \\\n    --input data/runs/my-run/raw_responses.jsonl \\\n    --output-dir data/runs/my-run"},{"language":"bash","snippet":"git clone https://github.com/veeicwgy/devtool-answer-monitor.git\ncd devtool-answer-monitor\nbash install.sh\nmake doctor\nbash quickstart.sh"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: devtool-answer-monitor\ndescription: >\n  Use when the user wants to monitor how ChatGPT, Claude, Gemini, and other LLMs describe a developer tool, API, SDK, or open-source project. DevTool Answer Monitor is the companion skill for the devtool-answer-monitor repo and covers query pool design, four-metric monitoring, model-specific content placement, content checks, negative-answer repair, activation analysis, and T+7 or T+14 regression validation.\nlicense: MIT\nallowed-tools: Read\nmetadata:\n  openclaw:\n    emoji: \"📈\"\n    author: \"veeicwgy\"\n    homepage: \"https://github.com/veeicwgy/devtool-answer-monitor\"\n    requires:\n      env:\n        - OPENAI_API_KEY\n        - OPENAI_BASE_URL\n      bins:\n        - python3\n        - bash\n    primaryEnv: OPENAI_API_KEY\n    env:\n      - name: OPENAI_API_KEY\n        description: \"Optional provider API key for API collection mode only. Quickstart replay and manual paste mode do not need it.\"\n        required: false\n        sensitive: true\n      - name: OPENAI_BASE_URL\n        description: \"Optional OpenAI-compatible gateway URL for multi-provider API collection mode.\"\n        required: false\n        sensitive: false\n---\n\n# Monitor What LLMs Say Before Users Choose Your Dev Tool\n\nUse this skill as the **main visibility workflow router** for developer tools and open-source products.\n\n**Brand:** DevTool Answer Monitor\n\n**Companion repo:** [`devtool-answer-monitor`](https://github.com/veeicwgy/devtool-answer-monitor)\n\nUse this when you want an agent to help you monitor how LLMs describe your product, build a reusable query pool, diagnose negative or outdated answers, and plan what to fix next.\n\n## Safety First\n\n- Treat this root skill as a **read-only workflow router**.\n- Default to `quickstart replay` or `manual paste mode` when you only need examples or scoring help.\n- Do not ask users to paste API keys into chat. If API collection mode is needed, tell them to configure local environment variables themselves and then hand off execution to `visibility-monitor`.\n- Review local scripts such as `install.sh`, `quickstart.sh`, and the selected runner before executing shell commands.\n\n## Start Here\n\nCopy one of these prompts to begin:\n\n- `Analyze how ChatGPT and Claude describe my API docs`\n- `Build a developer-tool answer monitoring query pool for my SDK`\n- `Find negative or outdated LLM claims about my project`\n\n## 30-Second Result\n\n**Typical input**\n\n- product truth such as a README, docs, changelog, integrations, or positioning page\n- answer evidence such as copied model answers, screenshots, or cited URLs\n- scope such as target models, languages, regions, or a repeated query set\n\n**What this skill returns**\n\n- a reusable query pool\n- raw evidence and a score draft plan\n- a monitoring summary and report outline\n- a repair backlog with T+7 or T+14 validation points\n\n**Companion demo and sample outputs**\n\n- Zero-install demo: [sample-run viewer](https://cdn.jsdelivr.net/gh/veeicwgy/devtool-answer-monitor@main/doc"},{"path":"skills/visibility-content-check/SKILL.md","content":"---\nname: visibility-content-check\ndescription: >\n  Use when the user wants to quality-check a draft before publishing it to improve AI visibility. Covers citation friendliness, Q&A structure, factual density, entity clarity, authority signals, and release readiness for developer-tool or open-source content.\n---\n\n# visibility-content-check\n\nUse this skill to decide whether a draft is ready to influence LLM answers.\n\n## Trigger\n\nUse this skill before publishing a tutorial, comparison article, FAQ page, changelog summary, or product page update.\n\n## Outputs\n\n| Output | Description |\n|---|---|\n| Visibility readiness verdict | go, revise, or block |\n| Issue list | missing signals, weak structure, entity ambiguity, thin evidence |\n| Revision priorities | top fixes before publish |\n\n## Next Best Skill\n\nIf the draft is blocked because of negative framing or wrong facts, use `visibility-repair`."},{"path":"skills/visibility-monitor/SKILL.md","content":"---\nname: visibility-monitor\ndescription: >\n  Use when the user wants to run or design AI visibility monitoring for a developer tool, API, SDK, or open-source project. Covers Query Pool execution, evidence logging, four-metric scoring, weekly reporting, anomaly detection, and action prioritization across multiple LLMs and languages.\nallowed-tools: Read, Write, Edit, Bash\nmetadata:\n  openclaw:\n    author: \"veeicwgy\"\n    homepage: \"https://github.com/veeicwgy/devtool-answer-monitor\"\n    requires:\n      env:\n        - OPENAI_API_KEY\n        - OPENAI_BASE_URL\n      bins:\n        - python3\n        - bash\n    primaryEnv: OPENAI_API_KEY\n    env:\n      - name: OPENAI_API_KEY\n        description: \"Optional provider API key for API collection mode. Not needed for quickstart replay or manual paste mode.\"\n        required: false\n        sensitive: true\n      - name: OPENAI_BASE_URL\n        description: \"Optional OpenAI-compatible gateway URL for multi-provider monitoring.\"\n        required: false\n        sensitive: false\n---\n\n# visibility-monitor\n\nUse this skill to turn repeated model checks into a consistent visibility monitoring workflow.\n\n## Safety\n\n- Use `quickstart replay` or `manual paste mode` first when the user does not need live API calls.\n- Keep provider keys in local shell environment variables. Do not ask users to paste secrets into chat.\n- Inspect `install.sh`, `quickstart.sh`, and the selected runner before executing Bash commands.\n\n## Trigger\n\nUse this skill when the user already has, or is ready to create, a Query Pool and wants to know:\n\n1. whether the product is being mentioned;\n2. whether mentions are positive, neutral, or negative;\n3. whether the model understands the product's capabilities;\n4. whether the model understands the product's ecosystem and integrations.\n\n## Quick Start\n\n### Zero-key paths first\n\n- `bash quickstart.sh` replays sample data and does not require provider keys.\n- `python -m devtool_answer_monitor run --manual-responses ...` scores copied answers without live API calls.\n\n### Choose the right runner\n\n| Runner | API Type | Works with | Use when |\n|---|---|---|---|\n| `scripts/run_monitor.py` | Responses API | OpenAI models only | GPT-4o, GPT-4.1 and variants |\n| `scripts/run_chat_completions.py` | Chat Completions API | Any OpenAI-compatible provider | Claude, Gemini, DeepSeek, Qwen, MiniMax, GLM, or any gateway |\n\n**For multi-model coverage across providers, always use `run_chat_completions.py`.**\n\n```bash\npython scripts/run_chat_completions.py \\\n    --query-pool data/query-pools/mineru-example.json \\\n    --model-config data/models.sample.json \\\n    --out-dir data/runs/my-run\n```\n\nBefore running the command above, verify that `OPENAI_API_KEY` is configured in the local shell. `OPENAI_BASE_URL` is optional and only needed for gateways or proxies.\n\nThen annotate scores and generate the report:\n\n```bash\npython -m devtool_answer_monitor report \\\n    --input data/runs/my-run/raw_responses.jsonl \\\n    --output-dir data/run"},{"path":"skills/visibility-query-matrix/SKILL.md","content":"---\nname: visibility-query-matrix\ndescription: >\n  Use when the user wants to turn a product description into a visibility query matrix and Query Pool. Covers task-scenario mapping, multilingual keyword expansion, three-level clustering, competitor comparison queries, and model-validation loops for developer tools and open-source projects.\n---\n\n# visibility-query-matrix\n\nUse this skill to build the query foundation for visibility monitoring and content planning.\n\n## Trigger\n\nUse this skill when the user has product context but does not yet have a robust Query Pool.\n\n## Outputs\n\n| Output | Description |\n|---|---|\n| Scenario matrix | user jobs to bilingual query language |\n| Three-layer keyword set | core, scenario, and long-tail clusters |\n| Query Pool seeds | reusable monitoring prompts |\n| Validation notes | where the brand is missing or weak |\n\n## Next Best Skill\n\nAfter the Query Pool is ready, use `visibility-monitor`."},{"path":"skills/visibility-repair/SKILL.md","content":"---\nname: visibility-repair\ndescription: >\n  Use when the user has a wrong, negative, outdated, or competitor-only LLM answer and needs a structured repair plan. Covers negative-type classification, source tracing, authority coverage, channel-specific repair actions, and regression checks.\n---\n\n# visibility-repair\n\nUse this skill to turn a bad model answer into a repair backlog instead of reacting ad hoc.\n\n## Trigger\n\nUse this skill when an LLM answer contains any of the following:\n\n1. factual error;\n2. negative recommendation against the product;\n3. outdated product description;\n4. competitor-only recommendation in a strategic query.\n\n## Outputs\n\n| Output | Description |\n|---|---|\n| Problem type | one of the four negative categories |\n| Repair actions | source fixes, content moves, authority pages, feedback actions |\n| Regression plan | repeat checks for the affected queries |\n\n## Next Best Skill\n\nIf the next task is to quality-check the newly repaired content, use `visibility-content-check`."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":null,"editorialQuality":{"score":100,"threshold":65,"status":"thin","wordCount":1590,"uniquenessScore":41,"reasons":["uniqueness-below-45"]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-10T04:55:45.290Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-10T04:55:45.290Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-10T10:48:30.052Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs","url":"https://github.com/CherryHQ/cherry-studio","homepage":"https://cherry-ai.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-11T14:38:40.986Z","createdAt":"2026-02-25T03:38:19.379Z","downloads":null},{"id":"6f6582d0-5d76-4f0f-b81d-86520247950b","entityType":"agent","canonicalPath":"/agent/copilotkit-copilotkit","slug":"copilotkit-copilotkit","name":"CopilotKit","description":"The Frontend for Agents & Generative UI. React + Angular","url":"https://github.com/CopilotKit/CopilotKit","homepage":"https://docs.copilotkit.ai","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-03-25T09:50:57.846Z","createdAt":"2026-02-25T03:39:14.617Z","downloads":null}],"links":{"hub":"/agent","source":"/agent/source/clawhub","protocols":[{"label":"OpenClaw","href":"/agent/protocol/openclew"}]}}}