What makes Launch Retro Analyzer notable?

1.1K downloads. Trust evidence available

How should Launch Retro Analyzer be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Launch Retro Analyzer

Use when the user asks to "run a launch retro / post-mortem", "compare launch results vs targets by channel", or "decide what to keep or kill for the next la... Skill: Launch Retro Analyzer Owner: aaron-he-zhu Summary: Use when the user asks to "run a launch retro / post-mortem", "compare launch results vs targets by channel", or "decide what to keep or kill for the next la... Tags: latest:19.0.0 Version history: v19.0.0 | 2026-07-24T14:44:38.748Z | auto **Launch Retro Analyzer 19.0.0** - Updated SKILL.md for version 19.0.0, including versioning and metadata. - Added distrib

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

19.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 19.0.0release · observed Jul 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17e1tg8pjra8dn1dvtq21sahx83hrxj:launch-retro-analyzer

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-aaron-he-zhu-launch-retro-analyzer/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

84,024 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: launch-retro-analyzer
slug: aaron-launch-retro-analyzer
displayName: "Launch Retro Analyzer · 发布复盘"
summary: "发布复盘/渠道归因/5-Whys/keep-kill"
description: 'Use when the user asks to "run a launch retro / post-mortem", "compare launch results vs targets by channel", or "decide what to keep or kill for the next launch"; produces a structured D1/W1/M1 retrospective — a per-channel actual-vs-target table (UTM-attributed own analytics as the truth column, platform self-reported numbers as reference, every figure labeled Measured / User-provided / Estimated), a 5-Whys chain on the single largest miss, keep / kill / change decisions per channel, 3-5 actionable learnings for the next launch, and an outcome snapshot submitted to the launch registry. Not for return math (CPA / ROI) — use roi-calculator; not for the stakeholder-facing report writeup — use report-generator; not for a metric deep-dive — use performance-analyzer. 发布复盘/渠道归因/5-Whys/keep-kill'
version: "19.0.0"
license: Apache-2.0
compatibility: "Claude Code and compatible agent-skill hosts"
homepage: "https://github.com/aaron-he-zhu/aaron-marketing-skills"
when_to_use: "Use when a launch has shipped and needs a structured D1/W1/M1 retrospective: comparing per-channel actuals against pre-declared targets with UTM-attributed own analytics as the truth set, running a 5-Whys on the single largest miss, making keep/kill/change calls per channel, drafting 3-5 learnings for the next launch, and submitting the outcome snapshot to the launch registry. The retro layer downstream of launch-monitor tracking; return math stays with roi-calculator and the stakeholder writeup with report-generator."
argument-hint: "<launch / product> [window: D1|W1|M1] [targets] [analytics export]"
metadata: {"author": "aaron-he-zhu", "version": "19.0.0", "discipline": "launch", "phase": "prove", "geo-relevance": "low", "hermes": {"tags": ["marketing", "launch", "prove"], "category": "launch"}, "openclaw": {"emoji": "🚀", "homepage": "https://github.com/aaron-he-zhu/aaron-marketing-skills"}}
---

# Launch Retro Analyzer

Runs the structured D1/W1/M1 retrospective after a launch: the per-channel actual-vs-target read, the 5-Whys on the single largest miss, the keep / kill / change call per channel, and the 3-5 learnings that change the next launch. It sits in the **Prove** phase of the RAMP loop (Research → Assemble → Mobilize → Prove) and feeds the RAMP `P` retro sub-items — retro completed (channel actual-vs-target, 5-Whys on misses, keep/kill) and learnings promoted to memory + the launch-registry outcome snapshot — plus the `P` attribution discipline that own UTM-attributed analytics, not platform self-reported numbers, are the truth column. See [ramp-benchmark.md](../../../references/ramp-benchmark.md).

Only [launch-readiness-auditor](../../mobilize/launch-readiness-auditor/SKILL.md) runs a typed lifecycle RAMP profile; this skill owns the retro evidence and hands off.

**Scope guard**: this skill runs the retro onl

_meta.json

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skill-card.md

## Description:

Launch Retro Analyzer helps agents run D1/W1/M1 launch retrospectives by comparing channel actuals to targets, analyzing the largest miss with 5-Whys, making keep/kill/change decisions, drafting learnings, and preparing a launch-registry outcome snapshot.

This skill is ready for commercial/non-commercial use.

## Publisher:

[aaron-he-zhu](https://clawhub.ai/user/aaron-he-zhu)

### License/Terms of Use:

MIT-0

## Use Case:

Marketing, growth, and launch teams use this skill after a launch to turn targets, analytics exports, and platform dashboard numbers into a structured retrospective with channel decisions and next-launch learnings.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Analytics exports, dashboard screenshots, or pasted launch data may include sensitive or untrusted content.

Mitigation: Only provide analytics exports suitable for this workflow, treat external data as untrusted input, and label every figure as Measured, User-provided, or Estimated.

Risk: Retrospective summaries and outcome snapshots may persist launch analytics or decisions.

Mitigation: Require user confirmation before saving results and review proposed saved content before confirming persistence.

Risk: Platform self-reported metrics can be mistaken for the authoritative launch result.

Mitigation: Keep platform-reported numbers in a reference column and use UTM-attributed own analytics as the truth column.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/aaron-he-zhu/skills/launch-retro-analyzer)
- [Project homepage](https://github.com/aaron-he-zhu/aaron-marketing-skills)

## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown retrospective with tables, a 5-Whys narrative, keep/kill/change decisions, learnings, and a reviewed persistence proposal]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Every figure is labeled Measured, User-provided, or Estimated; UTM-attributed own analytics are treated as the truth column and platform-reported numbers as reference.]

## Skill Version(s):

19.0.0 (source: SKILL.md frontmatter and server release evidence)

## Ethical Considerations:

Users 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.

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