Agent skill · Data & Analytics

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 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 f

aaron-he-zhu2,508★ · 1 repos on radarProfile →
claude-codeApache-2.0
Install
npx skills add aaron-he-zhu/aaron-marketing-skills --skill launch-retro-analyzer --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 13 KB
Bundled scripts: none
Version: 19.1.0
Requires: Claude Code and compatible agent-skill hosts
Path: launch/prove/launch-retro-analyzer/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,508
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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 only. It does **not** compute return math — CPA / ROI / payback is [roi-calculator](../../../influencer/report/roi-calculator/SKILL.md); does not write the stakeholder-facing report — that is [report-generator](../../../influencer/report/report-generator/SKILL.md)

What's inside
Steps it walks through
  1. Quick Start
  2. Skill Contract
  3. Handoff Summary
  4. Data Sources
  5. Instructions
  6. Save Results
  7. Reference Materials
  8. Next Best Skill
More from aaron-marketing-skills
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About this skill
What does the launch-retro-analyzer skill do?

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 f

How do I install it?

Run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill launch-retro-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From aaron-he-zhu/aaron-marketing-skills, a repository with 2,508 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going