| name | launch-retro-analyzer |
| slug | aaron-launch-retro-analyzer |
| displayName | Launch Retro Analyzer ยท ๅๅธๅค็ |
| summary | ๅๅธๅค็/ๆธ ้ๅฝๅ /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 โ 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) โ use roi-calculator; not for the stakeholder-facing report writeup โ use report-generator; not for a metric deep-dive โ use performance-analyzer. ๅๅธๅค็/ๆธ ้ๅฝๅ /5-Whys/keep-kill |
| version | 19.2.0 |
| 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":"19.2.0","discipline":"launch","phase":"prove","geo-relevance":"low","hermes":{"tags":["marketing","launch","prove"],"category":"launch"},"openclaw":{"emoji":"๐","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 โ Assemble โ Mobilize โ Prove) and feeds the RAMP P retro sub-items โ retro completed (channel actual-vs-target, 5-Whys on misses, keep/kill) and learnings promoted to memory + the launch-registry outcome snapshot โ plus the P attribution discipline that own UTM-attributed analytics, not platform self-reported numbers, are the truth column. See ramp-benchmark.md.
Only launch-readiness-auditor 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 โ CPA / ROI / payback is roi-calculator; does not write the stakeholder-facing report โ that is report-generator; does not run metric deep-dives or anomaly analysis โ that is performance-analyzer; does not track the live T-0โT+30 window (launch-monitor) or triage feedback (launch-feedback-synthesizer); and it never writes memory/launch-registry/ records directly โ launch-registry is the sole writer; this skill submits the outcome snapshot to memory/events/launches.ndjson via an authorized operation: propose request to registry-events.py only.
Quick Start
Run a W1 retro on our [product] launch. Targets: [D0/W1 KPIs]. Here is the GA4 UTM export and the platform dashboards.
Our biggest miss was [channel / KPI]. Walk the 5-Whys and tell me what to keep, kill, or change for the next launch.
Close out the [product] launch: build the actual-vs-target table, log the learnings, and submit the outcome snapshot to the launch registry.
