Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add varunk130/ai-gtm-skill-library --skill launch-debriefgit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/launch-debrief)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/launch-debrief"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/launch-debrief/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/launch-debrief"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/launch-debrief.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.02785 |
| Opus 5 | $0.00022 | $0.01392 |
| Sonnet 5 | $0.00009 | $0.00557 |
| Haiku 4.5 | $0.00004 | $0.00279 |
Grade A, and why
launch-debrief scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Launch Debrief (MIRROR Protocol)
A structured post-launch retrospective engine that transforms raw launch data into quantified learnings, root-cause analyses, and improvement playbooks. MIRROR ensures every launch makes future launches better by extracting actionable insights from both successes and failures through systematic analysis rather than anecdotal recall.
When to Use
- Conducting a post-launch retrospective (ideally at T+30 and T+90)
- Analyzing why a launch over- or underperformed expectations
- Building an institutional knowledge base of launch learnings
- Creating improvement playbooks for the next launch cycle
- Presenting launch results to leadership with root-cause analysis
- Comparing actual results against pre-launch projections
- Identifying systemic issues across multiple launches
What You'll Need
Critical inputs (ask if not provided):
- Launch name, date, and type (GA, beta, feature, expansion)
- Pre-launch targets for all VITAL metrics (from launch-pulse)
- Actual performance data for all tracked metrics
- Launch readiness scores from gate reviews (from launch-command)
- Budget allocation and actual spend (from budget-allocator)
- Channel performance data by channel (from demand-engine)
Nice-to-have:
- Customer feedback (NPS, surveys, support tickets, social mentions)
- Internal team feedback (retro notes, Slack threads, post-mortems)
- Competitive activity during launch window (from battle-scanner)
- Sales feedback on messaging and enablement effectiveness
- Win/loss analysis data from CRM
- Previous launch debrief reports for trend analysis
Process
Step 1: Metrics Review -- Actual vs Target vs Baseline
For each VITAL metric, calculate the Performance Index and classify the result.
Performance Index Table:
| VITAL Layer | Metric | Baseline | Target | Actual | Perf. Index | Classification |
|---|---|---|---|---|---|---|
| Volume | Website Traffic | Actual/Target | ||||
| Volume | Impressions | |||||
| Volume | Social Reach | |||||
| Intent | MQLs | |||||
| Intent | Demo Requests | |||||
| Intent | Trial Signups | |||||
| Traction | SQLs | |||||
| Traction | Pipeline Created | |||||
| Traction | Win Rate | |||||
| Adoption | Activation Rate | |||||
| Adoption | Time to Value | |||||
| Adoption | DAU/WAU | |||||
| Loyalty | NPS | |||||
| Loyalty | 30-Day Retention | |||||
| Loyalty | Referral Rate |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 259 lines · 43 tokens per session scan A f3a48732bb80
launch-debrief is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (6 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 2,785 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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