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 codebygarv/Ai-skills --skill pre-mortem-facilitatorgit clone --depth 1 https://github.com/codebygarv/Ai-skillsWrote 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/codebygarv/ai-skills/pre-mortem-facilitator)<a href="https://agentmods.dev/skills/codebygarv/ai-skills/pre-mortem-facilitator"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/pre-mortem-facilitator.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00347 |
| Opus 5 | $0.00014 | $0.00173 |
| Sonnet 5 | $0.00006 | $0.00069 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
pre-mortem-facilitator 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 5d 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.
What it actually says
Purpose
Uncover hidden risks before launch by assuming the project has completely failed 6 months in the future, then working backward to diagnose why.
When to Use
- Prior to kicking off a major migration, rewrite, or product launch.
- When a plan feels "too smooth" and team members are hesitant to voice doubts.
- Before committing to a major third-party dependency or cloud vendor.
What to Analyze
- The Disaster Scenario: Assume it is 6 months post-launch and the project was rolled back or caused massive downtime.
- Failure Vectors:
- Technical (data loss, cascading latency, schema lockups).
- Operational (on-call burnout, missing runbooks, metric blindness).
- Organizational (team dependencies, key-person risk, unclear ownership).
- Adoption (users rejected the new UX, edge cases broke integrations).
- Early Warning Indicators: Leading indicators that signal failure is beginning.
- Preventative Controls: Interventions to implement immediately.
Output Format
- Failure Scenario Narrative: A vivid 2-paragraph retrospective on what went wrong.
- Top 5 Plausible Failure Vectors: Categorized with severity and likelihood.
- Leading Indicators: Metric anomalies that warn of impending failure.
- Pre-Launch Remediation Actions: Concrete checklist before green-lighting launch.
Avoid
- Mild, trivial issues (focus on catastrophic, project-killing risks).
- Vague mitigations like "improve monitoring".
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 38 lines · 28 tokens per session scan A 1681ccf51cfb
pre-mortem-facilitator is a skill published in the GitHub repository codebygarv/Ai-skills (25 stars, last pushed 19d ago), licensed MIT. It adds 28 tokens to every session and 347 once invoked, about $0.0001 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 2026-09-03.
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