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 caipe-io/ai-platform-engineering --skill incident-postmortem-reportgit clone --depth 1 https://github.com/caipe-io/ai-platform-engineeringWrote 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/caipe-io/ai-platform-engineering/incident-postmortem-report)<a href="https://agentmods.dev/skills/caipe-io/ai-platform-engineering/incident-postmortem-report"><img src="https://agentmods.dev/badge/skills/caipe-io/ai-platform-engineering/incident-postmortem-report/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/caipe-io/ai-platform-engineering/incident-postmortem-report"><img src="https://agentmods.dev/badge/skills/caipe-io/ai-platform-engineering/incident-postmortem-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00076 | $0.00906 |
| Opus 5.5 | $0.00030 | $0.00362 |
| Sonnet 5.5 | $0.00015 | $0.00181 |
| Haiku 4.5 | $0.00008 | $0.00091 |
Grade A, and why
incident-postmortem-report 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 1mo 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident Post-Mortem Report
Guide the user through producing a blameless, audit-ready post-mortem suitable for engineering leadership, compliance, and future incident prevention.
Instructions
Phase 1: Scope and audience
- Confirm what incident is in scope (ticket ID, time window, service, or free-text summary).
- Identify audience (internal engineering only vs. includes executives or customers) and adjust depth of business impact language.
- List facts already known vs. gaps that need research (logs, metrics, deploys, comms).
Phase 2: Structure the report
Use the sections below in order unless the organization mandates a different template. Fill each section with concrete data; avoid vague statements.
- Executive summary — 2–4 sentences: what broke, who was affected, how long, current status.
- Impact — Quantify: duration, error rates, revenue/users affected if known, SLA breach yes/no.
- Timeline — UTC timestamps, short event labels. Include detection, escalation, mitigation, full recovery.
- Root cause — Single primary cause, explained clearly. Use 5 Whys or equivalent if helpful.
- Contributing factors — Environment, process, tooling, or communication issues that amplified impact (not blame).
- What went well — Detection, runbooks, teamwork, rollback, comms.
- What went wrong — Gaps in monitoring, deploy process, testing, on-call routing, documentation.
- Corrective actions — Short-term fixes with owners and dates.
- Preventive actions — Longer-term hardening (tests, SLOs, chaos, capacity).
- Lessons learned — 2–5 bullet takeaways for the org.
- References — Links to incidents, PRs, dashboards, chat threads (no secrets).
Phase 3: Tone and quality bar
- Blameless: describe systems and processes, not individuals.
- Specific: numbers, tool names, versions, ticket keys.
- Actionable: every action item has an owner and a target date when possible.
Output Format
What ships with it
1 file 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.
- 1mo ago First seen · 100 lines · 76 tokens per session scan A 376a8b95a474
incident-postmortem-report is a skill published in the GitHub repository caipe-io/ai-platform-engineering (429 stars, last pushed today), licensed Apache-2.0. It adds 76 tokens to every session and 906 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
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