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 JayRHa/AgentSkills --skill incident-postmortemgit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/jayrha/agentskills/incident-postmortem)<a href="https://agentmods.dev/skills/jayrha/agentskills/incident-postmortem"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/incident-postmortem/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/jayrha/agentskills/incident-postmortem"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/incident-postmortem.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 | $0.00108 | $0.01775 |
| Opus 5.5 | $0.00043 | $0.00710 |
| Sonnet 5.5 | $0.00022 | $0.00355 |
| Haiku 4.5 | $0.00011 | $0.00178 |
Grade A, and why
incident-postmortem 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident Postmortem
Overview
Keywords: postmortem, post-mortem, RCA, root cause analysis, blameless, incident review, outage retro, SEV1, SEV2, contributing factors, action items, five whys, timeline, MTTR, COE, incident report, learning review.
This skill turns a resolved incident into a durable learning artifact. It enforces a blameless stance (focus on systems and conditions, never individuals), drives toward systemic root causes (not just the proximate trigger), and converts findings into specific, owned, dated action items with verification. The output is a single postmortem document plus a list of trackable follow-ups.
A good postmortem answers five questions: What happened? What was the impact? Why did it happen? How was it detected and resolved? How do we prevent recurrence (and reduce time-to-detect/time-to-resolve next time)?
Use templates/postmortem.md as the document skeleton, references/root-cause-techniques.md for the analysis methods, references/blameless-language.md to rewrite blame into systems language, and scripts/timeline_builder.py to assemble a clean timeline and compute incident metrics.
Workflow
-
Gather the raw record. Collect the incident channel/war-room transcript, alert timestamps, deploy and config-change logs, dashboards/graphs, the first customer report, and the resolution moment. Note the SEV/severity level and the systems affected. Do not start writing prose yet — collect facts.
-
Build the timeline. Convert every relevant event into a
timestamp | actor/system | eventrow. Use UTC and ISO-8601. Runscripts/timeline_builder.pyto sort events, normalize timestamps, and auto-derive the key metrics (time-to-detect, time-to-mitigate, time-to-resolve, total duration). The timeline is the spine of the document — get it right before reasoning about causes. -
Establish impact. Quantify in user/business terms: affected users or %, requests failed, revenue/SLA/error-budget burned, duration of degradation. Vague impact ("some users affected") undermines prioritization — push for numbers or explicit estimates with their basis.
What ships with it
7 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.
- 1mo ago First seen · 75 lines · 108 tokens per session scan A 0beeadcf1460
incident-postmortem is a skill published in the GitHub repository JayRHa/AgentSkills (12 stars, last pushed 2mo ago), licensed MIT. It adds 108 tokens to every session and 1,775 once invoked, about $0.0004 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 2026-08-31.
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