PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill agent-incident-postmortemgit clone --depth 1 https://github.com/mohitagw15856/pm-claude-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/mohitagw15856/pm-claude-skills/agent-incident-postmortem)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/agent-incident-postmortem"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-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/mohitagw15856/pm-claude-skills/agent-incident-postmortem"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-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 | $0.00120 | $0.01266 |
| Opus 5.5 | $0.00048 | $0.00506 |
| Sonnet 5.5 | $0.00024 | $0.00253 |
| Haiku 4.5 | $0.00012 | $0.00127 |
Grade A, and why
agent-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 2d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Incident Postmortem Skill
AI incidents differ from outages: the system didn't go down — it did something wrong, confidently, and maybe only once. This skill adapts blameless postmortem practice to nondeterministic systems, where "can we reproduce it?" needs traces, not just steps.
What This Skill Produces
- A blameless postmortem document with timeline and user/business impact
- A trace reconstruction of what the agent saw, decided, and did
- A root-cause analysis across the AI failure layers (not "the model hallucinated" as a conclusion)
- Corrective actions — always including a new permanent case in the regression suite
Required Inputs
Ask for (if not already provided):
- What the agent did and what it should have done
- The trace — the full request: system prompt, context, tool calls and results, output. If no trace exists, that absence is itself a finding
- Blast radius — how many users/requests, over what window, and whether it's ongoing
- Detection — how it was noticed (user report? monitor? luck?) and how long after it started
Root-Cause Layers
Walk the layers in order; the root cause is usually the earliest layer that could have prevented the outcome. "The model was wrong" is a starting point, never the conclusion — models are known to be fallible, so the question is what let a fallible output become an incident.
| Layer | Ask |
|---|---|
| Input / context | Was the context wrong, stale, contradictory, or poisoned (injection)? Did retrieval feed it bad ground truth? |
| Model behaviour | Given that context, was the output a foreseeable failure mode (fabrication under missing data, over-compliance with injected text)? |
| Guardrails | What check should have caught this output and didn't exist / didn't fire? (schema validation, groundedness check, action allow-list) |
| Action layer | Why could the wrong output become a real action or reach a user without the appropriate gate for its risk level? |
| Detection | Why did we learn about it this way, this late? What signal would have caught it in minutes? |
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.
- 2d ago First seen · 97 lines · 120 tokens per session scan A d10311599fae
agent-incident-postmortem is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 120 tokens to every session and 1,266 once invoked, about $0.0005 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 2026-10-12.
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