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 sigistry/marketplace --skill incident-responsegit clone --depth 1 https://github.com/sigistry/marketplaceWrote 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/sigistry/marketplace/incident-response)<a href="https://agentmods.dev/skills/sigistry/marketplace/incident-response"><img src="https://agentmods.dev/badge/skills/sigistry/marketplace/incident-response/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/sigistry/marketplace/incident-response"><img src="https://agentmods.dev/badge/skills/sigistry/marketplace/incident-response.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 | $0.00078 | $0.00709 |
| Opus 5 | $0.00039 | $0.00354 |
| Sonnet 5 | $0.00016 | $0.00142 |
| Haiku 4.5 | $0.00008 | $0.00071 |
Grade A, and why
incident-response 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 12d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident Response and Blameless Postmortems
Purpose
A consistent, blameless methodology for writing incident postmortems and classifying severity, so writeups drive systemic fixes instead of finger-pointing. This is incident-management domain knowledge (structure, blameless framing, severity, metrics), applied uniformly every time.
The blameless principle
Analyze systems and processes, never individuals. People act rationally given the information and tools they had; if a human error caused harm, the real finding is the missing guardrail that let it. This is not about avoiding accountability, it's that blame suppresses the honest disclosure you need to actually fix the system.
Reframe every human-error statement:
| Blameful (reject) | Blameless (use) |
|---|---|
| "X pushed a bad config" | "the change reached prod because no test covered this path" |
| "on-call was slow" | "the alert didn't page; ack took N minutes" |
| "someone ran the wrong command" | "the safe and destructive commands were indistinguishable" |
Postmortem structure (standard sections)
- Summary: 2-3 sentences: what broke, blast radius, duration.
- Impact: users affected, duration, severity, SLO/error-budget burn.
- Timeline (UTC): detection → mitigation → resolution, each entry evidence-cited.
- Root Cause / Contributing Factors: multiple systemic factors, not one scapegoat.
- What went well / what went poorly: specific and honest.
- Action items: owner + due date + type (preventive / detective).
- Lessons learned: durable takeaways.
Timeline discipline
- Normalize all timestamps to UTC; note any timezone assumption.
- Mark the phase transitions explicitly: incident begin, detection, escalation, mitigation, resolution.
- Cite a source for every entry (alert, log line, chat timestamp, deploy record). Never invent a time, mark gaps
[unknown, needs follow-up].
Action item quality
Each item: maps to a contributing factor, has an owner (role/team) and a due date, and is classified:
- Preventive: removes the failure mode (test, guardrail, config default).
- Detective: catches it faster next time (alert, dashboard, probe). Avoid vague items ("be more careful"); every item must be verifiable as done.
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.
- 12d ago First seen · 48 lines · 78 tokens per session scan A 7a59311b7edf
incident-response is a skill published in the GitHub repository sigistry/marketplace (3 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 709 once invoked, about $0.0004 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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