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 agentmods add skills/caiaffa/claude-code-ultimate-engineering-system/incident-responsenpx skills add caiaffa/claude-code-ultimate-engineering-system --skill incident-responsegit clone --depth 1 https://github.com/caiaffa/claude-code-ultimate-engineering-systemWrote 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/caiaffa/claude-code-ultimate-engineering-system/incident-response)<a href="https://agentmods.dev/skills/caiaffa/claude-code-ultimate-engineering-system/incident-response"><img src="https://agentmods.dev/badge/skills/caiaffa/claude-code-ultimate-engineering-system/incident-response.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 | $0.00022 | $0.00612 |
| Opus 5 | $0.00011 | $0.00306 |
| Sonnet 5 | $0.00004 | $0.00122 |
| Haiku 4.5 | $0.00002 | $0.00061 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mission
Reduce user impact quickly, stabilize the system safely, and turn operational pain into learning and prevention.
When to use
- Production behavior degrades.
- Queue stalls or backlog grows.
- Latency or error rate spikes.
- Dependency fails or becomes slow.
- On-call response needs structure.
Handoff
- Receives from: CLAUDE.md orchestrator (incident category) or direct alert.
- Hands off to: deep-root-cause-investigator (after stabilization), then postmortem-reviewer + incident-learning-loop (after resolution).
Phase 1: Triage (first 5 minutes)
- What is the user impact? (none / degraded / partial outage / full outage)
- What is the blast radius? (one user / segment / all users / all services)
- When did it start? (correlate with deploys, config changes, traffic spikes)
- Is it getting worse? (check trend in last 15 min)
Phase 2: Contain (next 15 minutes)
- Can we rollback? → Do it. Don't debug first.
- Can we feature-flag it off? → Do it.
- Can we scale or rate-limit? → Do it.
- Can we redirect traffic? → Do it.
- Mitigation before diagnosis when users are impacted.
Phase 3: Investigate (after stabilization)
- List top 3 hypotheses ranked by likelihood.
- For each hypothesis, list the ONE signal that confirms or eliminates it.
- Check signals in order. Eliminate fast.
- When cause is found, verify with a second independent signal.
Phase 4: Resolve and learn
- Confirm user impact has ended.
- Document timeline: start → detect → contain → resolve.
- Identify contributing factors (not just the trigger).
- Schedule postmortem within 48 hours.
Red flags during incident
- "Let's monitor" without defining what signal and what threshold.
- Debugging deep code paths while users are still impacted.
- Multiple people making changes simultaneously without coordination.
- Assuming a restart fixed the root cause.
Output format
- Impact: who, how many, what functionality
- Severity: SEV1 / SEV2 / SEV3 / SEV4
- Timeline: when started, when detected, gap
- Immediate containment actions (prioritized)
- Hypotheses (ranked, with confirmation signals)
- Investigation plan (ordered steps)
- Contributing factors (not just the trigger)
- Long-term fixes (not just "add monitoring")
- Postmortem actions (owner + deadline)
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 · 61 lines · 22 tokens per session scan A 24eb78d24077
incident-response is a skill published in the GitHub repository caiaffa/claude-code-ultimate-engineering-system (17 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 612 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 2026-08-30.
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