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-learning-loopnpx skills add caiaffa/claude-code-ultimate-engineering-system --skill incident-learning-loopgit clone --depth 1 https://github.com/caiaffa/claude-code-ultimate-engineering-systemWhat 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 | $0.00024 | $0.00472 |
| Opus 5 | $0.00012 | $0.00236 |
| Sonnet 5 | $0.00005 | $0.00094 |
| Haiku 4.5 | $0.00002 | $0.00047 |
Grade A, and why
incident-learning-loop 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 3d 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.
What it actually says
Mission
Ensure every serious incident improves the engineering system, not just the affected service.
When to use
- Closing a postmortem.
- Deciding what should become a standard.
- Updating templates, checklists, or runbooks after incidents.
- Propagating lessons beyond the local failure.
Handoff
- Receives from: postmortem-reviewer (after postmortem analysis) or deep-root-cause-investigator.
- Hands off to: principal-engineer (for standard updates), operational-excellence-enforcer (for runbook/alert updates).
The learning extraction process
For every incident, ask:
- What class of failure was this? (not "what happened" but "what category")
- Examples: missing idempotency, schema migration lock, stale cache read, missing circuit breaker
- What invariant was violated? (check against SYSTEM_INVARIANTS.md)
- What standard should change? (PROJECT_CONVENTIONS.md, DECISION_RULES.md, etc.)
- What template or checklist should be updated? (SERVICE_SCORECARD.md, DEFINITION_OF_DONE.md, etc.)
- What other services have this same exposure? (lateral scan)
- What review requirement should be added? (new item in code review, ADR review, etc.)
Red flags — learning is too shallow if
- Lesson stays local to one team or service.
- Fix applied to code but no standard or process changes.
- Runbook improved but the design that caused the incident is unchanged.
- Same class of failure is still possible in 3 other services.
- Action items are all "add monitoring" without prevention.
Output format
- Class of failure (categorized)
- Systemic lesson (what the org should learn)
- Standards to update (specific files and changes)
- Templates/checklists to add or modify (specific)
- Other exposed services (lateral risk scan)
- Wider rollout plan (how to propagate the fix beyond this service)
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.
- 3d ago First seen · 44 lines · 24 tokens per session scan A 715be53ef3bf
incident-learning-loop is a skill published in the GitHub repository caiaffa/claude-code-ultimate-engineering-system (17 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 472 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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