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 cdeust/zetetic-team-subagents --skill failure-forensicsgit clone --depth 1 https://github.com/cdeust/zetetic-team-subagentsWrote 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/cdeust/zetetic-team-subagents/failure-forensics)<a href="https://agentmods.dev/skills/cdeust/zetetic-team-subagents/failure-forensics"><img src="https://agentmods.dev/badge/skills/cdeust/zetetic-team-subagents/failure-forensics/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/cdeust/zetetic-team-subagents/failure-forensics"><img src="https://agentmods.dev/badge/skills/cdeust/zetetic-team-subagents/failure-forensics.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 | $0.00065 | $0.00659 |
| Opus 5 | $0.00032 | $0.00329 |
| Sonnet 5 | $0.00013 | $0.00132 |
| Haiku 4.5 | $0.00006 | $0.00066 |
Grade A, and why
failure-forensics 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 9d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Failure Forensics
Problem shape: something failed (or will fail) and the evidence is being averaged away, cleaned up, or explained by its most convenient story. The move: treat anomalies as data, reconstruct the timeline at the right timescale, and design the degraded state as a first-class behavior.
Relevant geniuses
| Agent | Use when |
|---|---|
| hamilton | overload and simultaneous failure scenarios; priority shedding by criticality; the degraded mode is undesigned; "users will never do that" |
| mcclintock | aggregate metrics look smooth but one specific case is weird; a class of observations is being trimmed as noise |
| fleming | anomalies keep appearing during routine work and being cleaned up; "that's weird" said and never investigated |
| ginzburg | reconstruct what happened from marginal traces and involuntary evidence (logs, timestamps, artifacts nobody meant to leave) |
| braudel | the incident needs three-timescale decomposition — event, cycle, structure — before a root cause is named |
| wu | the failure hides under an assumption everyone considered too obvious to test |
| maxwell | the system oscillates, overshoots, or hunts — feedback stability and gain-margin diagnosis |
Invocation
- Pick the best-fit agent above. If two or more fit, run
tools/genius-invoker.sh route "<problem>"and take the top ranked match. - Load it:
tools/genius-invoker.sh invoke <agent> "<problem>", then readagents/genius/<agent>.mdin full. - Apply the agent's
<workflow>step by step and answer in its<output-format>. Reproduce before claiming a cause; classify the cause before proposing the fix (coding-standards §6 root-cause protocol). - Typical chain: ginzburg reconstructs the timeline → braudel separates
timescales → hamilton designs the degraded state. Run via
tools/genius-invoker.sh compose ginzburg hamilton -- "<problem>". - If no shape above matches, use a standard team agent instead.
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.
- 9d ago First seen · 49 lines · 65 tokens per session scan A f96702f59fb2
failure-forensics is a skill published in the GitHub repository cdeust/zetetic-team-subagents (7 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 659 once invoked, about $0.0003 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.
Other skills, from other repositories
release-it
Build production-ready systems with stability patterns: circuit breakers, bulkheads, timeouts, and retry logic. Use when the user mentions "production outage", "circuit breaker", "deployment pipeline", "chaos engineering", "retry storm", "health checks", "my service keeps crashing", "prevent cascading failures", or…
tidewave-integration
Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.
debug
Interactive debugging workflow with hypothesis-driven probe loop. Use when: unknown bugs, script errors, silent failures, troubleshooting. Not for: known bugs (use bug-fix), GitHub issue analysis (use issue-analyze), code understanding (use code-explore). Output: debug report with probe journal + root cause + fix.
git-investigate
Git history investigation. Use when: tracking code changes, finding where bugs were introduced, root cause analysis. Not for: code exploration (use code-explore), issue analysis (use issue-analyze). Output: history trace + root cause report.
analyze
Deep-dive codebase analysis that explains how things actually work — business rules, architecture patterns, auth flows, data models, integrations, and performance hotspots. Use whenever the user asks "how does X work", "map the Y flow", "what are the business rules for Z", "trace the auth path", "explore the codebase…
simplify
Wrap-up refactoring — simplify code, eliminate duplication, preserve behavior.