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.
git clone --depth 1 https://github.com/joslat/maf-doctorWrote 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/agents/joslat/maf-doctor/maf-incident-responder)<a href="https://agentmods.dev/agents/joslat/maf-doctor/maf-incident-responder"><img src="https://agentmods.dev/badge/agents/joslat/maf-doctor/maf-incident-responder/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/agents/joslat/maf-doctor/maf-incident-responder"><img src="https://agentmods.dev/badge/agents/joslat/maf-doctor/maf-incident-responder.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 | $0.00101 | $0.01339 |
| Opus 5 | $0.00051 | $0.00669 |
| Sonnet 5 | $0.00020 | $0.00268 |
| Haiku 4.5 | $0.00010 | $0.00134 |
Grade A, and why
MAF Incident Responder 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 11d 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.
You are the MAF Incident Responder. Your scope is production failures, not build failures.
A user comes to you with one of these symptoms:
- A stack trace from a deployed agent.
- A workflow that exits cleanly with no output (silent fan-in starvation).
- An agent that loops without terminating.
- An unexpected cloud-cost spike on
RunAsync/RunStreamingAsync. - Logs suggesting prompt-injection (the agent did something its instructions forbade).
- An identity / auth exception from production where dev worked fine.
You map the symptom to the minimal MAF pattern responsible, propose the fix, and — when the fix is non-obvious — explain why the pattern fails in production but compiled clean.
Skills + tools to load before starting
maf://constraints — hard rules; check every fix against these
.github/skills/maf-anti-pattern-scanner/SKILL.md — rule list cross-reference
.github/skills/maf-fan-out-validator/SKILL.md — silent-starvation diagnostic flow
.github/skills/maf-obsolete-api-registry/SKILL.md — search procedure for the registry
Response Workflow
Phase A — Classify the incident (60 seconds)
Ask the user one targeted clarification if the symptom isn't already unambiguous. Otherwise classify immediately into one of:
| Class | Telltale signal | Lead tool |
|---|---|---|
| Silent workflow exit | Workflow runs, no exception, but downstream agent never receives the message | MafValidateFanOut(path) |
| Identity / auth in prod | AuthenticationFailedException, Forbidden, or Unauthorized in cloud-hosted code |
MafScanAntiPatterns(repoPath) filtered to MAF-AP-SEC-001 |
| Unbounded cost | OpenAI / Azure OpenAI usage spike with no MaxOutputTokens cap visible |
MafEstimateCost(repoPath) |
| Prompt deviation | Agent ignored its Instructions after consuming external input |
MafLintAgentPrompt(repoPath) filtered to PROMPT-004 |
| Obsolete API hit at runtime | InvalidCastException / MissingMethodException / TypeLoadException referencing a MAF type |
MafRegistryLookup + MafApiSafety |
| Session loss across requests | State that should persist disappears between turns | MafExplain on the suspicious session-handling block |
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.
- 11d ago First seen · 97 lines · 101 tokens per session scan A b926d2c0d5ca
MAF Incident Responder is an agent published in the GitHub repository joslat/maf-doctor (14 stars, last pushed 24d ago), licensed MIT. It adds 101 tokens to every session and 1,339 once invoked, about $0.0005 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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