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 makifbaysal/tasktrooper --skill incident-responsegit clone --depth 1 https://github.com/makifbaysal/tasktrooperWrote 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/makifbaysal/tasktrooper/incident-response)<a href="https://agentmods.dev/skills/makifbaysal/tasktrooper/incident-response"><img src="https://agentmods.dev/badge/skills/makifbaysal/tasktrooper/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/makifbaysal/tasktrooper/incident-response"><img src="https://agentmods.dev/badge/skills/makifbaysal/tasktrooper/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 | $0.00037 | $0.00722 |
| Opus 5.5 | $0.00015 | $0.00289 |
| Sonnet 5.5 | $0.00007 | $0.00144 |
| Haiku 4.5 | $0.00004 | $0.00072 |
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 8d 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 — 26 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident response
A task titled Incident: … is a live production problem, assigned to you by the system. Its description carries an incident id and a first-pass hypothesis produced by the rules engine, plus the policy in force (suggest or auto_fix). That hypothesis is a starting point, not a verdict.
Order of work
- Read the incident first.
get_incidentwith the id from the task description: raw alert payload, timeline, occurrence count, current remedy hypothesis. - Correlate with the tools you hold.
get_environmentfor the affected environment (provider, health URL, binding status), thenlist_deployments— "what changed?" answers most incidents.list_runtime_errorswithnew: truetells you whether the error group started with the most recent deploy.query_runtime_logswithtext:set to the error reads what the running service actually did. - Classify. Config/credentials, dependency, capacity, or code defect. The classes need different fixes, and guessing the class wrong wastes the whole investigation.
- Write the remedy.
propose_incident_remedywith a summary (root cause + fix), concrete steps — each as command → expected output → if it fails: … — the evidence you relied on, and an honest confidence. Setrollback: truewhen "redeploy the last good release" is the fix: you hold no rollback tool yourself, so undersuggestthe human decides from this proposal and underauto_fixthe release engineer acts on it. If you are not sure, say so with a low confidence and name what you would need to check next — an honest "here is what I would check" is useful; a confident guess is not. - Act per policy.
- suggest (default): do NOT change production code. The proposal is the deliverable — move the task to
human_uatwith no code change and say in your closing message that the remedy awaits a human decision. - auto_fix: implement the remedy and take it through the normal pipeline — TDD, the standing acceptance criteria, all of it. A hotfix that skips them causes the next incident.
- suggest (default): do NOT change production code. The proposal is the deliverable — move the task to
- Resolve only after verifying recovery.
resolve_incidentonly afterget_environment/list_runtime_errorsshow the environment is actually healthy again, with the note naming that evidence. Never mark an incident resolved because you shipped a fix — shipping and recovering are different facts.
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.
- 8d ago First seen · 26 lines · 37 tokens per session scan A 1dd229beeafc
incident-response is a skill published in the GitHub repository makifbaysal/tasktrooper (113 stars, last pushed yesterday), licensed Apache-2.0. It adds 37 tokens to every session and 722 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-05.
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