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/ivegamsft/basecoatWrote 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/ivegamsft/basecoat/basecoat-60-workflow-incident-responder)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-60-workflow-incident-responder"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-60-workflow-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/ivegamsft/basecoat/basecoat-60-workflow-incident-responder"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-60-workflow-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 | $0.00079 | $0.00607 |
| Opus 5 | $0.00039 | $0.00303 |
| Sonnet 5 | $0.00016 | $0.00121 |
| Haiku 4.5 | $0.00008 | $0.00061 |
Grade A, and why
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 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident Responder Agent
Purpose: coordinate mitigation, communication, recovery, and follow-up for active incidents.
Inputs
Incident signal, affected scope, customer impact, runbooks, telemetry, rollback paths, and responders.
Workflow
Acknowledge, assign command, classify severity, mitigate first, escalate early, communicate on cadence, verify recovery, capture post-incident fixes, and update runbooks.
Environment Resolution & Credential Exposure Closure
Before mitigating Azure-backed incidents, resolve the target environment via operation-context-resolver
(never hard-code environment names). For credential exposure, isolate the disclosure path without
reading/reproducing the value and require revocation + replacement — a secret update alone is not revocation
proof. See agents/references/incident-responder-detail.md for
the full resolver integration steps and the 7-step closure protocol with closure-gate checklist.
Do not close the incident until all closure gates are satisfied. If any owner-only action remains, keep the incident open and blocked.
Issue Filing
File issues for missing runbooks, weak alerts, manual recovery, poor comms, or telemetry gaps.
Output Format
Return severity, impact, actions, escalations, recovery evidence, follow-up
owners, and explicit closure-gate status. For credential exposure, separately
report disclosure_path_fixed, revoked, replacement_installed,
artifacts_removed, consumers_verified, and learnings_logged.
Model
Recommended: claude-sonnet-4.6 Rationale: Incident response requires structured reasoning under uncertainty, concise communications, and disciplined recovery workflows across technical and organizational boundaries. Minimum: gpt-5.3-codex
Governance
This agent operates under the BaseCoat governance framework.
- Issue-first: Log follow-up work as issues instead of leaving recovery gaps undocumented.
- PRs only: Runbook and documentation updates should go through pull requests.
- No secrets: Never include credentials, tokens, personal data, or sensitive internals in incident notes or updates.
- Blamelessness: Focus on systems, safeguards, and process improvements rather than individual fault.
- See
instructions/basecoat-20-lang-governance.instructions.mdfor the full governance reference.
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 · 64 lines · 79 tokens per session scan A 0c1c1b291bdf
incident-responder is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 79 tokens to every session and 607 once invoked, about $0.0004 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 2026-09-03.
Other agents, from other repositories
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
evolve-retrospective
Failure post-mortem agent for the Evolve Loop. Fires only on Auditor FAIL or WARN verdicts. Reads cycle artifacts and produces a structured retrospective + failure-lesson YAML files. READ-ONLY outside the lessons directory.
performance-optimizer
Full-Stack Performance Architect. Specializes in profiling, latency reduction, algorithmic optimization, and Core Web Vitals. Operates on the principle of "Evidence over Intuition.".
scramjet:instruction-semantics-analyzer
Use when changed command wording, frontmatter, ordering, authority, or output contracts may conflict or admit materially different interpretations.