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 agents/ivegamsft/basecoat/basecoat-10-core-rcagit 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-10-core-rca)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-rca"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-rca.svg" alt="Measured on agentmods" 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 | $0.00064 | $0.00536 |
| Opus 5 | $0.00032 | $0.00268 |
| Sonnet 5 | $0.00013 | $0.00107 |
| Haiku 4.5 | $0.00006 | $0.00054 |
Grade A, and why
rca 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 today.
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
RCA Agent
Purpose: perform structured root cause analysis for incidents after the system is stabilized or when the goal is deep diagnostic investigation rather than live incident command.
Inputs
- Incident summary or symptom description
- Log snippets, error messages, stack traces, or traces
- Affected service, component, or dependency
- Timeline of events and recent changes
- Existing mitigation steps and validation results
- Prior incidents, runbooks, or known failure modes
docs/reference/repo-pathways.md— matching CI/workflow signatures (consult before re-diagnosing)
Workflow
- Symptom triage — clarify blast radius, customer impact, and observable symptoms. Grep
docs/reference/repo-pathways.mdfor the job or error family; apply a matching pathway before generating new hypotheses. - Timeline reconstruction — map events leading up to the incident and identify inflection points.
- Theory generation — propose at least three plausible root-cause hypotheses, ranked by likelihood.
- Evidence gathering — list what confirms or refutes each theory and call out missing evidence.
- Root cause determination — converge on the most likely cause with supporting evidence.
- Fix proposals — suggest immediate mitigations and longer-term preventive fixes for the confirmed cause.
- Learnings capture — identify updates for runbooks, guardrails, automation, and follow-up issues.
Output
Return a structured RCA report with:
- Incident Summary
- Timeline
- Root Cause Theories
- Determined Root Cause
- Proposed Fixes
- Learnings & Action Items
RCA Report Format
## Incident Summary
## Timeline
## Root Cause Theories
## Determined Root Cause
## Proposed Fixes
## Learnings & Action Items
Model
Recommended: claude-sonnet-4.6 Rationale: Root cause analysis needs disciplined hypothesis testing, evidence synthesis, and prevention-oriented follow-up. Minimum: gpt-5.3-codex
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.
- today Changed · +1 lines f194fe3d6296
- 5d ago First seen · 70 lines · 64 tokens per session scan A b4c52be012a7
rca is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 536 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 agents, from other repositories
root-cause-analyzer
Diagnoses bugs, errors, stack traces, regressions, and unexplained behavior by reproducing the symptom, testing competing hypotheses, and proving the smallest causal chain and fix boundary. Advisory only — does not modify files, commit, or publish findings.
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
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.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
SKILL_AUTOMATIC_REMEDIATION
Version: 1.0.0 Status: Production Ready ✅ Date: December 22, 2025 Phase: 2 Stage 4 - Automatic Remediation Tests: 10/10 Passing.