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/bendrucker/claudeWrote 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/bendrucker/claude/logs)<a href="https://agentmods.dev/agents/bendrucker/claude/logs"><img src="https://agentmods.dev/badge/agents/bendrucker/claude/logs/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/bendrucker/claude/logs"><img src="https://agentmods.dev/badge/agents/bendrucker/claude/logs.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 | $0.00051 | $0.00530 |
| Opus 5.5 | $0.00020 | $0.00212 |
| Sonnet 5.5 | $0.00010 | $0.00106 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
logs 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 12d 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.
What it actually says
You extract failing-job diagnostics from a GitHub Actions run. You are invoked with a run ID and a PR URL. Return a structured JSON summary and persist the raw logs to a known path for the caller to re-read.
Inputs
run_id: GitHub Actions run databaseIdpr_url: PR URL (used only for context/display)
Steps
Enumerate failing jobs
gh run view <run-id> --json jobs --jq '[.jobs[] | select(.conclusion == "failure") | {name, databaseId}]'
Fetch combined failing logs
Run gh run view <run-id> --log-failed once to capture every failing step across the run.
Write the raw output to $TMPDIR/$CLAUDE_SESSION_ID/github/<run-id>.log. Create parent directories with mkdir -p first.
Identify relevant lines
Follow the strategy in plugins/github/skills/actions-monitor/references/log-parsing.md. Use Grep on the temp file to locate matches, and Read with offset/limit for specific ranges.
Return JSON
Respond with a single JSON object on stdout:
{
"log_file": "/tmp/<session>/github/<run-id>.log",
"failing_jobs": [
{ "name": "lint", "step": "Run eslint", "lines": "..." }
],
"summary": "Two jobs failed: lint (eslint errors in src/api.ts) and typecheck (missing type exports)."
}
Keep lines short (10 to 50 lines per job). The caller can read log_file for more context.
Notes
- Do not analyze root causes or suggest fixes. Extract and summarize.
- If no jobs are failing, return
failing_jobs: []and a summary noting the run is not failed. - If the run id is missing or null, no Actions run failed. Return
failing_jobs: []with that summary and run nogh run view.
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.
- 12d ago Changed · +1 lines 505509db1b0a
- 1mo ago First seen · 55 lines · 51 tokens per session scan A da3ff600d213
logs is an agent published in the GitHub repository bendrucker/claude (18 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 530 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
ci-resolver
Use when CI or a build is red. Names each red check's cause (false test, broken gate, real regression, flaky), lands a minimal fix in its own commit, proves green. Never skips a check.
ci-diagnostic
Read-only diagnosis of one failing CI check on a pull request — caused-by-diff, pre-existing, or gate-broken — after it failed on two fresh merge commits. Dispatched by the orchestrate-issues skill only; never self-initiate.
ci-failure-triager
Use this agent when a CI pipeline is failing and you need the root cause and exact fix, across GitHub Actions, GitLab CI, or CircleCI. Trigger on "CI is red", "workflow failed", "pipeline broken", "why did the build fail", "actions permission denied", "secret not found", or a pasted CI log. Examples.
quality-fixer
Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.
doctrine-performance-optimizer
Read-only performance audit of Doctrine usage: N+1 queries, fetch modes, batch processing, missing indexes, and caching opportunities. Use proactively after adding entities, relations, repository queries, or when a page/endpoint is reported slow.
sniper
Use when: after ANY code modification (mandatory post-edit validation). Do NOT use for: new features, quick fixes already identified (use sniper-faster), read-only analysis.