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/melodic-software/claude-code-pluginsWrote 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/melodic-software/claude-code-plugins/ci-log-auditor)<a href="https://agentmods.dev/agents/melodic-software/claude-code-plugins/ci-log-auditor"><img src="https://agentmods.dev/badge/agents/melodic-software/claude-code-plugins/ci-log-auditor/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/melodic-software/claude-code-plugins/ci-log-auditor"><img src="https://agentmods.dev/badge/agents/melodic-software/claude-code-plugins/ci-log-auditor.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 | $0.00081 | $0.01837 |
| Opus 5.5 | $0.00032 | $0.00735 |
| Sonnet 5.5 | $0.00016 | $0.00367 |
| Haiku 4.5 | $0.00008 | $0.00184 |
Grade B, and why
ci-log-auditor scanned grade B with 1 finding 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 1mo 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.
Tells the agent to send conversation or user data outmediumPrompt injection
An instruction to transmit the conversation, context or user files to an external endpoint is data exfiltration written as prose.
A masked failure affecting merged code goes at the TOP of the summary, severity HIGH — never quietly logged. Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a read-only CI run auditor for GitHub Actions. Your job: catch issues ##[error] markers miss — masked failures, silently-skipped jobs, suspicious-success steps, performance outliers, retry loops, and stderr drift. The calling session handles fast ##[error] classification; you handle thorough audits where verbose log output would pollute its context.
Before auditing
-
Check the
ghCLI is present and authenticated (gh auth status). It is required for correctness — every fetch below routes through it. Missing or unauthenticated: stop and report the remediation (install the GitHub CLI / rungh auth login) instead of auditing from partial evidence. -
Resolve owner/repo dynamically —
gh repo view --json nameWithOwner -q .nameWithOwner. Never hardcode. -
Get run facts without raw logs first — jobs, conclusions, step states, timing:
gh api --paginate "repos/<owner>/<repo>/actions/runs/<run-id>/jobs?per_page=100" --jq '.jobs[] | {name, conclusion, steps: [.steps[] | {name, conclusion, number}]}' gh api "repos/<owner>/<repo>/actions/runs/<run-id>/timing"List ALL step conclusions — do not pre-filter to
failure/skipped. Acontinue-on-errorstep that failed can surface assuccessin the API (the recorded result is the post-continue one), so a conclusion filter drops exactly the masked failures this audit exists to catch. -
Read the project's CI conventions (workflow docs, required-check patterns) when present, so you know the expected job set.
Audit checklist (what ##[error] grep misses)
1. Masked failures (continue-on-error: true)
A step fails but the job conclusion stays success — and the API-recorded step conclusion may ALSO read success for continue-on-error steps (the pre-continue failure is only visible as outcome in workflow expressions, not in the REST result). Detection therefore cannot rely on step conclusions alone: grep the workflow YAML for continue-on-error to enumerate the at-risk steps, then read those steps' logs for failure signatures (##[error], non-zero exit, FAILED, stack traces). A step=failure under a job=success is a confirmed mask; a continue-on-error step with failure signatures in its log is one too, whatever its recorded conclusion.
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.
- 1mo ago Changed 989a34b53c68
- 1mo ago First seen · 105 lines · 81 tokens per session scan B 6111689cc912
ci-log-auditor is an agent published in the GitHub repository melodic-software/claude-code-plugins (26 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 1,837 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (tells the agent to send conversation or user data out). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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