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 paultyng/skill-issue --skill ci-debug-loopgit clone --depth 1 https://github.com/paultyng/skill-issueWrote 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/paultyng/skill-issue/ci-debug-loop)<a href="https://agentmods.dev/skills/paultyng/skill-issue/ci-debug-loop"><img src="https://agentmods.dev/badge/skills/paultyng/skill-issue/ci-debug-loop/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/paultyng/skill-issue/ci-debug-loop"><img src="https://agentmods.dev/badge/skills/paultyng/skill-issue/ci-debug-loop.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 | $0.00062 | $0.00908 |
| Opus 5.5 | $0.00025 | $0.00363 |
| Sonnet 5.5 | $0.00012 | $0.00182 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
ci-debug-loop 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 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.
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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CI Debug Loop
Iteratively watch CI, diagnose failures, fix, and re-trigger until the run passes or escalation is needed.
1. Identify the Run
Find the latest run for the current branch:
gh run list --branch $(git branch --show-current) --limit 1 --json databaseId,status,conclusion,name
If a specific run ID or workflow is provided by the user, use that instead.
2. Watch
If the run is still in progress:
gh run watch <run_id> --exit-status
If it already completed, proceed to diagnosis.
3. Diagnose Failure
On failure, fetch logs for the failed job(s):
gh run view <run_id> --log-failed
Delegate log analysis to a two-stage subagent pipeline when the failed-log output is more than ~200 lines. CI logs flood main context fast and the parent only needs the root cause, not the raw output. Per parallelize-subagents, delegate-investigation, and subagent-model-routing:
Stage 1 — Extract (Haiku): Spawn an Explore subagent (model: haiku) per failed job (parallel if multiple jobs failed). Prompt: paste the command to fetch the log (not the log itself); ask for "first failing assertion + ~10 lines of surrounding context, ≤50 lines, prefixed with Status: ...". Haiku handles the mechanical extraction; no interpretation needed at this stage.
Stage 2 — Interpret (Sonnet): After Stage 1 completes, spawn a generalPurpose subagent (model: sonnet) with the Stage 1 structured output pasted inline plus the failure categories below. Ask for "likely root cause + file:line if identifiable, ≤100 words, prefixed with Status: ...".
- Parent receives the Stage 2 summary, decides the fix.
For short logs (<200 lines), inspect inline.
Common failure categories:
- Build errors: compilation failures, missing dependencies
- Test failures: assertion errors, timeouts, flaky tests
- Auth/permissions: token scopes, registry auth, SSH keys
- Config/YAML: syntax errors, wrong flags, missing env vars, heredoc issues
- Infrastructure: runner issues, Docker rate limits, service unavailability
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 First seen · 90 lines · 62 tokens per session scan A 1bc9af92fb61
ci-debug-loop is a skill published in the GitHub repository paultyng/skill-issue (8 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 908 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 2026-08-31.
Other skills, from other repositories
megalinter-setup
Install or upgrade MegaLinter on a repository. Use when the user wants to add MegaLinter to a project, set up linting CI, update MegaLinter configuration or version, or says "install megalinter", "setup linting", "add code quality checks", "update megalinter skills". Always goes through npx mega-linter-runner…
gh-fix-ci
Diagnose failing GitHub Actions checks on a PR via gh, summarize the failure context, propose a plan, and only implement after explicit user approval. External CI providers (Buildkite, CircleCI, etc.) are reported by URL only.
megalinter
Entry point for everything MegaLinter. Use when the user wants to lint their repository, set up MegaLinter, check or fix lint errors, make CI lint jobs pass, or says "run megalinter", "fix lint errors", "make the linters happy", "clean up code quality". Detects the repository state and orchestrates the…
GitHub CI Fix Debugging
Debug and fix failing GitHub PR checks by inspecting GitHub Actions logs, summarizing failure context, drafting fix plans, and implementing fixes after approval.
chatcrystal-debug-recall
Recall ChatCrystal memories for debugging tasks involving failing tests, compiler errors, runtime exceptions, dependency issues, environment breakage, or performance regressions. Use when historical root causes, fixes, or pitfalls may accelerate diagnosis before proposing a fix.
github-actions-debugger
Debug and monitor GitHub Actions workflow runs. Check run status, view failed job logs, and troubleshoot CI failures. Use this when the user needs to investigate GitHub Actions failures, inspect job output, or identify the root cause of a broken workflow run.