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 CobolJunkie/gitlab-mcp --skill gl-fix-cigit clone --depth 1 https://github.com/CobolJunkie/gitlab-mcpWrote 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/coboljunkie/gitlab-mcp/gl-fix-ci)<a href="https://agentmods.dev/skills/coboljunkie/gitlab-mcp/gl-fix-ci"><img src="https://agentmods.dev/badge/skills/coboljunkie/gitlab-mcp/gl-fix-ci/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/coboljunkie/gitlab-mcp/gl-fix-ci"><img src="https://agentmods.dev/badge/skills/coboljunkie/gitlab-mcp/gl-fix-ci.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 | $0.00070 | $0.00435 |
| Opus 5 | $0.00035 | $0.00217 |
| Sonnet 5 | $0.00014 | $0.00087 |
| Haiku 4.5 | $0.00007 | $0.00044 |
Grade A, and why
gl-fix-ci 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 8d 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
Fix GitLab CI
Use the bundled GitLab MCP tools for pipeline, job, log, artifact, and commit
status inspection. Use local git for code changes and local verification.
Workflow
- Resolve the project and merge request, branch, commit SHA, pipeline ID, or job ID from the request and current checkout.
- For a merge request, call
list_merge_request_pipelines, then inspect the most relevant failed or running pipeline withget_pipeline. - Call
list_pipeline_jobsand inspect failed, canceled, or blocked jobs. - Fetch only the relevant bounded log section with
get_job_log. Retrieve a specific artifact file withget_job_artifact_fileonly when logs are insufficient. - Separate the first causal failure from downstream or canceled noise.
- Explain the root cause with job names, failure evidence, and affected files.
- If the user asked for a fix, make the smallest scoped change in the local checkout and run the closest practical local checks.
- Recheck the resulting diff and report what should change in the next pipeline.
Guardrails
- A diagnosis request does not authorize editing code or CI configuration.
- Do not retry, cancel, or play jobs or pipelines unless the user explicitly requested that state change.
- Do not print or preserve access tokens, masked variables, credentials, or secrets that appear in logs or artifacts.
- Avoid downloading broad artifact archives; request only a named file.
- Do not claim the remote pipeline is fixed until a new pipeline verifies it.
Output
Lead with the root cause. Distinguish observed remote evidence, local changes, local verification, and any remaining remote verification step.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 42 lines · 70 tokens per session scan A 2be963bdbec4
gl-fix-ci is a skill published in the GitHub repository CobolJunkie/gitlab-mcp (0 stars, last pushed 20d ago), licensed MIT. It adds 70 tokens to every session and 435 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 skills, from other repositories
github-ci-fix
Use when PR checks fail, CI is red, or GitHub Actions workflows break - systematically inspects failing checks via gh CLI, pulls logs, checks for flakiness and scopes the breaking commit, scopes external checks, then creates fix plan using existing plan skill.
investigate-ci
Investigates GitHub Actions workflow failures for any repo. Fetches recent runs, identifies failures, extracts error logs, diagnoses root causes, and suggests fixes. Use when a deploy or CI workflow fails and you need to understand why.
atmos-lint
Atmos Terraform linting with TFLint: standalone atmos terraform lint, component-aware config discovery and toolchain versions, TFLint rule configuration, and lifecycle hooks/CI findings. Use when configuring, running, debugging, or documenting Terraform/OpenTofu linting in an Atmos project.
manage-skills
A maintenance workflow for checking whether project verification skills still cover the code and rules that changed during a session.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
systematic-debugging
Structured debugging methodology — use before proposing fixes for any error or failure. Covers: code bugs, build errors, deploy failures, config conflicts, dependency issues, infra problems. Also use when previous fix attempts failed or root cause is unclear.