Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill gh-fix-cigit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/gh-fix-ci)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/gh-fix-ci"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/gh-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/foryourhealth111-pixel/vibe-skills/gh-fix-ci"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/gh-fix-ci.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00072 | $0.00898 |
| Opus 5.5 | $0.00029 | $0.00359 |
| Sonnet 5.5 | $0.00014 | $0.00180 |
| Haiku 4.5 | $0.00007 | $0.00090 |
Grade A, and why
gh-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 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gh Pr Checks Plan Fix
Overview
Use gh to locate failing PR checks, fetch GitHub Actions logs for actionable failures, summarize the failure snippet, then propose a fix plan and implement after explicit approval.
- If a plan-oriented skill (for example
create-plan) is available, use it; otherwise draft a concise plan inline and request approval before implementing.
Prereq: authenticate with the standard GitHub CLI once (for example, run gh auth login), then confirm with gh auth status (repo + workflow scopes are typically required).
Inputs
repo: path inside the repo (default.)pr: PR number or URL (optional; defaults to current branch PR)ghauthentication for the repo host
Quick start
python "<path-to-skill>/scripts/inspect_pr_checks.py" --repo "." --pr "<number-or-url>"- Add
--jsonif you want machine-friendly output for summarization.
Workflow
- Verify gh authentication.
- Run
gh auth statusin the repo. - If unauthenticated, ask the user to run
gh auth login(ensuring repo + workflow scopes) before proceeding.
- Run
- Resolve the PR.
- Prefer the current branch PR:
gh pr view --json number,url. - If the user provides a PR number or URL, use that directly.
- Prefer the current branch PR:
- Inspect failing checks (GitHub Actions only).
- Preferred: run the bundled script (handles gh field drift and job-log fallbacks):
python "<path-to-skill>/scripts/inspect_pr_checks.py" --repo "." --pr "<number-or-url>"- Add
--jsonfor machine-friendly output.
- Manual fallback:
gh pr checks <pr> --json name,state,bucket,link,startedAt,completedAt,workflow- If a field is rejected, rerun with the available fields reported by
gh.
- If a field is rejected, rerun with the available fields reported by
- For each failing check, extract the run id from
detailsUrland run:gh run view <run_id> --json name,workflowName,conclusion,status,url,event,headBranch,headShagh run view <run_id> --log
- If the run log says it is still in progress, fetch job logs directly:
gh api "/repos/<owner>/<repo>/actions/jobs/<job_id>/logs" > "<path>"
- Preferred: run the bundled script (handles gh field drift and job-log fallbacks):
- Scope non-GitHub Actions checks.
- If
detailsUrlis not a GitHub Actions run, label it as external and only report the URL. - Do not attempt Buildkite or other providers; keep the workflow lean.
- If
- Summarize failures for the user.
- Provide the failing check name, run URL (if any), and a concise log snippet.
- Call out missing logs explicitly.
- Create a plan.
- Use the
create-planskill to draft a concise plan and request approval.
- Use the
- Implement after approval.
- Apply the approved plan, summarize diffs/tests, and ask about opening a PR.
- Recheck status.
- After changes, suggest re-running the relevant tests and
gh pr checksto confirm.
- After changes, suggest re-running the relevant tests and
What ships with it
5 files 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.
- 1mo ago First seen · 70 lines · 72 tokens per session scan A 7b326b4a2f0f
gh-fix-ci is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,770 stars, last pushed yesterday), licensed Apache-2.0. It adds 72 tokens to every session and 898 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
comet-github-ci-triage
A workflow for diagnosing failed GitHub Actions checks on a Comet pull request. GitHub Actions runs automated builds and tests; a pull request is a proposed code change waiting to be reviewed and merged.
squid-review-ci
Drive CI green on a pushed, review-clean feature PR — On-Call diagnoses failures and hands fix tasks to the SWE. Output: a CI-validated feature PR. Trigger after /squid-review passes.
k8s-yaml-validator
Comprehensive toolkit for validating, linting, testing Kubernetes YAML resources. Use this skill when validating Kubernetes manifests, debugging YAML syntax errors, performing dry-run tests on clusters, working with Custom Resource Definitions (CRDs) that require documentation lookup.
ci-auto-fix
Diagnoses and fixes failing GitHub Actions CI, verifies locally, pushes, and confirms the checks pass on the new revision — never weakens checks. Triggers on "CI is failing", "fix the CI", "the build is red", "/ci-auto-fix".
fix-ci-until-green
Drive a failing GitHub Actions run to green in as few CI runs as possible, with a bounded fix-critique-commit-push-recheck loop that batches every evidenced fix into each push. Use when the user supplies a GitHub Actions run URL or run ID and wants the failure fixed, or asks to "make CI green", "fix the failing…
ci-failure-triage
Diagnose failed CI runs, build logs, test output, deployment checks, or pipeline summaries and produce a root-cause hypothesis, reproduction path, likely owner, fix plan, and escalation guidance. Use when Codex needs to triage broken builds, flaky tests, failed checks, or release-blocking automation failures.