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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongh-fix-ciExecute the skills CLI command in your project's root directory to begin installation:
Fetches gh-fix-ci from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate gh-fix-ci. Access via /gh-fix-ci in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
plan skill for drafting and approving the fix plan.Prereq: ensure gh is authenticated (for example, run gh auth login once), then run gh auth status with escalated permissions (include workflow/repo scopes) so gh commands succeed. If sandboxing blocks gh auth status, rerun it with sandbox_permissions=require_escalated.
repo: path inside the repo (default .)pr: PR number or URL (optional; defaults to current branch PR)gh authentication for the repo hostpython "<path-to-skill>/scripts/inspect_pr_checks.py" --repo "." --pr "<number-or-url>"--json if you want machine-friendly output for summarization.gh auth status in the repo with escalated scopes (workflow/repo) after running gh auth login.sandbox_permissions=require_escalated to allow network/keyring access.gh pr view --json number,url.python "<path-to-skill>/scripts/inspect_pr_checks.py" --repo "." --pr "<number-or-url>"--json for machine-friendly output.gh pr checks <pr> --json name,state,bucket,link,startedAt,completedAt,workflow
gh.detailsUrl and run:
gh run view <run_id> --json name,workflowName,conclusion,status,url,event,headBranch,headShagh run view <run_id> --loggh api "/repos/<owner>/<repo>/actions/jobs/<job_id>/logs" > "<path>"detailsUrl is not a GitHub Actions run, label it as external and only report the URL.plan skill to draft a concise plan and request approval.gh pr checks to confirm.Fetch failing PR checks, pull GitHub Actions logs, and extract a failure snippet. Exits non-zero when failures remain so it can be used in automation.
Usage examples:
python "<path-to-skill>/scripts/inspect_pr_checks.py" --repo "." --pr "123"python "<path-to-skill>/scripts/inspect_pr_checks.py" --repo "." --pr "https://github.com/org/repo/pull/123" --jsonpython "<path-to-skill>/scripts/inspect_pr_checks.py" --repo "." --max-lines 200 --context 40Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend gh-fix-ci for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in gh-fix-ci — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
gh-fix-ci has been reliable in day-to-day use. Documentation quality is above average for community skills.
gh-fix-ci is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: gh-fix-ci is focused, and the summary matches what you get after install.
gh-fix-ci fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
gh-fix-ci has been reliable in day-to-day use. Documentation quality is above average for community skills.
gh-fix-ci reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend gh-fix-ci for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added gh-fix-ci from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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