Github

GitHub Skill

Use the gh CLI to interact with GitHub. Always specify --repo owner/repo when not in a git directory, or use URLs directly.

When to Use

• When the user asks about GitHub issues, pull requests, workflow runs, or CI failures.
• When you need gh issue, gh pr, gh run, or gh api from the command line.

Pull Requests

Check CI status on a PR:

gh pr checks 55 --repo owner/repo

List recent workflow runs:

gh run list --repo owner/repo --limit 10

View a run and see which steps failed:

gh run view <run-id> --repo owner/repo

View logs for failed steps only:

gh run view <run-id> --repo owner/repo --log-failed

Debugging a CI Failure

Follow this sequence to investigate a failing CI run:

1. Check PR status — identify which checks are failing:

   gh pr checks 55 --repo owner/repo
   

2. List recent runs — find the relevant run ID:

   gh run list --repo owner/repo --limit 10
   

3. View the failed run — see which jobs and steps failed:

   gh run view <run-id> --repo owner/repo
   

4. Fetch failure logs — get the detailed output for failed steps:

   gh run view <run-id> --repo owner/repo --log-failed
   

API for Advanced Queries

The gh api command is useful for accessing data not available through other subcommands.

Get PR with specific fields:

gh api repos/owner/repo/pulls/55 --jq '.title, .state, .user.login'

JSON Output

Most commands support --json for structured output. You can use --jq to filter:

gh issue list --repo owner/repo --json number,title --jq '.[] | "\(.number): \(.title)"'

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags github <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns