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agentic-repo-discovery

Use when an existing repository contains agents, prompts, workflows, playbooks, commands, or Skills and you need to identify which capabilities are worth converting into a ChatGPT/Codex Plugin.

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SKILL.md (original)

Review the instructions the agent will receive before you install.

Agentic Repo Discovery

Find the reusable user workflows inside a repository before creating Plugin files.

Procedure

  1. Read repository instructions and the main README first.
  2. Run the bundled analyzer when filesystem execution is available:
python3 ../chatgpt-codex-plugin-autopilot/scripts/analyze_repo.py <target-repo> --json
  1. Inspect high-scoring candidates in context. The analyzer finds signals; it does not understand the full product by itself.
  2. Build a candidate disposition table using: preserve_skill, compile_skill, reference_only, runtime_dependency, internal_only, or discard.
  3. Identify the user's repeatable job for every candidate. Reject candidates whose only value is repository maintenance trivia unless that is the intended Plugin product.
  4. Flag secrets, organization-specific instructions, destructive operations, security-sensitive workflows, hidden telemetry, and policy-sensitive capabilities for explicit public-distribution review.
  5. Recommend skills-only, MCP-backed, or hybrid from actual behavior. Treat .mcp.json or .app.json as evidence to inspect, not automatic proof that the public Plugin needs them.
  6. Hand selected workflow candidates to workflow-to-skill-compiler and the overall product boundary to plugin-experience-architect.

Output

Return a concise conversion brief containing:

  • repository/ref inspected
  • reusable jobs discovered
  • candidate dispositions with reasons
  • architecture recommendation with confidence
  • runtime dependencies
  • public exclusions
  • missing evidence
  • next conversion actions

Do not generate Plugin configuration before this boundary is clear.

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