CCPM is a structured project management skill for AI agents (Claude Code, Codex, etc.) that enforces a spec-driven development workflow: PRD → Epic → GitHub Issues → Parallel Agents → Shipped Code. The codebase consists of markdown reference documents (plan.md, structure.md, sync.md, execute.md, track.md, conventions.md) and a set of deterministic bash scripts that read project state from .claude/ directory (PRDs, epics, tasks with frontmatter). The system does not contain any runtime code—it is purely a collection of instructions and helper scripts that an AI agent reads and executes. Its architecture is a flat skill structure following the agentskills.io standard, with no external dependencies beyond git, gh CLI, and a GitHub repository. The primary data flows are local file operations (creating/reading/updating markdown files) and GitHub API calls via gh CLI for issue creation, syncing, and closing. The skill is harness-agnostic and works with any compatible agent framework.
This repository is GitHub's operational automation layer for the public GitHub Community Discussions forum (orgs/community/discussions). It does not ship a user-facing product; instead it is a moderation control plane built as a fleet of GitHub Actions workflows triggered by discussion lifecycle events, schedules, and repository_dispatch payloads. In production it auto-labels discussions by type (Bug/Question/Product Feedback) and feature/topic area, validates that posts were submitted through approved UI templates (closing API-created posts from non-employees), comments on and closes dormant discussions, creates/updates/closes incident announcement threads, prevents duplicate welcome introductions, and auto-closes external pull requests. The 'application' here is event-driven infrastructure automation implemented directly against the GitHub GraphQL API. The code is split into Ruby action scripts (in .github/actions/ with shared libraries in .github/lib) and Python helper scripts (in .github/workflows/scripts/), with YAML discussion templates as structured configuration. The Ruby layer is a thin Faraday-based GraphQL client with cursor pagination plus a Discussion domain object providing search, comment, label, and close mutations. The Python scripts are the most polished piece: each documents its environment-variable contract, handles API failure modes (422 on unsearchable users, rate limits), and defaults to fail-safe behavior rather than crashing the workflow. Notable engineering: integration_test_runner.py creates real discussions in the production forum with an '[IT]' prefix, waits for the triggered labeler workflows to run, verifies label and closure outcomes, and then closes all test discussions — an unusually thorough end-to-end testing pattern that also captures the subtle requirement that a PAT (not the built-in job token) is needed to trigger downstream discussion-created workflows. Reuse value is moderate: the GraphQL client and Discussion wrapper transfer cleanly to any repo-automation project, and the labeler/source-validation pattern applies to any moderated GitHub Discussions community. Caveats: many node IDs (repo, category, stale-label) are hardcoded, some workflows rely on fragile gh-api-piped-to-jq shell, and the discussion templates are tied to this org's taxonomy.