dojo-ascension

CONTEXT: AI and Contributor Onboarding

Dojo Ascension is a lightweight, mission-driven learning project designed to teach Python, Git, JSON, and code review through practical prompts and reflection. The repository is intentionally structured so that non-programmers, educators, and engineers can collaborate on a shared curriculum without needing a heavy platform or external services.

At a high level, there are two active frontends that share the same pedagogical model: a terminal experience in dojo_classroom.py and a browser experience in dojo_web.html. The terminal path includes optional reflective journaling and local JSON persistence for progress tracking. Core mission data is stored in missions/missions.json, with companion mission files in missions/ for structured, schema-rich authoring workflows.

If you are trying to understand the project quickly, read in this order:

  1. README.md for project intent, workflows, and contributor entry points.
  2. missions/missions.json for the canonical mission sequence consumed by the runtime.
  3. CONTRIBUTING.md and missions/MISSION_REVIEW_RUBRIC.md for writing standards.
  4. docs/ materials for governance, metrics, privacy, and accessibility constraints.

Continuous integration currently validates quality with tests and mission validation. Existing checks live in .github/workflows/validate.yml and run unit tests plus mission schema checks on pushes and pull requests. A dedicated workflow, .github/workflows/refresh-repo-bundle.yml, now keeps repo_bundle.txt refreshed on pushes to main, using only GITHUB_TOKEN for bot-authenticated commits when bundle content changes.

The plain-text bundle pipeline is maintained by repo_text_export.py and refresh_repo_bundle.py. The exporter intentionally excludes generated bundle artifacts to avoid recursive self-ingestion and unnecessary churn. This keeps AI-facing snapshots stable and useful for rapid analysis.

To verify local changes, run:

Project tone matters as much as syntax. Preferred contributions are clear, inclusive, and practical. The style favors plain language, respectful review, and system-level thinking over gatekeeping or jargon-heavy explanations. Keep changes focused, preserve interoperability between frontends, and avoid introducing hidden infrastructure dependencies.

For AI tools and fast repository ingestion, use llms.txt and repo_bundle.txt as the first loading surfaces, then drill into specific files relevant to the task.