ai-literacy-assessmentlisted

# AI Literacy Assessment Assess a team's AI collaboration literacy level by combining observable evidence from the repository with clarifying questions, then produce a timestamped assessment document and a README badge. ## The Assessment Process ### Phase 1: Observable Evidence Scan the repository for signals that indicate which framework level the team is operating at. **Habitat document discovery comes first.** Before scanning for any of the level indicators below, apply the methodology in `references/habitat-discovery.md` to find `HARNESS.md`, `AGENTS.md`, and `CLAUDE.md` — including their alternative paths and embedded forms. A habitat document found at a non-conventional path counts as *present* for the Level 3 indicators that reference it; "not at the default path" is not the same as "doesn't exist". Every absence claim must come from a fully-completed search across known alternatives, with the discovery report citing what was matched and where. Each signal below maps to a specific level: **Level 0-1 indicators (awareness + prompting)**: - Does the repo exist and contain code? (baseline) - Are there any AI-related configuration files at all? **Level 2 indicators (verification)**: - CI workflows that run tests (`*.yml` in `.github/workflows/`) - Test coverage enforcement (coverage thresholds in CI or build files) - Vulnerability scanning (govulncheck, OWASP, Docker Scout) - Markdownlint or other linting in CI - Mutation testing configuration - Small, TDD-paced