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AI Readiness is a repository-level assessment. You get a score, status, factor breakdown, and prioritized recommendations for each repository. Propel stores one readiness snapshot per company and repository.

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What AI readiness measures

AI Readiness measures how prepared your repository is for reliable AI-assisted engineering work. The current rubric version is v1. The rubric evaluates:
  • Agent instructions and reusable rules
  • Documentation depth and clarity
  • Project structure and discoverability
  • Build and development consistency
  • Validation quality (CI, tests, lint)
  • Automation and governance workflows

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Assessment lifecycle

StatusMeaning
not_availablePropel cannot score the repository yet.
computingPropel is currently computing a new assessment.
readyA current score is available.
staleA score exists but is older than 14 days.
failedThe assessment job failed.
If a score is ready and computed_at is older than 14 days, Propel surfaces status as stale.

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Not available reasons

ReasonMeaning
not_assessedNo assessment has been run yet.
no_active_codebaseThe repository is not attached to an active codebase.
no_repository_dataRepository structure data is missing.
empty_repositoryNo source files were found.
assessment_errorAssessment failed internally.

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Scoring algorithm

The rubric has 33 pass or fail criteria across levels 1 through 5. Propel computes score as: 
score = round((passed_criteria / total_criteria) * 100)
score is clamped to 0..100
Propel also computes maturity level:
  • Level 1 to 5 are evaluated in order.
  • A level is achieved when at least 80% of that level’s criteria pass.
  • The first level below 80% stops progression.
  • next_level_progress is pass percent for the next level.

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Rubric definition (v1)

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Level 1 baseline

  • Primary instruction file exists: AGENTS.md, CLAUDE.md, or CODEX.md.
  • Reusable rules exist in .propel/rules, .cursor/rules, .claude/rules, .codex/rules, or .cursorrules.
  • A README exists.
  • At least one standard layout directory exists (src, app, cmd, pkg, internal, services).
  • A developer entrypoint exists (Makefile, Justfile, .devcontainer, or CI config).
  • Lint or static analysis config exists.

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Level 2 team readiness

  • At least one nested rules file exists (for example .propel/rules/api/...).
  • CONTRIBUTING guidance exists.
  • Test files exist.
  • CI pipeline config exists.
  • PR validation workflow exists.
  • Environment template exists (for example .env.example).

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Level 3 scalable engineering

  • Architecture or ADR documentation exists.
  • Docs depth is at least 3 files.
  • Standard layout coverage is at least 2 directories.
  • Max directory depth is between 2 and 12.
  • Test framework config types are at most 2.
  • Dependency update automation exists.
  • Autofix automation exists.
  • Security automation exists.
  • Secret scan automation exists.
  • Stale work automation exists.

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Level 4 governance and consistency

  • Code review workflow is active.
  • CODEOWNERS enforcement exists (CODEOWNERS plus PR validation or code review automation).
  • Docs depth is at least 8 files.
  • JavaScript lockfile strategy is consistent (at most 1 lockfile type).
  • Python tooling formats are bounded (at most 2).
  • Language ecosystem count is bounded (at most 4).

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Level 5 mature operating model

  • Nested rule files are at least 4.
  • Docs depth is at least 20 files.
  • Standard layout coverage is at least 3 directories.
  • CI, tests, and lint are all present.
  • Tooling fragmentation is low: js_lockfile_kinds <= 1 and test_framework_config_kinds <= 2 and java_build_system_kinds <= 1 and python_tooling_kinds <= 2.

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Factors and recommendations

Propel groups criteria into pillar factors. Each factor returns:
  • score: passed criteria in that pillar
  • max_score: total criteria in that pillar
  • evidence: proof points from your repository
  • recommendations: practical improvements for that pillar
You also get top_recommendations, a prioritized set of next actions. Propel prioritizes recommendations from your next maturity level first.

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Next steps

  1. Open Codebase and click AI Readiness on a repository.
  2. Click Refresh Assessment to compute the latest score.
  3. Apply top recommendations and refresh again to track improvement.