AI Readiness Report

{{repoName}} · Assessed {{date}} · L{{level}} — {{levelName}} · Overall {{overallPct}}% · Grade {{grade}}

What is AI Readiness?

AI coding agents are only as effective as the context they receive. AgentRC measures how AI-ready a repo is across 9 pillars in two categories — Repo Health and AI Setup — and maps the result to a 5-level maturity model. This report is the Measure step in AgentRC's Measure → Generate → Maintain loop.

Each pillar carries an AI relevance rating (High / Medium / Low) so you can tell at a glance which gaps most directly affect Copilot's output and which are general engineering hygiene.

Maturity
L{{level}} — {{levelName}}
Overall Score
{{overallPct}}%
Grade {{grade}}
Pass rate
{{passRate}}
Threshold {{threshold}}

Maturity Progression

LevelNameStatus

Active Policy

{{policyName}} — {{policySummary}}

Repo Health Breakdown

AI Setup Breakdown

Extras (informational, do not affect score)

ExtraStatus

Prioritised Remediation Plan

🔴 Fix First (high impact / low effort)

#FindingFile / configWhy it matters

🟡 Fix Next (medium impact / low effort)

#FindingFile / configWhy

🔵 Plan (medium impact / medium effort)

#FindingFile / configWhy

Next Steps

  1. Generate or refresh instructions: agentrc instructions --output .github/copilot-instructions.md (or use the generate-instructions skill).
  2. Address each item under 🔴 Fix First; re-run this report to confirm score improvement.
  3. Codify org standards via a JSON policy (strict.json, ai-only.json, …) and re-run with --policy.
  4. Wire agentrc readiness --fail-level <n> into CI to prevent regressions.
Raw AgentRC JSON 
{{rawJsonPretty}}