Why

AI coding agents are only as effective as the context they receive. agentrc solves the creation problem — it analyzes real code and generates tailored instructions. But creation without distribution is a dead end. Teams need to share instructions across repos, govern what agent packages are allowed, and audit them in CI.

APM (Agent Package Manager) solves the distribution problem — transitive dependency resolution, security scanning, multi-platform compilation, and enterprise policy enforcement for agent primitives.

Together: agentrc generates. APM distributes. Neither tool needs the other to be useful, but together they close a loop that neither closes alone.

What

This epic tracks the first wave of agentrc × APM integration:

1. Readiness scoring — agentrc scores repos on APM adoption (feat(readiness): Add APM (Agent Package Manager) awareness to ai-tooling pillar #91)
2. Init bridge — agentrc init detects APM and offers to set it up (feat(init): bridge to APM during init for cross-team distribution #93)
3. Cross-documentation — README sections establishing the ecosystem narrative

Key design principles

• agentrc never writes apm.yml — it delegates to apm init (APM owns its manifest)
• Formats are compatible by default — .instructions.md files work in both tools without conversion
• Explicit opt-in — APM is suggested, never imposed; --yes skips it entirely
• Subprocess integration — tools call each other via CLI, respecting the Python/TypeScript boundary

Child issues

• feat(readiness): Add APM (Agent Package Manager) awareness to ai-tooling pillar #91 — feat(readiness): Add APM awareness to ai-tooling pillar → PR feat(readiness): add APM awareness to ai-tooling pillar #92
• feat(init): bridge to APM during init for cross-team distribution #93 — feat(init): Bridge to APM during init → PR feat(init): bridge to APM during init for cross-team distribution #94
• Cross-documentation (README updates) → PR docs: add APM ecosystem section to README #95