Opportunity Scan — find what to change, from what actually happened

Reads your agent's own capabilities plus one target you choose, and recommends which primitive each finding should become. Agent-agnostic (Claude Code, Codex, PI, …). It maps what it finds to the full primitive palette (rules · skill · hook · subagent · MCP · automation/workflow), and it works for any agent because it learns that agent's capabilities first.

Two targets, one skill — this is the whole design:

• A run → the REACTIVE loop. Something just went sideways in a loop you ran. Point the scan at that run's artifacts and ask "what in the AI layer would have prevented this?" You fix the system, not the code.
• A window of logs → the PROACTIVE scan. Nothing is broken. Point it at weeks of sessions and ask "what do I keep doing by hand that should be encoded?"

Same skill, same output shape — you're just changing what it reads.

This is a discovery tool — what to change — NOT a quality eval (whether a built thing is good). Keep the two separate.

Inputs — required first, then optional