ai-discover, the discovery step of the auto-improvement loop

This skill drives Phase A (discovery) of an auto-improvement cycle. It is discovery only: it never applies a change, never runs the keep-or-revert A/B, and never decides whether a finding is kept or drafted as a pull request. Those are the spine's deterministic Python gate / keeper / pipeline, which no model can argue past. That separation is the point, the measurement is the product.

How discovery actually runs

The spine runs discovery as ONE bounded agent turn per cycle (spine/agent_discovery.py discover_surfaces_via_agent), not a fan-out. Do NOT spawn subagents: the turn must end with the JSON array itself, and children spawned from an unattended runner orphan and hang the run (see the SessionAgentRunner notes in spine/agent_runner.py).

• Tools are Read, Grep and Glob only. There is no shell and no edit: discovery is read-only by capability, so you cannot run a profiler or write a test here.
• Read the priority slice you are given, stop after 2-3 solid findings or the read budget, and reply with ONLY a JSON array (at most the stated limit) of {file, line, symbol, rule, message, hypothesis}, rule is AGENT, or COVERAGE for a coverage gap. Reply [] when nothing is testable. The final reply IS the output; there is no separate artifact file.
• The same surface shape feeds both tracks. On the bug track the profile names the reproducing test path and author_bug_fix later writes the RED test and the fix in an isolated worktree; on the perf track author_perf_fix writes the edit. The spine dedups by (kind, basename::symbol) locus, then gates (RED -> GREEN -> STAYGREEN for bugs, serial pinned A/B for perf).

Reporting a candidate honestly