eval-capture

name

eval-capture

description

Snapshot a just-completed task into the regression corpus (evals/corpus/). Use after any task where the user accepted the result AND future harness changes could plausibly regress it — recurring task shapes, tasks that needed corrections before acceptance, anything involving taste the user articulated. Also use when /meta-retro reports a category with misses but no eval coverage. Without corpus growth, "harness improvement" is unmeasurable drift.

Eval Capture

The corpus is the only proof that harness vN+1 beats vN. Feed it.

When to capture (any one suffices)

• The task shape recurs (third "write a migration" = the migration eval)
• The user corrected you before accepting — the correction IS the rubric
• The result encodes taste the user articulated ("never bury the lede")

Skip: one-offs, secrets/proprietary data (sanitize or skip), pure lookups.

Case anatomy: evals/corpus//

• task.md — the request, verbatim enough to be re-runnable, with any needed fixtures alongside.
• exactly one grader:
  • check.py — objective: gets the output dir as argv[1], exits 0/1. Prefer this whenever checkable; it's free to run forever.
  • rubric.md — subjective: 3-6 falsifiable criteria for the critic agent. "Subject line under 60 chars" yes; "feels professional" no.
• meta.json — {"date", "category", "source_session", "origin": "..."}

Rules

• A correction-born case MUST encode the corrected behavior as the expected one — that's the regression you're guarding against.
• Keep cases hermetic: no network, no machine-specific paths, fixtures < 50KB.
• Tag category to match prediction categories so calibration stats and eval results join up in /meta-retro.
• Leakage check (behavioral evals). An eval that claims to test "the agent used system X" is confounded if X's answer is pre-taught. Before finalizing, grep the loaded skills/, hooks/, and memory/decisions/ for the expected answer; if an artifact that loads for this task already teaches it, the eval passes WITHOUT exercising the capability — choose a fixture whose solution is NOT pre-taught. Writing a fidelity constraint is not satisfying it: verify the chosen fixture against it. (session 0d0fe086, 2026-06-22: a cp1252 recall eval used the single most pre-taught fix in the repo.)

Verify in-session before committing: run /run-evals (fresh subagent performs the task, no headless — ADR 0003) and confirm it passes today. A case that fails on day one is either a real finding (file it) or a bad rubric (fix it).