Implement And Check

implement-and-check — make the contract real

Optimizing against a half-wired adapter produces a number that means nothing: a stub scorer gives every candidate the same reward, an empty tasks() averages over nothing, a non-deterministic scorer makes the gate chase measurement noise. This phase proves the measurement apparatus works before budget is spent. It is cheaper to fail here than after a full run.

Steps

1. Implement the 3 required adapter methods in .capevolve/project/adapters/adapter.py. These are the @abstractmethods (core/cap_evolve/adapter.py:77-106) — the gate refuses to run until all three are real:

   • tasks(split) → list[Task] for 'train'|'val'|'test'|'all'; non-empty, same list every call.
   • run_target(task, ctx, *, seed=0) → Rollout. Run the agent under test with the candidate live as ctx; capture output + trace + tool calls + cost. Do not score here. Forward seed if the runner is stochastic; set Rollout.error on an infra failure so the engine treats it as noise, not as a low score.
   • score(task, rollout) → Score: reward in [0,1] + general feedback (it becomes the diagnosis signal, so never leak the gold answer).

   Override a defaulted hook only when its default does not fit: materialize(candidate_dir, edits=None) (pure write of {component: text}), live(candidate_dir) (context manager yielding ctx), apply(candidate_dir, edits=None) (back-compat inject), trajectories(split, ctx=None) and runner_model() (both default None). Three optional fast paths are not on the base class at all — the harness feature-detects them with hasattr and uses them only if you define them: run_batch(tasks, ctx, *, seed) (drive a benchmark's own batch runner instead of run_target), run_trials(tasks, ctx, *, n_trials, base_seed) (all trials in one concurrent run), score_batch(tasks, rollouts) (score a whole trial in one external harness call). docs/ADAPTER_CONTRACT.md is the full contract, including the shown-only metrics catalog score() may return.

   Note capability_sources is not an adapter method — it is a capevolve.yaml key (the data-model/types files copied into the optimizer's context), owned by intake.

2. Implement any selected skill's scripts/abstract.py (most are concrete and need nothing).

3. Run the gate:

   python scripts/run.py --project .capevolve/project \
       --skill-check <skills>/capabilities/<cap>/scripts/check.py
   

   Exit 0 = green. The JSON has three fields with three different meanings — see the table below before you react to it.

4. Pipeline-wiring self-test (automatic once the check is green). A green adapter is necessary but not sufficient — the optimizer also needs its context wired. run.py then runs pipeline_selftest.py (zero API cost): the optimizer-prompt template named by capevolve.yaml::optimizer_instructions_file exists, still carries its {{...}} placeholders, and renders through the real harness renderer with none left over; and whether the adapter defines trajectories() or inherits the base default (both valid, both reported). The template checks are skipped with a note for an algorithm that never reads the template — only hill-climb is passed --instructions-file (cli.py:869-876). --no-pipeline-selftest skips it; it also runs standalone.

   A full one-iteration mock run is deliberately not attempted: it would need a baseline, a frozen split and a run dir that do not exist yet at gate time, and building them is benchmark-specific. This exercises the same workdir-building and prompt-rendering paths.

When it is red — what to do, per failure kind

CheckReport has three fields (core/cap_evolve/check.py:30-38) and only problems affects ok. Treating a note as a failure is how an agent gets stuck in a loop.

Re-run until green. Green is the entry condition for baseline.

What the gate does and does not guarantee

Determinism is genuinely executed, not asserted: check.py:133-142 scores one fixed rollout twice and reports a problem when the rewards differ. Do not read more into green than that. Measured on this checkout (issue #358):

• run_target is never called on the default path, so a pass-body runner goes green and fails later, after the split is frozen.
• The scorer probe uses a synthetic rollout (output="__probe_output__"), so a scorer that short-circuits on unrecognizable output — every LLM-judge scorer — is not really tested. Both score() calls happen on one in-process instance, so a memoized scorer is unfalsifiable here.
• materialize() is a probe, not an assertion: a raise is a note and does not fail the check (check.py:166-167), because a real adapter may need its full environment. Green means "callable or explained", not "edit path verified".
• Both entry paths fail closed, so a red check never freezes a split: cap-evolve run returns 1 before creating a run dir (cli.py:721-726), and the standalone /cap-evolve:baseline re-runs the core check itself and exits non-zero before the run dir exists (baseline/scripts/run.py). What is not a runtime precondition is the provides: checked token — it declares ordering only, so a phase that skips baseline gets no gate from the DAG.

If your scorer calls a judge, say so in PROJECT.md along with how its decoding is pinned — the gate cannot see it. The one failure mode nothing here can catch: feedback that leaks the gold answer passes every wiring check and still corrupts diagnosis.

Dual-mode

Standalone as /cap-evolve:implement-and-check; orchestrator-callable — but uniquely for this phase, cap-evolve run does not invoke scripts/run.py. It calls the core check inline and shells straight to baseline, so --skill-check and the pipeline self-test run in standalone mode only. Run this phase yourself before either cap-evolve run or /cap-evolve:baseline if you want them: both of those re-run the core check, but neither runs --skill-check or the pipeline self-test.

References

• references/concepts.md — why each check exists, the scorer-determinism-vs-target-stochasticity distinction (load this when deciding whether your scorer's variance is a bug or a measurement), and the sources.