# Evaluate > Score a candidate on a split with honest, variance-aware evaluation. Use whenever you need a number for a candidate (the algorithm calls it internally; you can also call it directly to inspect). Runs the target via the adapter for each task, scores each rollout, aggregates mean + standard error, and reports pass^k when trials > 1. Never touches the test split (that is finalize's sealed job). - Skill: `skillberry-ai/evaluate` (Agent Skill, multi-file: 7 files) - Install (CLI): `npx skillmds@latest add skillberry-ai/evaluate` - Raw SKILL.md: https://api.skillmd.com/api/skills/skillberry-ai/evaluate/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: skillberry-ai (https://skillmd.com/u/skillberry-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/skillberry-ai/evaluate --- # evaluate — honest, multi-trial scoring Turns a candidate into a score you can *trust*. A reward number is only as honest as the variance around it and as the denominator under it: agents are stochastic, and infrastructure fails. evaluate produces a point estimate, its uncertainty, and the count of tasks that actually produced a measurement. The math lives in `cap_evolve.stats`; this skill drives the adapter and aggregates. ## What it produces A `SplitResult` (`core/cap_evolve/loop.py:59-115`): - **`reward`** — the mean, over the tasks that were **scored**, of each task's mean over its **valid** trials (`harness.py:405-414`, `loop.py:127-131`). Not the mean over every task in the split — see the next section. - **`n_tasks` / `n_scored`** (and `coverage = n_scored/n_tasks`, `loop.py:79-84`) — the honest denominator. Read these on every result, never `reward` alone. - **`stderr`** — the *combined* SE of that reported mean: between-task variance (do different tasks agree?) folded with within-task trial variance (is the agent consistent on a fixed task?), `stats.combined_stderr`. This is what the report prints and what the gate's `significant` mode consumes — **not** what the default gate reads; see "What the gate actually consumes". - **`pass_k`** — when trials > 1, the estimated probability that **all** k i.i.d. trials pass (reliability). Also `pass_at_k` — at least one of k passes (capability). Opposite questions; see `references/concepts.md`. - **per-task scores + feedback**, and the rollout files `diagnose` reads: `/rollouts//____t.json` (`harness.py:334`). ## A crashed rollout is missing data, not a zero The single largest honesty mechanism in the eval path. Two ways a trial produces no measurement: - the runner errored (`rollout.error` set) — the target never ran; - the rollout *succeeded* and the **scorer** could not grade it (crashed grading harness, missing report file). There is no `rollout.error`, so adapters must flag it by setting `Score.raw["errored"]` (`harness.py:308-323`). An adapter that doesn't is how a scorer outage becomes a real 0.0. Such a trial is excluded from the mean (`harness.py:324-331`), and a task with zero valid trials is dropped from **every** statistic (`loop.py:118-127`). Averaging its 0.0 in would state that the capability failed a task it was never given — which is how a registry rate-limit storm produced `val 0.000` and taught the optimizer to "fix" content that was never at fault. The rollout file is still written, for forensics. **What to check.** `raw.valid_trials == 0` on a per-task record means unmeasured, not failed. A `reward` computed over a third of a split describes the infrastructure, not the edit. Below `coverage 0.6` the gate returns `indecisive=True` and declines to judge rather than calling it a regression (`gate.py:137-146`) — a run producing repeated indecisive steps has an infrastructure fault, not a bad optimizer. Pinned by `core/tests/test_infra_errors_not_zeros.py` (518 lines). ## What the gate actually consumes When per-task data is available the loop sets gate mode to `paired` (`harness.py:1524-1526`), and paired mode **recomputes** the SE from the per-task deltas against the same tasks (`gate.py:156-160`); `SplitResult.stderr` is never read on that path. So what extra trials buy you under the default gate is a more stable *per-task* mean, which shrinks the paired delta variance — not a smaller `stderr`. `stderr` feeds the report and the `significant` fallback used when paired data is unavailable (`gate.py:184-207`). ## How to run ``` python scripts/run.py --run-dir .capevolve/run_XXXX --project .capevolve/project \ --candidate seed --split val --n-trials 3 ``` - `--split` accepts only `train` or `val`, enforced by argparse choices (`scripts/run.py:25`) — a `--split test` invocation exits non-zero. The enforcement lives in *this CLI*, not in `harness.evaluate_candidate` (issue #361), so never "helpfully" widen those choices. - `--n-trials` **defaults to 1**. On a stochastic target that is the degenerate case below; run.py prints a warning to stderr when it happens. - `--ks` picks the k values for pass^k; it defaults to `1..n_trials`, so `--n-trials 3` reports pass^1..pass^3. Any k above a task's trial count is omitted rather than reported as 0.0 (`loop.py:134-147`). - `CAPEVOLVE_WORKERS=N` generates rollouts through a thread pool (`harness.py:49-57`); scoring stays serial so the numbers match a serial run. Keep it at 1 if `run_target` is not thread-safe (shared scratch dir, one live container, module-global client) — `harness.py:225-227`. - A subset/triage eval (`ids=`) is **never** gateable: its `n_tasks` is the subset, so `coverage` reads 1.0 (`harness.py:229-237`). ## How much measurement do you need Two axes, and the trials axis is the one people get wrong. - **Trials.** Deterministic scorer + greedy decode: 1 trial is honest. Any sampling / temperature / tool nondeterminism: ≥3–4. Trials are only independent draws if the adapter **forwards the per-trial seed** — trial `k` runs with `seed = base_seed + k` (`harness.py:374`, `trials.py:10-13`) and the adapter contract requires passing it to a stochastic runner (`adapter.py:52-54`). An adapter that drops it gives you n identical copies: per-task `stderr` is 0, `pass^k` is exactly 0 or 1, and the whole apparatus looks healthy while measuring nothing. `cap-evolve check` can prove it: with `CAPEVOLVE_N_TRIALS=3 CAPEVOLVE_CHECK_TRIAL_PROBE=1` it fires two real rollouts at different seeds and warns if they are byte-identical (`core/cap_evolve/check.py:169-190`). It is opt-in because the probe costs real rollouts — run it once per adapter, and treat the warning as "every variance number here is fiction". Trials cost budget linearly, so spend them where variance actually threatens a decision — the val split the gate reads — not on every exploratory probe. - **Tasks.** `stats.stderr` returns 0.0 below 2 tasks and `combined_stderr`'s between-task term is 0 below 2 (`stats.py:28-30, 47-50`), so a 1-task val gives `stderr = 0`, a bar of 0, and the gate degenerates to strict ("any Δ>0 wins") with a logged warning (`gate.py:40-60`). Below roughly 5 val tasks the `k·SE` bar is dominated by sample size and is optimistic — issue #113. An empty val presents as `coverage 1.0` with `reward 0.0` (`loop.py:79-84`). **A one-task gain is not reliably bankable.** Under the shipped default (`mode: paired`, `k_se: 1.0`) a candidate that improves exactly one val task and changes nothing else has `Δ̄ == SE(Δ)` *algebraically*, so the strict `>` at `gate.py:176` is settled by floating-point representation — rejected at n=4, 8, 50, accepted at n=20, identical printed numbers. Issue #351, open; derivation in `references/concepts.md`. Do not read a rejection of a single-task fix as evidence the edit was bad — check how many tasks moved. ## What good vs bad looks like - **Good:** `n_trials ≥ 3` on a stochastic agent with the seed forwarded; `stderr` non-zero; `n_scored == n_tasks`; pass^k inspected alongside the mean. - **Bad:** a plausible low reward that is an infrastructure outage, not a capability measurement (check `coverage` first, always); single-trial scores feeding a significance gate; identical trial rewards across seeds; trusting a high mean when pass^k is low (the gain is fragile). ## References - `references/concepts.md` (125 lines) — the variance decomposition and the combined-SE formula, where these statistics break down on small samples (including the #351 `Δ̄ == SE` derivation), pass^k vs pass@k with their unbiased estimators, bootstrap CIs, where the test-split refusal is enforced, and sources. Load it when you need the statistics themselves rather than how to run an evaluation.