Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/skillberry-ai/cap-evolve/implement-and-checknpx skills add skillberry-ai/cap-evolve --skill implement-and-checkgit clone --depth 1 https://github.com/skillberry-ai/cap-evolveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/skillberry-ai/cap-evolve/implement-and-check)<a href="https://agentmods.dev/skills/skillberry-ai/cap-evolve/implement-and-check"><img src="https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/implement-and-check.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00094 | $0.02137 |
| Opus 5 | $0.00047 | $0.01069 |
| Sonnet 5 | $0.00019 | $0.00427 |
| Haiku 4.5 | $0.00009 | $0.00214 |
Grade A, and why
implement-and-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
-
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 asctx; capture output + trace + tool calls + cost. Do not score here. Forwardseedif the runner is stochastic; setRollout.erroron 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 yieldingctx),apply(candidate_dir, edits=None)(back-compat inject),trajectories(split, ctx=None)andrunner_model()(both defaultNone). Three optional fast paths are not on the base class at all — the harness feature-detects them withhasattrand uses them only if you define them:run_batch(tasks, ctx, *, seed)(drive a benchmark's own batch runner instead ofrun_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.mdis the full contract, including the shown-onlymetricscatalogscore()may return.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 133 lines · 94 tokens per session scan A b717cc94fc0b
implement-and-check is a skill published in the GitHub repository skillberry-ai/cap-evolve (50 stars, last pushed today), licensed Apache-2.0. It adds 94 tokens to every session and 2,137 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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