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/baselinenpx skills add skillberry-ai/cap-evolve --skill baselinegit 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/baseline)<a href="https://agentmods.dev/skills/skillberry-ai/cap-evolve/baseline"><img src="https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/baseline.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 | $0.00072 | $0.01074 |
| Opus 5 | $0.00036 | $0.00537 |
| Sonnet 5 | $0.00014 | $0.00215 |
| Haiku 4.5 | $0.00007 | $0.00107 |
Grade A, and why
baseline 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 3d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
baseline — freeze splits, score the seed
baseline is the first phase that touches data, so it owns the run's one
irreversible decision: the split. It writes splits.json once (seeded), scores
the unmodified seed capability on val, and records that score as the bar every
algorithm must beat.
Run implement-and-check first. baseline re-runs that check itself and exits
non-zero before creating a run dir if it is red — a split frozen against a broken
adapter poisons every number measured afterwards.
Why it matters
- Fair comparison point. Every algorithm hill-climbs against the baseline val score; a candidate that does not beat it is not progress.
- Headroom. The printed JSON carries
headroom(1 - val) andheadroom_verdict:saturatedmeans the seed is already at the ceiling and further iterations buy noise — stop;floor(val at 0) usually means a mis-wired adapter rather than a hard task — re-check before spending budget;okmeans proceed. The same verdict is logged as aheadroomevent so the orchestrator can stop on it with no human reading the number.
Splitting choices
- Seeded ratio split (default
0.5 / 0.25 / 0.25): deterministic given--seed. Reproducible runs partition identically. - Pinned split (
--split-ids): a JSON{train,val,test}of ids — use a benchmark's official split, or set all three equal to fit the whole set with no holdout (the test number is then a fit metric, not a held-out result; the run dir records asplits_warningsaying so). - A ratio split that leaves val or test empty is refused — the gate would
have nothing to decide on and the sealed test number would cover no tasks.
Below 5 val tasks baseline warns: the gate's bar is optimistic at that
n, and a candidate that improves exactly one val task cannot reliably clear it at all (issue #351), so size val with the decisions it has to make in mind.
Reusing a prior baseline (--reuse-baseline PRIOR_RUN_DIR)
Re-scoring the seed is wasteful when the split + seed are unchanged.
--reuse-baseline <prior run_* dir> (spec key reuse_baseline) copies that run's
splits.json, baseline.json, seed snapshot and seed val rollouts into the fresh
run dir and skips the baseline eval; the copied test_used flag is reset so this
run can still finalize on test exactly once. --resume is the same-run variant:
reopen an existing run dir, skip the eval when baseline.json is already there.
Budget flags (--max-iterations, --stall, --max-usd, …) are accepted here
because the run dir owns the budget and later phases read it from there.
What ships with it
6 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.
- 3d ago First seen · 76 lines · 72 tokens per session scan A 33b063450bee
baseline is a skill published in the GitHub repository skillberry-ai/cap-evolve (47 stars, last pushed 3d ago), licensed Apache-2.0. It adds 72 tokens to every session and 1,074 once invoked, about $0.0004 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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