baseline

baseline is a skill for Claude Code, Codex from skillberry-ai/cap-evolve. It costs 72 tokens per session (1,074 once invoked), scanned A, original, Apache-2.0.

A starting-point measurement that freezes the task split and scores the unchanged agent before optimization begins. The validation split is the portion used for tuning; the test split is kept untouched for the final check.

In plain words
What is it for?
Use it to create a run, measure the initial agent, and see whether there is still room for improvement.
Why use it?
It creates a fixed, fair reference for judging later changes and catches a broken setup before results are recorded.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/skillberry-ai/cap-evolve/baseline
Any agent
npx skills add skillberry-ai/cap-evolve --skill baseline
Clone the repo
git clone --depth 1 https://github.com/skillberry-ai/cap-evolve

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for baseline

README.md
[![agentmods](https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/baseline.svg)](https://agentmods.dev/skills/skillberry-ai/cap-evolve/baseline)
Your own site
<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>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,074 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash 33b063450bee, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/_bootstrap.py, scripts/abstract.py, scripts/check.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/phases/baseline/SKILL.md · 76 lines

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) and headroom_verdict: saturated means 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; ok means proceed. The same verdict is logged as a headroom event 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 a splits_warning saying 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.

Read the full file on GitHub · 76 lines

Files

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.

Changes

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.

  1. 3d ago First seen · 76 lines · 72 tokens per session scan A 33b063450bee

Subscribe to this mod's changes

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.