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/gepanpx skills add skillberry-ai/cap-evolve --skill gepagit 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/gepa)<a href="https://agentmods.dev/skills/skillberry-ai/cap-evolve/gepa"><img src="https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/gepa.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 5.1 | $0.00111 | $0.02630 |
| Opus 5 | $0.00056 | $0.01315 |
| Sonnet 5 | $0.00022 | $0.00526 |
| Haiku 4.5 | $0.00011 | $0.00263 |
Grade A, and why
gepa 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 6d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gepa — sample-efficient reflective Pareto search
algorithms/hill-climb owns the mechanics every algorithm shares: parent →
proposal → val gate → commit, specified once in
algorithms/hill-climb/references/run-step.md. Read that first. This page states
only what GEPA (Agrawal et al., 2025) does differently, and why those
differences are the paper's actual contribution rather than decoration. A thin
wrapper over cap_evolve.gepa.gepa_loop.
The two mechanisms, and why removing either turns GEPA back into hill-climb
1. The parent is sampled from per-instance winners, not from the global best. A mean is a lossy summary. A candidate that fixes one genuinely hard task while regressing three easy ones has a worse mean than the incumbent, so a best-parent rule discards it — and with it the only text in the pool that has ever solved that task. GEPA instead scores per val instance and samples frequency-weighted over candidates that (co-)win at least one, so specialists and stepping-stones stay reachable as parents while their mean is still behind. That is the quality-diversity argument (MAP-Elites): keep the set that covers the task distribution, not the single champion. Sampling is stochastic and seeded, so the exploration is reproducible.
2. A cheap train minibatch pre-gates the expensive val evaluation. Rollouts
dominate cost and a full-val eval costs |val| · n_trials of them. Most
proposals are bad; paying full price to find that out is what makes naive
reflective search unaffordable, and GEPA's headline "~35× fewer rollouts" comes
almost entirely from not paying it. So parent and child are evaluated on the
same small train minibatch (2 · minibatch-size rollouts, eval-cached) and
the child is dropped unless sum(child) > sum(parent). The minibatch never
decides acceptance — it decides whether acceptance is worth measuring.
A side benefit of (2): reflection reads train traces, so the proposer never sees the split its gate is computed on.
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.
- 6d ago First seen · 173 lines · 111 tokens per session scan A 962bd00bb617
gepa is a skill published in the GitHub repository skillberry-ai/cap-evolve (50 stars, last pushed yesterday), licensed Apache-2.0. It adds 111 tokens to every session and 2,630 once invoked, about $0.0006 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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