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/evographnpx skills add skillberry-ai/cap-evolve --skill evographgit 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/evograph)<a href="https://agentmods.dev/skills/skillberry-ai/cap-evolve/evograph"><img src="https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/evograph.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 | $0.00184 | $0.01218 |
| Opus 5 | $0.00092 | $0.00609 |
| Sonnet 5 | $0.00037 | $0.00244 |
| Haiku 4.5 | $0.00018 | $0.00122 |
Grade A, and why
evograph 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evograph — DEPRECATED
Do not select algorithm_skill: evograph for a new run. Use agent-optimize (agent mode) or
hill-climb / gepa / skillopt (deterministic). This file stays so an existing evograph run dir
is still readable and so the wiki file-format contract has an owner until it moves.
Why it is deprecated
evograph advertised one distinctive capability — a collaborative weakness graph with one solver agent per weakness, merged into a shared candidate each round. Measured against its four siblings, that capability is not distinctive and the part that was distinctive was a defect:
- The fan-out already exists, gated properly.
agent-optimizefans out N sibling candidates from the same parent, one diagnosed failure cluster each, every sibling in its own working copy (a git worktree when the capability is in git), gated one at a time with a re-gate after each accept so several fixes accumulate into one lineage honestly. That is evograph's round, minus the flaws below. The clustering itself isphases/diagnose's job in both cases. - Acceptance was never held out. evograph kept a merge on a raw delta over a frozen 3-task
subset of train, self-reported by the solver subagent that made the edit — no val split, no
standard error, no
Δ > k·SE. Betweenbaselineandfinalizean evograph run took no held-out measurement at all, so the sealed test number was the first honest signal anyone saw. Whole-round revert existed only as a one-round-late substitute for the gate it lacked; wire the real gate and there is nothing left for it to catch. - What remains unique is an output format, not a search strategy. The run-dir
wiki/is genuinely useful, but the dashboard renders the Weakness-graph tab fromwiki/presence alone, for any algorithm that writes the format (core/cap_evolve/dashboard.py). An output contract does not earn a second agent-mode algorithm that users must choose between.
There is no deterministic engine
What ships with it
9 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 · 83 lines · 184 tokens per session scan A 297cf97e1adb
evograph is a skill published in the GitHub repository skillberry-ai/cap-evolve (47 stars, last pushed 4d ago), licensed Apache-2.0. It adds 184 tokens to every session and 1,218 once invoked, about $0.0009 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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