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/using-cap-evolvenpx skills add skillberry-ai/cap-evolve --skill using-cap-evolvegit clone --depth 1 https://github.com/skillberry-ai/cap-evolveWhat 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 | $0.00188 | $0.00629 |
| Opus 5 | $0.00094 | $0.00315 |
| Sonnet 5 | $0.00038 | $0.00126 |
| Haiku 4.5 | $0.00019 | $0.00063 |
Grade A, and why
using-cap-evolve scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
using-cap-evolve — the router
The front door: it works out where the user is and hands off, running no phase and
editing nothing. Boundary: this router picks the door, orchestrate drives the run.
Routing decision
Run from the user's project dir; S is the absolute path of the directory you loaded this
SKILL.md from — the one location always known here (no env var is set for a plugin install):
S=<this skill's own directory>; python "$S/scripts/run.py" --base .capevolve
Follow next; pass reason on to the user. Two things the JSON cannot say for itself:
- On a fresh request go through
intake, and if an input it needs is missing, ask the user for it rather than inventing one (intakeowns that rule). - An existing run is never restarted from zero: interrupted →
cap-evolve run --resume; sealed and the user wants another attempt →cap-evolve run --reuse-baseline <run dir>.
Three ways to run — orchestrate has the detail
- Phase chain —
/cap-evolve:<phase>turn by turn, so each step is inspected. - Deterministic —
cap-evolve run --spec .capevolve/project/capevolve.yamlsequences the check gate → baseline → algorithm → finalize → report. It presumes intake already happened; it does not run intake. - Agent handoff — with
orchestration_mode: agent,cap-evolve runstops after baseline and hands the loop back to you; no sealed-test number until you finalize.
No plugin, or a non-Claude host: follow RUN.md step by step. Same engine, same rules.
What ships with it
5 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.
- 2d ago First seen · 38 lines · 188 tokens per session scan A b118e1f117c4
using-cap-evolve is a skill published in the GitHub repository skillberry-ai/cap-evolve (47 stars, last pushed 2d ago), licensed Apache-2.0. It adds 188 tokens to every session and 629 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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