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/toolsnpx skills add skillberry-ai/cap-evolve --skill toolsgit 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 | $0.00081 | $0.04199 |
| Opus 5 | $0.00041 | $0.02099 |
| Sonnet 5 | $0.00016 | $0.00840 |
| Haiku 4.5 | $0.00008 | $0.00420 |
Grade A, and why
tools 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.
How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capability: tools (full control)
This capability treats the agent's entire tool surface as the optimizable
artifact. It applies when the agent owns its tools — it implements the handlers,
defines the wire schema, and controls every caller — so names, descriptions,
parameter docs, in-description examples, the JSON Schema, and the implementation
code are all fair game. (When the tools come from an external server you can only
re-describe, not re-implement: that is mcp-tool, whose policy is tightened to
documentation-only edits.)
What you can change here
The tool's documentation AND its return value are what the agent SEES — make both
clear and recovery-oriented. The doc surface (description, important-notes,
per-param, error/Raises text, examples) drives which tool the model calls and
how it fills the arguments; the return value (and especially the error text) steers
the next turn. Confirm which parts of a docstring your runtime actually SENDS before
writing into it — some frameworks discard whole sections, and guidance written into
that void does nothing (references/field-notes.md §1).
Ship MULTIPLE fixes per iteration — but every one must be REAL (targets a currently-failing task), SAFE (cannot change a passing task's behavior), and VERIFIED (proven to fix its target). Several such fixes beat a long list that includes a speculative edit: one edit that regresses a passing task sinks the whole candidate at the val gate. Never add an edit to hit a count, and never re-add a rule or tool the run already tried and rejected.
Per-change SAFETY (the rule that makes multi-change work). Scope every guard to fire ONLY on the exact violating condition, and check its blast radius: run it on the args of 1–2 currently-PASSING tasks that use the same tool and confirm it does NOT fire. A guard that fires on a passing task is a regression — rescope or drop it.
Pick the lever by failure type
Each item is an edit class. In ONE pass, apply EVERY class the traces call for — a validation wrapper AND a loop tool AND enriched returns/errors AND doc fixes across all implicated tools can and should ship in the same candidate. The in-body guard is the default strong move; reach for a documentation edit only after asking "can this rule be code in the existing body instead?"
What ships with it
11 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.
- meta.yaml 353 B
- references/concepts.md 11 KB
- references/doc-contract.md 2.8 KB
- references/examples.md 24 KB
- references/field-notes.md 5.6 KB
- references/optimizer-playbook.md 2.6 KB
- references/pitfalls.md 8.4 KB
- scripts/_bootstrap.py 3.6 KB runs code
- scripts/abstract.py 1.4 KB runs code
- scripts/check.py 3.2 KB runs code
- scripts/run.py 883 B runs code
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 · 239 lines · 81 tokens per session scan A a46d8e5e4ba9
tools is a skill published in the GitHub repository skillberry-ai/cap-evolve (47 stars, last pushed 2d ago), licensed Apache-2.0. It adds 81 tokens to every session and 4,199 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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