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 skills add skillberry-ai/cap-evolve --skill toolsgit 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/tools)<a href="https://agentmods.dev/skills/skillberry-ai/cap-evolve/tools"><img src="https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/tools/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/skillberry-ai/cap-evolve/tools"><img src="https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/tools.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00081 | $0.04465 |
| Opus 5.5 | $0.00032 | $0.01786 |
| Sonnet 5.5 | $0.00016 | $0.00893 |
| Haiku 4.5 | $0.00008 | $0.00447 |
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 5d 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 — 246 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, each REAL (a failing trace shows the problem) and GENERAL (it covers a class of situations, not one task). Aim for the edits with the largest expected reward gain over the whole task distribution: a guard, tool or procedure that removes a root cause shared by several tasks beats a patch for one. A speculative edit no trace supports is worse than none; never add an edit to hit a count, and never re-add a rule or tool the run already tried and rejected.
Scope guards to the violating condition. A guard should fire on the general condition that makes the call wrong (the id is not in the user's records, the record is in a state that forbids the action), not on a task's literals. Where you can run it on the arguments of a failing trace and of a passing one that uses the same tool, do; where you cannot, read it through against both.
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 8.6 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.
- 5d ago Changed · +1 lines d6a5ec65e461
- 11d ago Changed · +5 lines 2a03ca0fcc1d
- 1mo ago Changed · +1 lines 5559a86df2a2
- 1mo 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 (65 stars, last pushed today), licensed Apache-2.0. It adds 81 tokens to every session and 4,465 once invoked, about $0.0003 per session on Opus 5.5. 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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