tools

A guide for improving an agent’s own tools, including their names, instructions, input formats, outputs, and implementation code. “Own tools” are tools the agent’s project defines and controls, rather than tools supplied by another service.

In plain words
What is it for?
Use it to improve tool descriptions, schemas, error messages, return values, and handlers when optimizing an agent’s toolset.
Why use it?
Agents can choose the wrong tool, fill in arguments incorrectly, repeat calls, or struggle with unclear tool results. This helps identify and fix those causes.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/skillberry-ai/cap-evolve/tools
Any agent
npx skills add skillberry-ai/cap-evolve --skill tools
Clone the repo
git clone --depth 1 https://github.com/skillberry-ai/cap-evolve

Made for: Claude Code, Codex.

Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,199 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash a46d8e5e4ba9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/_bootstrap.py, scripts/abstract.py, scripts/check.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/capabilities/tools/SKILL.md · 239 lines

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?"

Read the full file on GitHub · 239 lines

Changes

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.

  1. 2d ago First seen · 239 lines · 81 tokens per session scan A a46d8e5e4ba9

Subscribe to this mod's changes

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.