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/skill-packagenpx skills add skillberry-ai/cap-evolve --skill skill-packagegit 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/skill-package)<a href="https://agentmods.dev/skills/skillberry-ai/cap-evolve/skill-package"><img src="https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/skill-package.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 | $0.00112 | $0.01581 |
| Opus 5 | $0.00056 | $0.00790 |
| Sonnet 5 | $0.00022 | $0.00316 |
| Haiku 4.5 | $0.00011 | $0.00158 |
Grade A, and why
skill-package 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capability: skill package
The artifact is a whole skill directory — SKILL.md plus references/, scripts/
and assets/ — and all of it is editable: materialize() exposes every file as
a component, apply() can rewrite or CREATE one (a new bundled script included), and
validate() checks the result against the skill-creator authoring rules
(first-party sources in references/concepts.md).
What you can change (highest leverage first)
Pick the lever that fixes the biggest failure cluster; depth is in the references.
- The
description/ trigger — the only text loaded before the skill fires, so the single highest-leverage edit. Third person; state what it does AND when to use it; use the keywords a user would actually say. Lean slightly pushy for under-trigger, tighten the boundary and name near-miss cases for over-trigger, and front-load the key use case (hosts truncate the listing — 1,536 chars on Claude Code by default). Ex: "Formats data" → "Exports records to CSV. Use when the user asks to export or download a table." Playbook + the measurable loop:references/description-optimization.md. - A skipped step → a bundled script (the determinism lever). Prose is only
likely to be followed; code that runs is repeatable. When the traces show the
agent skipping a step, re-deriving the same helper, or doing a deterministic
transform by hand, write it into
scripts/and make the body invoke it by command line. Write real, working code — never...or a docstring-only stub — give it a--self-checkentry point (validate()runs it, so a broken script is caught before any rollout is paid for), and say execute, don't read: a script's source never enters the agent's context, only its output. - The body — improve clarity and altitude, delete dead weight, fix the instruction the agent misreads. The body loads on every trigger and stays in context all session — a recurring cost — so keep it ≤500 lines (enforced), imperative, and explain a rule's why briefly instead of piling on ALL-CAPS MUSTs.
- References — move mutually-exclusive or rarely-co-used detail into
references/*.md. Keep them one level deep (a ref must not point at another ref — the agent may read only part of it), link each directly from SKILL.md with a pointer saying what it holds and when to load it, and give a long ref (>300 lines) a table of contents at the very top, above any orientation prose — the check is positional because a TOC the head-reader never reaches is not a TOC. Multiple variants/domains → one ref per variant (references/aws.md,gcp.md, …) plus a selection body, so only one is read. - Assets —
assets/holds files the skill emits (templates, icons, fonts), not context the agent reads. Edit one only when the skill's output depends on it.
What ships with it
10 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 344 B
- references/anti-patterns.md 2.8 KB
- references/concepts.md 4.8 KB
- references/description-optimization.md 3.6 KB
- scripts/_bootstrap.py 3.6 KB runs code
- scripts/abstract.py 27 KB runs code
- scripts/check.py 9.4 KB runs code
- scripts/run.py 819 B runs code
- scripts/token_report.py 4.2 KB runs code
- scripts/trigger_eval.py 7.6 KB 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.
- 4d ago First seen · 99 lines · 112 tokens per session scan A 1fff7350f562
skill-package is a skill published in the GitHub repository skillberry-ai/cap-evolve (50 stars, last pushed today), licensed Apache-2.0. It adds 112 tokens to every session and 1,581 once invoked, about $0.0006 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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