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/diagnosenpx skills add skillberry-ai/cap-evolve --skill diagnosegit 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/diagnose)<a href="https://agentmods.dev/skills/skillberry-ai/cap-evolve/diagnose"><img src="https://agentmods.dev/badge/skills/skillberry-ai/cap-evolve/diagnose.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 5.1 | $0.00097 | $0.02436 |
| Opus 5 | $0.00048 | $0.01218 |
| Sonnet 5 | $0.00019 | $0.00487 |
| Haiku 4.5 | $0.00010 | $0.00244 |
Grade A, and why
diagnose 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 yesterday.
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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
diagnose — failures into actionable side information
A scalar reward says how much a candidate failed; it does not say why, and "why" is the only thing an editor can act on. Where RL back-propagates a scalar into weights, natural-language feedback back-propagates into prompt/tool/skill edits — and the richer it is, the larger the update extractable from a handful of rollouts.
What it produces
{
"split": "val", "tag": "cand_003",
"reflective_dataset": [
{"task_id": "t12", "Inputs": "<what the task asked>",
"Generated Outputs": "<what the agent produced>",
"Feedback": "<the scorer's diagnosis>",
"Trajectory": "<path to this task's full trace>"}
],
"clusters": [
{"signature": "confirm write", "tasks": ["t12", "t19"], "score_lost": 1.6,
"tag": "BEHAVIORAL", "blast_radius": ["t3", "t7"]}
],
"kept_good": ["t1", "t4"]
}
scripts/run.py emits everything except tag (one of KNOWLEDGE, BEHAVIORAL,
DECISION / PERMISSION, CAPABILITY-GAP) and blast_radius, which it leaves null
because they need judgement — filling them in is the work below. kept_good is the
set the gate's no-regression check protects.
What counts as a failure
Not only zero-score tasks. Three kinds are real lost score and routinely missed: partial credit (scored e.g. 0.5 because one part of the action was wrong), communication / omission (the action happened but the required information was never reported or confirmed), and near-miss (≈0.7–0.9, one small correct change from a pass — the cheapest marginal gain per edit, easiest to overlook while staring at the zeros).
Separate always-failing (mean ≈ 0 — a root-cause fix) from flaky (0 < mean < 1 —
a consistency fix; find what the passing trials do and make it reliable). The reward
is the honest signal: a per-task Feedback line comes from the last trial and
can disagree with the graded mean.
Where the trace comes from
The rollout record supplies the score and the feedback; the trajectory supplies the failure site, and the site is half of the cluster key. The runner owns the trace format, so never assume one — the location is asked for, not guessed:
What ships with it
7 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.
- yesterday Changed · +16 lines 206b9cead4b7
- 5d ago First seen · 170 lines · 97 tokens per session scan A 154985cfc7b0
diagnose is a skill published in the GitHub repository skillberry-ai/cap-evolve (50 stars, last pushed today), licensed Apache-2.0. It adds 97 tokens to every session and 2,436 once invoked, about $0.0005 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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foundry-config-setup
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jetson-diagnostic
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nemo-automodel-launcher-config
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agile-product-owner
../../../product-team/agile-product-owner/skills/agile-product-owner/SKILL.md.
agent-memory
../../../engineering/agent-memory/skills/agent-memory/SKILL.md.