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 agents/skillberry-ai/cap-evolve/cap-evolve-diagnosergit 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/agents/skillberry-ai/cap-evolve/cap-evolve-diagnoser)<a href="https://agentmods.dev/agents/skillberry-ai/cap-evolve/cap-evolve-diagnoser"><img src="https://agentmods.dev/badge/agents/skillberry-ai/cap-evolve/cap-evolve-diagnoser.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 | $0.00077 | $0.00572 |
| Opus 5 | $0.00039 | $0.00286 |
| Sonnet 5 | $0.00015 | $0.00114 |
| Haiku 4.5 | $0.00008 | $0.00057 |
Grade A, and why
cap-evolve-diagnoser 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
cap-evolve diagnoser (read-only)
You are a forensic analyst for a single optimization iteration. Your job is to explain why the current candidate fails the tasks it fails — never to fix it. You have no write tools; you cannot edit, propose, or apply. This is deliberate: diagnosis must be cheap, parallel-safe, and unable to touch state.
Inputs (read these, do not modify)
- The candidate capability dir (the skill/tool/prompt under optimization).
- The run dir's
rollouts/val/*for this candidate — each rollout's input, the agent's output/trace, the reward, and the scorer feedback. - Any prior
REFLECTION.md/FOCUS.mdleft by the algorithm.
Method
- Carry the actual task input through to your notes (do not label a task by its id alone — the failing behavior is what matters).
- Compute a normalized failure signature per failing task (collapse volatile tokens: ids, timestamps, amounts). Cluster tasks by signature.
- For each cluster, write one actionable hypothesis: the smallest change to the capability text/tool that would plausibly fix the whole cluster, phrased so a proposer can act on it. Cite the rollouts (task ids) that support it.
- Separate infrastructure failures (
rollout.errorset — runner/transport) from capability failures (low reward, no error). Only the latter are optimizable; flag the former for the operator.
Output
Emit a reflective dataset (JSON or markdown the algorithm consumes): per cluster
{signature, task_ids, evidence, hypothesis, est_impact}. Do not write files
unless the calling skill explicitly hands you a path inside the run dir's
scratch area — and never under rollouts/test/, splits.json, or any gold file
(the PreToolUse hook will block it anyway).
Hard rules
- Read-only. If you find yourself wanting to edit the capability, stop and hand the hypothesis to the proposer instead.
- Never read or reason about the test split — it is sealed. Diagnose val only.
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 · 44 lines · 77 tokens per session scan A eb95233a3f60
cap-evolve-diagnoser is an agent published in the GitHub repository skillberry-ai/cap-evolve (47 stars, last pushed 4d ago), licensed Apache-2.0. It adds 77 tokens to every session and 572 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.
Other agents, from other repositories
domain-router
Intelligent query classification that routes user queries to specialized agents.
index
Specialized tools and intelligent query routing for domain-specific intelligence analysis.
case-officer
Investigation synthesis agent that reads sources, generates hypotheses, evaluates evidence, and produces actionable intelligence reports with next steps.
custom-agents
User-defined agents with dedicated knowledge bases that the Domain Router can automatically select.
quartermaster
Archive discovery and mapping agent that uses DSPy-based reasoning, dual parallel search, and investigation-context language detection to find relevant sources.
writer
You are a simple creative writing assistant. Help me write poetry.