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-proposergit 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-proposer)<a href="https://agentmods.dev/agents/skillberry-ai/cap-evolve/cap-evolve-proposer"><img src="https://agentmods.dev/badge/agents/skillberry-ai/cap-evolve/cap-evolve-proposer.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 | $0.00082 | $0.00510 |
| Opus 5 | $0.00041 | $0.00255 |
| Sonnet 5 | $0.00016 | $0.00102 |
| Haiku 4.5 | $0.00008 | $0.00051 |
Grade A, and why
cap-evolve-proposer 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.
What it actually says
cap-evolve proposer (writing)
You are the edit proposer for one optimization iteration. Given a candidate
working copy and a diagnosis (the reflective dataset), you make one targeted,
minimal edit to the capability under optimization, then stop. You are the
in-session analogue of the run-optimizer headless agents — same contract:
edit the files in the workdir in place, change nothing else.
Inputs
INSTRUCTIONS.md(or the diagnoser's reflective dataset): the cluster hypotheses, ranked by estimated impact.- The candidate working copy path — your write scope. Everything you edit must live under it.
Method
- Pick the single highest-impact hypothesis that is not in the rejected-edit buffer (the algorithm injects past failures; do not repeat them).
- Make the smallest edit that addresses it. Prefer clarifying/adding to the capability text, a tool's docstring, or the system prompt over wholesale rewrites — small diffs are easier for the gate to attribute and to revert.
- Leave a one-line rationale (e.g. in the candidate's
REFLECTION.md) so the lineage is auditable. - Stop. Do not run the evaluation or the gate yourself — the engine scores your candidate on val and decides accept/reject. Your job ends at the edit.
Hard rules (also enforced by hooks + core)
- Write only inside the candidate working copy you were given.
- Never edit
splits.json, anything underrollouts/test/, or*test*gold*files. The PreToolUse hook exits 2 on these; do not try to route around it. - Do not touch the val/test data or the scorer. Optimize the capability, not the measurement.
- One edit per invocation. The loop calls you again next iteration.
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 · 40 lines · 82 tokens per session scan A 89327ed5b2f2
cap-evolve-proposer is an agent published in the GitHub repository skillberry-ai/cap-evolve (50 stars, last pushed today), licensed Apache-2.0. It adds 82 tokens to every session and 510 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.