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 skills add taneltaluri/evolve-skill --skill evolve-skillgit clone --depth 1 https://github.com/taneltaluri/evolve-skillWrote 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/taneltaluri/evolve-skill/evolve-skill)<a href="https://agentmods.dev/skills/taneltaluri/evolve-skill/evolve-skill"><img src="https://agentmods.dev/badge/skills/taneltaluri/evolve-skill/evolve-skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/taneltaluri/evolve-skill/evolve-skill"><img src="https://agentmods.dev/badge/skills/taneltaluri/evolve-skill/evolve-skill.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00128 | $0.04013 |
| Opus 5.5 | $0.00051 | $0.01605 |
| Sonnet 5.5 | $0.00026 | $0.00803 |
| Haiku 4.5 | $0.00013 | $0.00401 |
Grade A, and why
evolve-skill 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 1mo 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 — 410 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evolve Skill
A measurement-first skill optimizer. Inspired by Karpathy's
autoresearchand thedarwin-skillproject, but rebuilt around one conviction: if you cannot trust your measurement, you cannot trust your optimization.Every decision this skill makes is gated by whether the measurement is reliable enough to justify it.
What This Skill Is Not
This is not a formatting linter. This is not a prompt rewriter. This is not an autonomous agent that will rewrite your skills in the background.
This is a disciplined experiment loop with strict gates around scoring noise, overfitting, and function drift — the three failure modes that make most "auto-optimize" tools produce worse skills over time.
If you want a quick skill tidy-up, use a simpler tool. If you have 20+ skills and need to know which ones are actually improving, this is for you.
Core Philosophy — The Three Gates
Every optimization cycle passes through three gates. If any gate fails, the cycle stops or rolls back.
Gate 1 — Measurement Stability (can we trust the score?)
Before any optimization runs, score the baseline skill three independent times using three separate sub-agents. Compute the standard deviation. If SD > 2.0 points, the rubric is too loose and no optimization may proceed until the rubric is tightened.
A ratchet mechanism with unreliable measurement is not a ratchet — it is a random walk pretending to be progress.
Gate 2 — Effect Size (is this improvement real?)
A keep decision requires Δscore ≥ max(3, 2×SD) where SD is the measurement noise from Gate 1. A skill that "improved" by 1.5 points when noise is ±2 points did not improve.
Gate 3 — Function Preservation (is it still the same skill?)
Before and after every change, extract the skill's core functions as a bullet list (via a sub-agent). If function overlap drops below 80%, the change is rejected regardless of score. A higher-scoring skill that silently lost a capability is worse, not better.
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.
- examples/example-results.tsv 1.7 KB
- examples/example-walkthrough.md 5.0 KB
- scripts/allocate.py 2.8 KB runs code
- scripts/calibrate.py 3.5 KB runs code
- scripts/ratchet.py 3.4 KB runs code
- scripts/score.py 3.5 KB runs code
- scripts/semantic_diff.py 3.6 KB runs code
- templates/playbook.template.md 1.1 KB
- templates/result-card.html 4.7 KB
- templates/test-prompts.template.json 1.9 KB
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.
- 1mo ago First seen · 410 lines · 128 tokens per session scan A 8fdf46e2e1a7
evolve-skill is a skill published in the GitHub repository taneltaluri/evolve-skill (2 stars, last pushed 6mo ago), licensed MIT. It adds 128 tokens to every session and 4,013 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-31.
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