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 LeeJuOh/claude-code-zero --skill auto-optimizegit clone --depth 1 https://github.com/LeeJuOh/claude-code-zeroWrote 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/leejuoh/claude-code-zero/auto-optimize)<a href="https://agentmods.dev/skills/leejuoh/claude-code-zero/auto-optimize"><img src="https://agentmods.dev/badge/skills/leejuoh/claude-code-zero/auto-optimize/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/leejuoh/claude-code-zero/auto-optimize"><img src="https://agentmods.dev/badge/skills/leejuoh/claude-code-zero/auto-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 45 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00054 | $0.03568 |
| Opus 5.5 | $0.00022 | $0.01427 |
| Sonnet 5.5 | $0.00011 | $0.00714 |
| Haiku 4.5 | $0.00005 | $0.00357 |
Grade A, and why
auto-optimize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch for Skills
Bundled with
skill-creator-pro. This skill reads design guidance from its sibling via${CLAUDE_SKILL_DIR}/../skill-creator-pro/. The two skills ship together in theskill-creator-proplugin and cannot be installed separately.
Most skills work about 70% of the time. The other 30% you get garbage. The fix isn't to rewrite the skill from scratch. It's to let an agent run it dozens of times, score every output, and tighten the prompt until that 30% disappears.
This skill adapts Andrej Karpathy's autoresearch methodology to Claude Code skills, enhanced with:
- Reflection-driven mutation -- reads failed outputs to diagnose WHY they failed, then proposes targeted fixes. The difference between throwing darts blindfolded and throwing them with your eyes open.
- Per-eval tracking -- tracks each eval separately so improving one at another's expense gets caught.
- Structured archive -- session-surviving changelog that new sessions read to avoid repeating failed experiments.
- Stuck detection -- recognizes when incremental mutations hit a wall and escalates strategy.
The Core Job
Take any existing skill, define what "good output" looks like as binary yes/no checks, then run an autonomous loop that:
- Generates outputs from the skill using test inputs
- Scores every output against the eval criteria
- Reflects on failed outputs -- reads the actual failures and diagnoses the root cause
- Mutates the skill prompt to fix the diagnosed issue
- Keeps mutations that improve the score, discards the rest
- Repeats until the score ceiling is hit or the user stops it
Output: An improved SKILL.md + results.json log + structured changelog.md + a live HTML dashboard.
Before Starting: Gather Context
STOP. Do not run any experiments until the fields below are confirmed with the user. Ask for any missing fields before proceeding.
- Target skill -- Which skill to optimize? (exact path to SKILL.md)
- Test inputs -- 3-5 different prompts/scenarios to test with. Variety matters -- pick inputs that cover different use cases so we don't overfit to one scenario.
- Runs per input (R) -- How many times each test input runs in one experiment. Default: 2 (3 inputs x 2 = 6 runs). More runs = more reliable scores but slower and costlier.
- Budget cap -- Optional. Max number of experiment cycles before stopping. Default: no cap (runs until you stop it).
What ships with it
3 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.
- 9d ago Changed · -75 lines · -11 tokens per session 7f4272ebfe40
- 23d ago Changed · -1 lines 0defe2c874ce
- 1mo ago First seen · 381 lines · 65 tokens per session scan A a530ce92e6d4
auto-optimize is a skill published in the GitHub repository LeeJuOh/claude-code-zero (52 stars, last pushed 3d ago), licensed MIT. It adds 54 tokens to every session and 3,568 once invoked, about $0.0002 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 2026-08-30.
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