Auto Skill Improver for OpenClaw — Benchmark-Driven Agent Skill Optimisation
Test and improve your SKILL.md files, agent personas, and multi-agent coordination with empirical measurement. Inspired by Karpathy's autoresearch.
How It Works with OpenClaw
- 1Classify — the tool identifies your OpenClaw skill type (research agent, coding agent, orchestrator, etc.)
- 2Benchmark — it builds a test suite that exercises your SKILL.md instructions against real agent scenarios
- 3Mutate — one instruction change at a time, each tested against the benchmark
- 4Keep or discard — only mutations that measurably improve agent output survive
Get the Guide File
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Step-by-Step: Set Up Auto Skill Improver in OpenClaw
Download the quickstart file
Enter your email in the form above to download the OpenClaw quickstart file.
Give the file to any OpenClaw agent
Paste it in chat or attach the file directly.
The agent clones the repo and sets up
Auto Skill Improver is installed automatically from the instructions in the file.
Point it at any SKILL.md file in your workspace
The tool targets your agent skill file and begins setup.
Review baseline, run mutations
The agent benchmarks each change automatically — only improvements are kept.
The improved SKILL.md replaces the original
Once the benchmark saturates, your optimised SKILL.md is ready.
Why Most OpenClaw Skill Iteration Fails
You edit your SKILL.md. The agent persona sounds sharper. You redeploy. But the agent's actual performance didn't measurably improve. Most skill editing is editorial — rewriting based on intuition rather than evidence. Auto Skill Improver treats OpenClaw skill iteration as empirical, not editorial.
Agent Vibes
- ✗Reword SKILL.md, hope the agent performs better
- ✗No baseline — no way to know if agent behaviour actually improved
- ✗Multiple persona changes at once hide what actually helped
- ✗Subjective evaluation: 'the agent seems smarter'
Agent Science
- ✓Establish a measurable baseline before any SKILL.md changes
- ✓Mutate one instruction at a time
- ✓Run the same benchmark before and after each change
- ✓Keep only what scores higher — discard the rest
What It Finds in OpenClaw Skills
The tool surfaces structural problems in your SKILL.md files and agent configurations that are invisible during manual editing — issues that silently degrade multi-agent performance.
Ambiguous Output Contracts
Vague success criteria that let agents produce wildly different outputs on each run.
Missing Fallback Behaviour
No defined recovery path when a tool call fails or agent coordination breaks down.
Conflicting Instruction Layers
Contradictory directives spread across SKILL.md files, persona configs, and orchestration rules.
Dependency & Portability Problems
Hard-coded paths, missing imports, or assumptions that break across different agent environments.
Weak Evidence Discipline
Agents making claims without citations, assertions without data, decisions without reasoning chains.
Structural Formatting Issues
Inconsistent heading levels, broken markdown, or output that doesn't match the stated format.
The Karpathy-Inspired Method
Andrej Karpathy's autoresearch applies measurement discipline to research iteration. Auto Skill Improver applies the same principle to OpenClaw skill engineering — a controlled loop where every SKILL.md change is accountable.
Classify the Skill Type
The tool analyses your SKILL.md file and determines its category — research agent, coding agent, orchestrator, reviewer, or something else. Classification informs what benchmarks make sense.
Build a Real Benchmark
Not a vibes check. A structured test suite with pass/fail criteria that exercises your agent's actual capabilities against representative multi-agent scenarios.
Establish a Baseline
Run the benchmark on the unmodified SKILL.md. Record the score. This is your point of comparison — every mutation is measured against this baseline.
Mutate One Thing at a Time
Change a single instruction, add one constraint, remove one ambiguity. Never change multiple SKILL.md directives simultaneously — otherwise you can't attribute improvement.
Keep Only What Improves
Re-run the benchmark after each mutation. If the score goes up, the change stays. If it doesn't improve — or regresses — it gets discarded. No exceptions.
Stop When the Benchmark Saturates
When successive mutations stop producing gains, your SKILL.md has reached its current ceiling. Further changes are noise, not signal. Move on or build a harder benchmark.
When to Use It — and When Not To
Best For
- OpenClaw SKILL.md files that define agent behaviour
- Agent persona configurations in multi-agent setups
- Orchestration rules that need measurable improvement
- Any OpenClaw setup where you need evidence that changes actually help
Not the Right Fit
- —One-off prompts you'll use once and discard
- —Creative agents where there's no objective success metric
- —Skills that are already performing at ceiling
- —Situations where you can't define what 'better' means for agent output
Frequently Asked Questions
Auto Skill Improver for OpenClaw is an open-source tool that applies benchmark-driven iteration to your SKILL.md files and agent configurations. It classifies your agent type, builds a test suite, establishes a baseline score, then systematically mutates one instruction at a time — keeping only changes that measurably improve agent performance in multi-agent setups.
It treats your SKILL.md as a testable artefact. The tool generates scenarios that exercise your agent's instructions, measures output quality against defined criteria, then makes targeted changes — one at a time — to find which instruction tweaks produce measurably better agent behaviour.
Any agent type — research agents, coding agents, orchestrators, reviewers, data analysts, and more. The tool classifies the agent type automatically from your SKILL.md and builds appropriate benchmarks for that category.
Yes. When agents interact in an OpenClaw multi-agent setup, improving one agent's SKILL.md can affect the entire system. Auto Skill Improver benchmarks the agent's output in context, so improvements are measured against how well the agent performs within the broader team.
A benchmark is a structured test suite with defined inputs and pass/fail criteria. For a research agent, this might be 'find and correctly cite three sources on this topic'. For a coding agent, it might be 'generate a function that passes these unit tests'. The benchmark runs the same tests before and after each SKILL.md change.
Benchmark saturation occurs when successive SKILL.md mutations stop producing score improvements. Your agent has reached the ceiling of what the current benchmark can measure. You can either accept the current performance or create a harder benchmark that tests more advanced multi-agent scenarios.
Manual editing is editorial: you rewrite agent instructions, they sound more precise, you redeploy. But 'sounds more precise' isn't evidence. Auto Skill Improver is empirical: it establishes a baseline, changes one instruction at a time, re-runs the same benchmark, and keeps only what scores higher.
Yes. Auto Skill Improver is fully open source and free to use. The source code is available on GitHub at github.com/mlobo2012/auto-skill-improver. There are no usage limits, no API keys required for the tool itself, and no premium tiers.
Stop Guessing. Start Measuring.
Download the quickstart guide, clone the repo, and run your first OpenClaw benchmark-driven improvement loop in under 10 minutes.