# SkillOpt — Train Reusable Natural-Language Skills for LLM Agents

> A Microsoft Research tool that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits and validation-gated updates.

## Install

Save in your project root:

# SkillOpt — Train Reusable Natural-Language Skills for LLM Agents

## Quick Use
```bash
git clone https://github.com/microsoft/SkillOpt.git
cd SkillOpt && pip install -e .
python run_skillopt.py --task_suite my_tasks --output best_skill.md
```

## Introduction
SkillOpt is a Microsoft Research project that trains reusable natural-language skills for frozen LLM agents. Rather than fine-tuning weights, it iteratively refines a text-space skill document that an agent loads into its context to improve task performance. The result is a deployable `best_skill.md` artifact any compatible agent can consume.

## What SkillOpt Does
- Optimizes natural-language skill documents through trajectory-driven editing
- Evaluates agent trajectories on validation tasks and proposes targeted revisions
- Produces a standalone `best_skill.md` file for any frozen LLM agent
- Supports multiple task families for cross-task generalization
- Gates every update on validation performance to prevent regressions

## Architecture Overview
SkillOpt runs a three-phase loop: execute, analyze, revise. The agent runs tasks with the current skill, SkillOpt examines trajectories for failure patterns, and a proposer model drafts edits that are accepted only if validation scores improve. Written in Python, it requires only API access to the target LLM.

## Self-Hosting & Configuration
- Install via pip from the cloned repository
- Provide an LLM API key via environment variable
- Define task suites as JSON with inputs and evaluation criteria
- No GPU required — operates entirely through API calls

## Key Features
- Learns from actual agent trajectories rather than static benchmarks
- Validation-gated updates ensure measurable improvement on every revision
- Produces portable markdown skill files usable across agent frameworks
- Open-source under MIT with example task suites included

## Comparison with Similar Tools
- **DSPy** — compiles prompts; SkillOpt optimizes skill documents via trajectory analysis
- **TextGrad** — gradient-like text feedback; SkillOpt uses validation-gated discrete edits
- **OPRO** — searches prompt variations; SkillOpt refines structured skills from trajectories

## FAQ
**Q: Does it require fine-tuning the LLM?**
A: No. The LLM stays frozen; only the text skill document is modified.

**Q: What is the output format?**
A: A markdown file (`best_skill.md`) with structured instructions and heuristics.

**Q: How many iterations does optimization take?**
A: Most tasks converge within 10 to 30 iterations depending on complexity.

## Sources
- https://github.com/microsoft/SkillOpt

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