SkillOpt

Skill Document

A skill document is a Markdown file that serves as the “prompt weights” of your agent. SkillOpt trains this document through iterative optimization.

What is a Skill Document?

A skill document is a structured set of instructions that tells a language model how to approach a specific type of task. It’s analogous to learned weights in a neural network — encoding task-specific knowledge in natural language rather than floating-point parameters.

Structure

A typical skill document contains:

# Task Strategy

## General Approach
- Break complex problems into sub-steps
- Always verify intermediate results

## Common Patterns
- When you see X, try approach Y
- Avoid Z because it leads to errors

## Edge Cases
- If the input contains A, handle it specially by...
- Watch out for B — it requires C

## Output Format
- Always include reasoning before the answer
- Format numbers with proper units

How It Evolves

During training, the skill document is modified by edit patches:

1. Additions: New rules or strategies discovered from failed trajectories
2. Modifications: Refining existing rules that are partially correct
3. Deletions: Removing rules that consistently lead to errors

Each edit is validated through the gate mechanism before being permanently accepted.

Initial Skill

You can start training with:

• Empty skill: The system learns everything from scratch
• Seed skill: Provide initial instructions to bootstrap training
• Pre-trained skill: Transfer a skill from a related benchmark

Configure the initial skill in your YAML:

train:
  init_skill: "path/to/initial_skill.md"  # or omit for empty

Skill Quality Metrics

Track your skill’s evolution through:

• Validation score: Primary metric on the selection split
• Test score: Final metric on held-out test data
• Skill length: Total tokens in the document
• Edit acceptance rate: Fraction of proposed edits that pass gating

Best Practices

!!! tip “Tips for better skills” 1. Start with a seed skill (env.skill_init) if you have domain knowledge — it converges faster 2. Use cosine LR schedule — aggressive early exploration + careful late refinement 3. Enable slow update (use_slow_update: true) to prevent forgetting across epochs 4. Enable meta skill (use_meta_skill: true) so the optimizer accumulates strategy memory

Next Steps

• Deep Learning Analogy
• Configuration Reference

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