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4a1b984d87
- Rename teacher -> optimizer, student -> target across all code, configs, docs, prompts - CLI: --teacher_model -> --optimizer_model, --student_model -> --target_model - Remove best_skill files, keep only initial skills - Fix slow update gate (force write into skill) - Fix SLOW_UPDATE marker stripping - Remove deep_reflect and meta_reflect mechanisms - Update .env.example with export prefix and azure_cli docs - Add endpoint empty validation in azure_openai.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
79 lines
2.5 KiB
Markdown
79 lines
2.5 KiB
Markdown
# Skill Document
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A **skill document** is a Markdown file that serves as the "prompt weights" of your agent. SkillOpt trains this document through iterative optimization.
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## What is a Skill Document?
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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.
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## Structure
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A typical skill document contains:
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```markdown
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# Task Strategy
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## General Approach
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- Break complex problems into sub-steps
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- Always verify intermediate results
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## Common Patterns
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- When you see X, try approach Y
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- Avoid Z because it leads to errors
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## Edge Cases
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- If the input contains A, handle it specially by...
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- Watch out for B — it requires C
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## Output Format
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- Always include reasoning before the answer
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- Format numbers with proper units
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```
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## How It Evolves
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During training, the skill document is modified by **edit patches**:
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1. **Additions**: New rules or strategies discovered from failed trajectories
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2. **Modifications**: Refining existing rules that are partially correct
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3. **Deletions**: Removing rules that consistently lead to errors
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Each edit is validated through the **gate** mechanism before being permanently accepted.
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## Initial Skill
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You can start training with:
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- **Empty skill**: The system learns everything from scratch
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- **Seed skill**: Provide initial instructions to bootstrap training
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- **Pre-trained skill**: Transfer a skill from a related benchmark
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Configure the initial skill in your YAML:
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```yaml
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train:
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init_skill: "path/to/initial_skill.md" # or omit for empty
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```
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## Skill Quality Metrics
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Track your skill's evolution through:
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- **Validation score**: Primary metric on the selection split
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- **Test score**: Final metric on held-out test data
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- **Skill length**: Total tokens in the document
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- **Edit acceptance rate**: Fraction of proposed edits that pass gating
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## Best Practices
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!!! tip "Tips for better skills"
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1. **Start with a seed skill** (`env.skill_init`) if you have domain knowledge — it converges faster
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2. **Use cosine LR schedule** — aggressive early exploration + careful late refinement
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3. **Enable slow update** (`use_slow_update: true`) to prevent forgetting across epochs
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4. **Enable meta skill** (`use_meta_skill: true`) so the optimizer accumulates strategy memory
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## Next Steps
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- [Deep Learning Analogy](dl-analogy.md)
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- [Configuration Reference](../reference/config.md)
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