Skill Distiller

Transform skills authored for high-capability models (Opus) into deterministic workflows that execute reliably on lower-cost models (Sonnet, Haiku). The core insight from EvoSkills: skills encode reusable task structure, not model-specific artifacts. A skill evolved on Opus transfers with +35-45pp gains to other models — but only when the instructions are sufficiently deterministic that lower-capability models can follow them without improvising.

Reference Files

Prerequisites

• The source skill must exist and pass package-evaluator at >= 70%
• Access to both the source model (Opus) and target model (Haiku/Sonnet) for validation
• The surrogate-verifier skill for cross-model assertion checking

Workflow

Phase 1: Complexity Analysis

Score each section of the source SKILL.md for reasoning difficulty:

Produce a complexity map: section name -> complexity score -> planned action.

Phase 2: Trace Collection

Execute the source skill with Opus on 5 representative tasks:

1. Select tasks from evals/cases.yaml (positive cases) or generate new ones
2. For each task, capture the full execution trace:

- Tool calls made (which tools, in what order) - Intermediate reasoning visible in output - Final output structure and content - Time taken and token usage

1. Store traces as structured data for pattern extraction

Phase 3: Pattern Extraction

From the collected traces, extract deterministic patterns:

1. Decision paths — For each HIGH-complexity section, find the actual decisions Opus made across

the 5 tasks. If Opus chose the same path in 4/5 cases, that path becomes the default rule

1. Lookup tables — Where Opus applied domain knowledge, build explicit lookup tables

(e.g., "if input contains SQL, use these patterns; if input contains Python, use those")

1. Concrete examples — Extract representative input/output pairs from traces to serve as

few-shot examples in the distilled skill

1. Tool sequences — Identify the common tool invocation pattern and make it explicit

("Step 1: Read the file. Step 2: Grep for pattern X. Step 3: Write output.")

Phase 4: Distilled Rewrite

Rewrite the SKILL.md applying all distillation actions from Phase 1:

Rules for the rewrite:

• Every instruction must be actionable by a model with no domain expertise
• No step should require inference — each step's input and output must be explicit
• Replace all "consider", "analyze", "determine" verbs with "check", "count", "list", "output"
• Add concrete examples for any step that could be ambiguous
• Keep the SKILL.md under 500 lines (distillation should reduce, not expand)

Phase 5: Target Model Validation

Run the distilled skill on the target model (Haiku or Sonnet):

1. Execute the same 5 tasks from Phase 2 with the distilled skill loaded
2. Use the surrogate-verifier to generate assertions for each task output
3. Compare pass rates:

1. If target model score < 80% of source model score, iterate:

- Identify which assertions the target model fails - Add more explicit instructions for those specific failure points - Re-run validation (max 3 iterations)

Phase 6: Cross-Model Report

Produce the final comparison:

# Skill Distillation Report: <skill-name>

## Complexity Reduction
- Sections distilled: N/M (HIGH → LOW)
- Instruction word count: original X → distilled Y (Z% reduction)
- Decision points replaced with lookup tables: N

## Cross-Model Performance
| Model   | Assertions Passed | Weighted Score | Format Compliance |
|---------|-------------------|----------------|-------------------|
| Opus    | 7/7               | 1.00           | 100%              |
| Sonnet  | 6/7               | 0.92           | 100%              |
| Haiku   | 5/7               | 0.85           | 85%               |

## Changes Made
1. [Section] "Analyze complexity" → explicit 5-item checklist
2. [Section] "Apply formatting" → fixed markdown table template
...

## Recommendation
[SHIP | ITERATE | MANUAL_REVIEW_NEEDED]

Error Handling

Limitations

• Cannot distill skills that rely on open-ended adaptive reasoning at many decision points or multi-turn reasoning
• Visual/interactive skills (HTML generation, browser automation) may not distill well
• Distillation optimizes for determinism, not creativity — skills requiring open-ended generation

(writing, brainstorming) are poor candidates

• Trace collection requires actual model execution, incurring API costs