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
| File | Contents | Load When |
|---|---|---|
references/distillation-patterns.md | Pattern catalog for converting reasoning to rules | Always |
Prerequisites
- The source skill must exist and pass
package-evaluatorat >= 70% - Access to both the source model (Opus) and target model (Haiku/Sonnet) for validation
- The
surrogate-verifierskill for cross-model assertion checking
Workflow
Phase 1: Complexity Analysis
Score each section of the source SKILL.md for reasoning difficulty:
| Complexity Signal | Score | Distillation Action |
|---|---|---|
| Decision tree with 3+ branches | HIGH | Convert to explicit if/then lookup table |
| "Use judgment" or "consider context" | HIGH | Replace with concrete heuristic rules |
| Multi-step inference chain | HIGH | Break into numbered atomic steps |
| Reference to domain expertise | MED | Add explicit reference file with knowledge |
| Clear enumerated steps | LOW | Keep as-is |
| Concrete examples with expected output | LOW | Keep as-is |
Produce a complexity map: section name -> complexity score -> planned action.
Phase 2: Trace Collection
Execute the source skill with Opus on 5 representative tasks:
- Select tasks from
evals/cases.yaml(positive cases) or generate new ones - 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
- Store traces as structured data for pattern extraction
Phase 3: Pattern Extraction
From the collected traces, extract deterministic patterns:
- 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
- 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")
- Concrete examples — Extract representative input/output pairs from traces to serve as few-shot examples in the distilled skill
- 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:
| Source Pattern | Distilled Replacement |
|---|---|
| "Analyze the code and determine..." | "Check for these 5 specific patterns: [list]" |
| "Use appropriate formatting" | "Output as a markdown table with columns: [A, B, C]" |
| "Consider the context to decide..." | "If [condition A]: do X. If [condition B]: do Y. Default: Z" |
| "Apply best practices for..." | Reference file with explicit best practices enumerated |
| Multi-paragraph reasoning instruction | Numbered step list with single-sentence steps |
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):
- Execute the same 5 tasks from Phase 2 with the distilled skill loaded
- Use the
surrogate-verifierto generate assertions for each task output - Compare pass rates:
| Metric | Source (Opus + original) | Target (Haiku + distilled) | Delta |
|---|---|---|---|
| Assertions passed | N/M | N/M | ± |
| Weighted score | X.XX | X.XX | ± |
| Output completeness | % | % | ± |
| Format compliance | % | % | ± |
- 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
| Error | Resolution |
|---|---|
| Source skill scores below 70% | Refuse distillation; recommend evolution via test-engineer |
| No execution traces available | Generate synthetic tasks and collect traces before proceeding |
| Target model fails all assertions | Skill may be too complex for target model; report with detail |
| Distilled skill longer than source | Review distillation; patterns may need consolidation |
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