Self distill

Skill LewenW/claude-distill-me/skills/self-distill

Guides deep behavioral distillation — three-layer extraction of decision patterns, personality, and values.From its SKILL.md

Install

npx -y skills add LewenW/claude-distill-me --skill self-distill

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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SKILL.md

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Deep Distillation Guide

Three-Layer Extraction

Every pattern category uses the same depth progression:

Layer 1 — Observable: Direct evidence from the data. What did they literally say or do? Layer 2 — Interpretive: What do the patterns imply? Read between the lines. Layer 3 — Contrastive: What makes this person DIFFERENT? What would surprise you?

Extraction Quality Standards

Each pattern MUST have:

Pattern: When [situation], this user [behavior] because [underlying reason]
Evidence: specific quote or concrete behavior from the data
Confidence: high / medium / low
Depth: surface / interpretive / deep

Bad pattern: "Prefers short messages" Good pattern: "Uses 2-5 word commands for routine tasks but switches to detailed paragraphs when stakes are high — the message length IS the priority signal"

Bad pattern: "Uses Chinese and English" Good pattern: "Thinks in Chinese but keeps technical terms in English. Code-switches mid-sentence. The ratio of Chinese to English tracks their emotional engagement — more Chinese = more invested"

Decision-Making — Look For:

Accept/reject patterns on AI suggestions
Implicit decision frameworks (data-driven? gut? speed-first?)
How they handle ambiguity (ask vs assume vs demand opinion)
Risk calibration (ship fast vs polish) and when it shifts
Guardrails and what past experiences they reveal
Trust architecture: what triggers verification vs delegation?

Communication — Look For:

Message length as a function of context (not just "short" or "long")
Language mixing rules (which language for what, and why)
Relationship with AI (peer? tool? how does this shift?)
Emotional register triggers (excitement, frustration, dismissal)
What they HATE in output and what that hatred reveals
Gap between their own style and their expectations for AI output

Values — Look For:

Revealed preferences vs stated preferences (where they diverge)
What they spend attention on that most people in their role wouldn't
What they skip that most people wouldn't skip
Values hierarchy: when two priorities conflict, which wins?
Root values driving surface decisions (usually 1-2 core values)
Meta-cognition: how they learn, handle their own mistakes

After Extraction

User corrections to generated patterns are the highest-quality signal. If they say "that's wrong" or "that's not why", the correction reveals more about them than the original data did. Incorporate immediately.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.