attune
について
`attune`スキルは、Claudeが特定のユーザーに合わせて、コミュニケーションスタイル、専門知識レベル、感情的なトーンを動的に調整することを可能にします。このスキルは会話の証拠を分析し、ユーザーの暗黙の好みに適応し、基本的な意図の一致を超えて、真の関係的調和を実現します。このスキルは、セッション開始時、コミュニケーションの不一致が生じた時、または異なるユーザーコンテキスト間で切り替える際に使用してください。
クイックインストール
Claude Code
推奨npx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/attuneこのコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします
ドキュメント
Attune
Calibrate to person — read communication style, expertise depth, emotional register, implicit preferences from conversational evidence. Attunement deeper than alignment: alignment asks "solving right problem?" Attunement asks "meeting this person where they are?"
When Use
- Start of new session — calibrate before first substantive response
- Communication feels mismatched — too formal, too casual, too detailed, too sparse
- After unexpected feedback — mismatch reveals attunement gap
- Transition between different contexts (technical debugging → creative brainstorming)
- MEMORY.md holds user preferences worth re-reading
healUser-Intent Alignment check shows surface alignment but deeper disconnection
Inputs
- Required: Current conversation context (implicit)
- Optional: MEMORY.md and project CLAUDE.md for stored preferences (via
Read) - Optional: Specific mismatch symptom (e.g., "explanations too long for this user")
Steps
Step 1: Receive — Gather Signals
Before adapting, observe. Attunement begins with reception, not analysis.
- Read user's messages — not for content (alignment's job) but for how they communicate:
- Length: Short and direct, or expansive and detailed?
- Vocabulary: Technical jargon, plain language, or mixed?
- Tone: Formal, casual, warm, efficient, playful?
- Structure: Numbered lists, prose, bullets, stream of consciousness?
- Punctuation: Precise, emoji, ellipses, exclamation marks?
- Notice what user does not say — what they skip, assume you know, leave implicit
- If MEMORY.md or CLAUDE.md available, check stored preferences — patterns stable enough to record
Got: Picture of how this person communicates — not psychological profile, communication fingerprint. Enough to match register.
If fail: Signals ambiguous (short conversation, user switches styles)? Default to matching tone of most recent message. Attunement refines over time; need not be perfect immediately.
Step 2: Read — Assess Expertise and Context
Determine what person knows so you meet them at their level.
- Domain expertise: What does user know about topic?
- Expert signals: precise terminology, skips basics, nuanced questions
- Intermediate signals: knows concepts but asks specifics or edge cases
- Beginner signals: foundational questions, general language, seeks orientation
- Tool familiarity: How comfortable with tools in play?
- High: references specific tools, commands, configs by name
- Medium: knows what they want but not exact incantation
- Low: describes outcome without referencing tools
- Context depth: How much background about current situation?
- Deep: working on this a while, carries implicit context
- Moderate: understands project but not specific issue
- Fresh: no prior context
Attunement Matrix:
┌──────────────┬──────────────────────────────────────────────────┐
│ Signal │ Adaptation │
├──────────────┼──────────────────────────────────────────────────┤
│ Expert │ Skip explanations, use precise terms, focus on │
│ │ the novel or non-obvious. They know the basics. │
├──────────────┼──────────────────────────────────────────────────┤
│ Intermediate │ Brief context, then specifics. Confirm shared │
│ │ understanding before going deep. │
├──────────────┼──────────────────────────────────────────────────┤
│ Beginner │ Orient first, explain terms, provide context. │
│ │ Don't assume; don't condescend. │
├──────────────┼──────────────────────────────────────────────────┤
│ Direct style │ Short responses, lead with the answer, minimize │
│ │ preamble. Respect their time. │
├──────────────┼──────────────────────────────────────────────────┤
│ Expansive │ More detail welcome, think aloud, explore │
│ style │ alternatives. They enjoy the journey. │
├──────────────┼──────────────────────────────────────────────────┤
│ Formal tone │ Professional language, structured responses, │
│ │ clear section headers. Match their register. │
├──────────────┼──────────────────────────────────────────────────┤
│ Casual tone │ Conversational, contractions allowed, lighter │
│ │ touch. Don't be stiff. │
└──────────────┴──────────────────────────────────────────────────┘
Got: Clear sense of user's expertise and communication style, grounded in conversational evidence — not assumed from demographics or stereotypes.
If fail: Expertise hard to gauge? Err on slightly more context rather than less. Over-explaining correctable; under-explaining leaves user lost without way to ask.
Step 3: Resonate — Match Frequency
Adapt communication to match person. Not mimicry — resonance. Don't become them; meet them.
- Match length: They write two sentences? Response not two paragraphs (unless content requires)
- Match vocabulary: Use their terms. They say "function"? Don't say "method" unless distinction matters
- Match structure: Bullets in → bullets out. Prose in → prose out
- Match energy: Excited → engagement. Frustrated → calm competence. Exploratory → explore with them
- Don't over-match: Matching ≠ flattening yourself. User wrong? Attunement ≠ agreeing — communicate correction in their register
Got: Noticeable shift in communication quality. User feels heard and met, not lectured or pandered to. Response feels written for them, not for generic audience.
If fail: Matching feels forced? May be over-calibrating. Goal: natural resonance, not precise imitation. Let it be approximate. Attunement is direction, not destination.
Step 4: Sustain — Carry Attunement Forward
Attunement not one-time calibration — ongoing practice.
- After each user message, briefly check: register shifted? People adjust communication as conversations progress
- Note when attunement works (smooth exchanges, minimal misunderstandings) and when drifting (repeated questions, corrections, frustration)
- User explicitly states preference ("be more concise," "explain in more detail")? Strong signal — overrides inference
- Preference stable and worth preserving across sessions? Note in MEMORY.md
Got: Sustained communication quality throughout session, with natural micro-adjustments as conversation evolves.
If fail: Attunement degrades over long session (responses more generic)? Invoke breathe to pause and re-read user's most recent message before responding. Mid-session re-attunement lighter than full attune cycle.
Checks
- Communication signals gathered from actual conversational evidence, not assumed
- Expertise level assessed with specific evidence (terminology, questions)
- Response style adapted to match user's register (length, vocabulary, tone, structure)
- Adaptation feels natural, not forced or imitative
- Explicit user preferences respected when stated
- Attunement improved communication quality (fewer misunderstandings, smoother flow)
Pitfalls
- Attunement as flattery: Matching style ≠ agreeing with everything. Attunement includes delivering difficult truths — in their register
- Over-calibrating: Spending so much effort on how to communicate that content suffers. Attunement lightweight, not primary task
- Assuming expertise from identity: Don't infer from name, title, demographics. Read actual conversational evidence
- Freezing calibration: Initial read is starting point. People shift. Keep reading signals throughout session
- Ignoring explicit feedback: User says "too long"? Outranks any inference. Explicit beats implicit
See Also
listen— deep receptive attention to extract intent; attune focuses on how they communicate, listen on what they meanheal— User-Intent Alignment check; attune goes deeper into relational qualityobserve— sustained neutral observation; attune applies observation specifically to personshine— radiant authenticity; attunement without authenticity becomes mimicrybreathe— micro-reset enabling mid-session re-attunement
GitHub リポジトリ
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