listen
정보
"듣기" 기술은 Claude가 깊은 수용적 주의를 실천하여, 표면적인 단어 너머의 진정한 의도를 추출하기 위해 문자적, 감정적, 맥락적, 메타적 의사소통의 다중 계층을 분석할 수 있도록 합니다. 이 기술은 모호한 요청, 맥락이 문자적 의미와 모순될 때, 또는 비용이 큰 오해를 방지하기 위해 주요 작업 전에 사용하도록 설계되었습니다. 개발자는 이전 응답이 핵심을 놓쳤거나 전체 신호가 종합적인 통합을 필요로 할 때 이 기술을 호출해야 합니다.
빠른 설치
Claude Code
추천npx 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/listenClaude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요
문서
Listen
Structured deep listening — clear assumptions, attend w/ full reception, parse multi signal layers, reflect understanding, notice unsaid, integrate complete picture of intent.
Use When
- Request ambiguous, rushing to action risks wrong problem
- Words say one thing, ctx suggests else (literal vs implied mismatch)
- Prev responses missed mark — user keeps clarifying / rephrasing
- Complex request w/ multi layers: technical + emotional + unstated constraints
- Before large task where misunderstanding wastes effort
- After
meditateclears noise →listendirects cleared attention outward
In
- Req: User msg(s) to attend to (implicit from conv)
- Opt: Conv history providing ctx
- Opt: MEMORY.md / CLAUDE.md w/ user prefs + project ctx
- Opt: Specific concern about what might be misunderstood
Do
Step 1: Clear — Release Assumptions
Before receiving signal, release preconceptions about what they want.
- Notice pre-formed responses → label + set aside
- Check pattern-matching: "Looks like request I've seen" → match may be wrong
- Release assumption first sentence = complete request
- Release assumption technical request = only request
- Approach words as first time, even if similar handled before
→ Receptive state, attention open not narrowing toward solution. Impulse to respond paused → fully receiving.
If err: Can't release (strong pattern persists) → acknowledge explicitly: "Looks like X — but check if actually asked." Naming weakens grip.
Step 2: Attend — Full Reception
Read msg w/ complete attention, hold all parts simultaneously.
- Read entire msg before processing any part
- Note structure: single request, multi, q, correction, narrative?
- Mark key nouns + verbs — concrete elements specified
- Note emphasis: what elaborated? What brief?
- Note ordering: first (often priority), last (often afterthought — or real request buried at end)
- Read 2nd time, attend to tone + framing vs content
→ Complete reception — no words skipped, no sentences glossed. Msg held as whole, not immediately decomposed.
If err: Very long → break into sections but read each completely. Attention pulled to one part (usually most technical) → deliberately attend non-technical parts, often contain intent.
Step 3: Layer — Parse Signal Types
Msg contains multi simultaneous signals. Parse each layer separately.
Signal Layer Taxonomy:
┌──────────────┬──────────────────────────────┬──────────────────────────┐
│ Layer │ What to Extract │ Evidence │
├──────────────┼──────────────────────────────┼──────────────────────────┤
│ Literal │ What the words explicitly │ Direct statements, │
│ │ say — the surface request │ specific instructions │
├──────────────┼──────────────────────────────┼──────────────────────────┤
│ Procedural │ What they want done — the │ Verbs, action words, │
│ │ desired action or output │ "I want," "please," │
│ │ │ "can you" │
├──────────────┼──────────────────────────────┼──────────────────────────┤
│ Emotional │ How they feel about the │ Frustration ("I keep │
│ │ situation — frustration, │ trying"), urgency ("I │
│ │ curiosity, urgency, delight │ need this now"), delight │
│ │ │ ("this is cool") │
├──────────────┼──────────────────────────────┼──────────────────────────┤
│ Contextual │ The situation surrounding │ Mentions of deadlines, │
│ │ the request — why now, │ other people, projects, │
│ │ what prompted it │ prior attempts │
├──────────────┼──────────────────────────────┼──────────────────────────┤
│ Constraint │ Boundaries on the solution │ "Without changing X," │
│ │ — what must be preserved, │ "keep it simple," │
│ │ what cannot change │ "compatible with Y" │
├──────────────┼──────────────────────────────┼──────────────────────────┤
│ Meta │ The request about the │ "Am I asking the right │
│ │ request — are they asking │ question?", "Is this │
│ │ whether they are asking │ even possible?", │
│ │ the right thing? │ "Should I be doing X?" │
└──────────────┴──────────────────────────────┴──────────────────────────┘
Per layer → note present + absent. Absent as informative as present.
→ Multi-layered reading. Literal + procedural usually clear. Emotional, contextual, constraint, meta require careful attention. ≥1 non-literal layer ID'd.
If err: Only literal visible → may genuinely be straightforward. But check: msg unusually short for complexity? Hedging words ("maybe", "I think", "if possible")? Often indicate unstated layer.
Step 4: Reflect — Mirror Understanding
Before acting → reflect back to verify alignment.
- Paraphrase in diff words than user used → reveals meaning captured, not just words
- Name layers explicitly if non-literal significant: "Sounds like you want X, urgency suggests blocking other work"
- State priority: "Most important part seems to be..."
- Multi interpretations → name: "Could mean A or B — which closer?"
- Apparent contradictions → surface gently: "Mentioned X + Y — how relate?"
→ User confirms / corrects. Either valuable — confirm = intent aligned; correct = now clearer. Feels like mirror, not judgment.
If err: User impatient ("just do it") → may value speed over alignment → honor pref but note risk. Reflection wrong → don't defend, accept correction, update immediately.
Step 5: Notice Silence — Read Gaps
Attend to what not said — can be as important as what said.
- Topic related to request not mentioned? (missing ctx)
- Constraint not stated? (assumed knowledge / unstated pref)
- Emotional tone missing? (calm in stressful situation, urgency w/o explanation)
- Alt approaches not considered? (tunnel vision / deliberate exclusion)
- Q not asked? (q behind q)
→ ≥1 significant gap ID'd. May not need addressing — awareness prevents blind spots. Most useful = missing constraints + missing ctx.
If err: No gaps apparent → user thorough, or more likely, gaps in areas AI also blind to. Consider: diff person working on this project would want to know what? Lateral perspective surfaces hidden gaps.
Step 6: Integrate — Synthesize Complete Understanding
Combine all layers + gaps → unified picture of actual need.
- State complete understanding: literal + implied + emotional + constraints + gaps
- ID core need: if everything else fell away, what is one thing most needed?
- Determine response type: action, understanding, validation, exploration?
- If integrated differs from literal → decide address deeper / stated (usually both)
- Set intent for next action: "Based on what heard, I will..."
→ Complete nuanced understanding beyond surface. Specific enough to guide action, honest enough to acknowledge uncertainty.
If err: Integration produces confused picture → signals genuinely conflict. Ask one focused q that resolves ambiguity: "Most important to understand is..." Don't ask multi qs — single well-chosen reveals more than list.
Check
- Assumptions cleared before attending
- Full msg read before any part acted on
- ≥1 non-literal signal layer ID'd
- Understanding reflected back before action
- Gaps + silences noticed + factored
- Integrated understanding addresses core need, not just surface
Traps
- Listen to respond: Forming response while receiving → shapes what heard, filters signals not fitting pre-formed answer.
- Literal-only listening: Take words at face value, miss intent, emotion, ctx behind.
- Projection: Hear what user would say if were AI, vs what actually said. Their priorities + ctx different.
- Over-interpretation: Find layers not there. Sometimes bug fix request = just bug fix — not every msg has hidden emotional content.
- Reflect too much: Turn every interaction reflective when user wants quick action. Match reflection depth to request complexity.
- Neglect literal: So focused on subtext, explicit request not fulfilled. Literal still matters — address even when deeper layers present.
→
listen-guidance— human-guidance variant → coach person developing active listeningobserve— sustained neutral pattern recognition feeding listening w/ broader ctxteach— effective teaching requires listening first to understand learnermeditate— inward attention clears space for outward listeningheal— self-assessment reveals if listening capacity impaired by drift
GitHub 저장소
연관 스킬
release-standards
문서 처리이 스킬은 소프트웨어 릴리스에 대한 시맨틱 버저닝(semver) 가이드라인과 변경 로그 형식 표준을 제공합니다. 릴리스를 준비할 때 버전 번호(메이저/마이너/패치)를 올바르게 증가시키고 변경 로그 항목을 구성하려면 이 스킬을 사용하세요. 사전 릴리스 식별자 규칙과 개발자를 위한 명확한 예시가 포함되어 있습니다.
commit-standards
문서 처리이 스킬은 Conventional Commits 표준에 따라 Git 커밋 메시지를 형식화합니다. 커밋 작성이나 리뷰 시 일관성을 보장하기 위해 템플릿과 유형 정의(예: `feat`, `fix`, `refactor`)를 제공합니다. 커밋 과정에서 이를 사용하여 명확하고 구조화된 커밋 기록을 생성할 수 있습니다.
huggingface-tokenizers
문서 처리이 스킬은 HuggingFace의 Rust 기반 라이브러리를 사용하여 1GB 텍스트를 20초 이내에 처리하는 고성능 토크나이제이션을 제공합니다. BPE, WordPiece, Unigram 알고리즘을 지원하며 사용자 정의 토크나이저 학습과 정렬 추적 기능을 포함합니다. 프로덕션 수준의 고속 토크나이제이션이 필요하거나 transformers 생태계와 통합된 맞춤형 토크나이저를 구축할 때 사용하세요.
nano-pdf
문서 처리nano-pdf는 개발자가 특정 페이지의 텍스트 변경이나 오타 수정과 같은 자연어 지시를 사용해 PDF를 편집할 수 있는 CLI 도구입니다. 터미널에서 직접 빠르고 프로그래밍 방식으로 PDF를 수정하는 데 이상적입니다. 페이지 번호 매기기가 버전마다 다를 수 있으므로 출력 결과는 항상 확인하세요.
