返回技能列表

listen

pjt222
更新于 2 days ago
6 次查看
17
2
17
在 GitHub 上查看
文档处理wordai

关于

The "listen" skill enables Claude to practice deep receptive attention, parsing multiple layers of communication—literal, emotional, contextual, and meta—to extract true intent beyond surface words. It's designed for ambiguous requests, when context contradicts literal meaning, or before major tasks to prevent costly misunderstandings. Developers should invoke it when previous responses have missed the mark or when the full signal requires holistic integration.

快速安装

Claude Code

推荐
主要方式
npx skills add pjt222/agent-almanac -a claude-code
插件命令备选方式
/plugin add https://github.com/pjt222/agent-almanac
Git 克隆备选方式
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/listen

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

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 meditate clears noise → listen directs 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.

  1. Notice pre-formed responses → label + set aside
  2. Check pattern-matching: "Looks like request I've seen" → match may be wrong
  3. Release assumption first sentence = complete request
  4. Release assumption technical request = only request
  5. 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.

  1. Read entire msg before processing any part
  2. Note structure: single request, multi, q, correction, narrative?
  3. Mark key nouns + verbs — concrete elements specified
  4. Note emphasis: what elaborated? What brief?
  5. Note ordering: first (often priority), last (often afterthought — or real request buried at end)
  6. 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.

  1. Paraphrase in diff words than user used → reveals meaning captured, not just words
  2. Name layers explicitly if non-literal significant: "Sounds like you want X, urgency suggests blocking other work"
  3. State priority: "Most important part seems to be..."
  4. Multi interpretations → name: "Could mean A or B — which closer?"
  5. 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.

  1. Topic related to request not mentioned? (missing ctx)
  2. Constraint not stated? (assumed knowledge / unstated pref)
  3. Emotional tone missing? (calm in stressful situation, urgency w/o explanation)
  4. Alt approaches not considered? (tunnel vision / deliberate exclusion)
  5. 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.

  1. State complete understanding: literal + implied + emotional + constraints + gaps
  2. ID core need: if everything else fell away, what is one thing most needed?
  3. Determine response type: action, understanding, validation, exploration?
  4. If integrated differs from literal → decide address deeper / stated (usually both)
  5. 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 listening
  • observe — sustained neutral pattern recognition feeding listening w/ broader ctx
  • teach — effective teaching requires listening first to understand learner
  • meditate — inward attention clears space for outward listening
  • heal — self-assessment reveals if listening capacity impaired by drift

GitHub 仓库

pjt222/agent-almanac
路径: i18n/caveman-ultra/skills/listen
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

相关推荐技能

release-standards

文档处理

这个Skill为开发者提供了语义化版本规范和变更日志格式标准。它能在准备软件发布时快速指导版本号更新和变更日志撰写,包含版本号递增规则、预发布标识符等关键信息。适用于需要遵循规范发布流程的开发场景。

查看技能

commit-standards

文档处理

这个Skill帮助开发者遵循Conventional Commits规范格式化Git提交信息。它提供了标准格式模板和常用提交类型的中英文对照表(如feat/新增、fix/修正等),适用于编写提交、执行git commit或审查提交历史的场景。通过确保提交信息的规范性和一致性,它能提升团队协作效率和版本历史可读性。

查看技能

huggingface-tokenizers

文档处理

HuggingFace Tokenizers 提供了基于 Rust 的高性能分词工具,支持 BPE、WordPiece 和 Unigram 算法,能在一分钟内处理 1GB 文本。它适用于需要快速分词或训练自定义词汇表的场景,并能无缝集成到 transformers 库中。开发者可以借助它进行对齐跟踪、填充截断等操作,满足从研究到生产的全流程需求。

查看技能

nano-pdf

文档处理

nano-pdf 让开发者能用自然语言指令直接编辑PDF文件,无需手动操作复杂工具。它通过命令行快速修改指定页面内容,如修正拼写错误或更新标题,适合处理日常文档微调。使用前请注意核对页码和输出结果,确保修改准确无误。

查看技能