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Agent Skill · 研究检索

intent-explorer(意图探索者)

intent-explorer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

intent-explorer 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 66,577。

总安装 261周安装 11GitHub Stars 34下载 92核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:intent-explorer(意图探索者)
来源仓库:https://github.com/fimoklei/pm-ai-playbook
仓库路径:skills/intent-explorer
安装命令:
npx skills add https://github.com/fimoklei/pm-ai-playbook --skill intent-explorer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/fimoklei/pm-ai-playbook --skill intent-explorer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。支持从来源仓库获取上下文,结合安装命令和原始 README 核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

适用于需要快速获取信息或背景知识的场景,提升研究效率。建议在提示词中明确搜索范围和筛选条件,避免无关结果干扰。

来源仓库内路径:skills/intent-explorer

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/fimoklei/pm-ai-playbook --skill intent-explore…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

intent-explorer 怎么安装?

在终端执行 npx skills add https://github.com/fimoklei/pm-ai-playbook --skill intent-explorer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

intent-explorer 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

intent-explorer 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

intent-explorer 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

intent-explorer 用的人多吗?

公开数据是累计安装 261 次,近一周安装 11 次,下载 92 次,GitHub 34 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

intent-explorer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
intent-explorer

意图探索者 · 本页

26134需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Intent Explorer

Mission

Explore the intent behind a problem or idea through structured interactive dialogue. Produce an intentSpec — a structured, agent-ready specification precise enough that an AI agent can execute it without guessing.

When to Use

  • Starting a new feature and the user problem isn't well-defined
  • Ambiguous goals where stakeholders disagree on what to build
  • Before /kiro:spec-init when no intent-spec.md or discovery.md exists
  • User says "what should we build", "what's the real problem", "explore this idea"
  • High-stakes decisions where getting the intent wrong is costly

When NOT to Use

  • Bug fixes with clear reproduction steps — just fix them
  • Well-scoped tasks where the user already has measurable success criteria
  • When intent-spec.md already exists for this feature
  • Pure implementation questions ("how do I build X") — this explores *what* and *why*, not *how*

Hard Rules

  • Synthesize, don't transcribe — elevate raw answers to product language
  • Validate before writing — present the full intentSpec draft for approval before writing to disk
  • No solution design — this skill explores intent, not implementation

Interaction Model — CRITICAL

This is a conversational skill. Each question-answer pair is one turn.

Per turn:

  1. Ask exactly ONE question using AskUserQuestion (with 2-4 options plus "Other" for free text)
  2. STOP. Wait for the user's response.
  3. Read and incorporate their answer
  4. Adapt the next question based on what you learned
  5. Repeat

NEVER call AskUserQuestion more than once per response. Each response contains at most one AskUserQuestion call. Between questions, you may add a brief synthesis of what you heard (1-2 sentences max) to show you're tracking.

AskUserQuestion format — every question MUST have options:

AskUserQuestion with:
- question: "Your question here?"
- options: 2-4 concrete choices relevant to the problem context
- The user can always select "Other" for free-text input

Entry Point

  1. Read the problem description from $ARGUMENTS
  2. Ask the user to choose exploration depth: "What depth of exploration does this need?" Quick — Well-scoped change, known user problem (~6-8 questions) Deep — Ambiguous goal, novel territory, high stakes (~15-20 questions)
  3. Generate a feature slug from the description (kebab-case, 2-4 words)
  4. Create .kiro/specs/<slug>/ directory if it doesn't exist
  5. Proceed to the selected mode

Quick Mode — JTBD-Focused Exploration

Read references/jtbd-framework.md for methodology, examples, and calibration.

Phase: Context & JTBD (~6-8 questions)

Cover these topics through conversation. Ask ONE question per turn, wait for the answer, then ask the next. Adapt phrasing and options to the specific problem. Skip or combine topics if the user's earlier answers already covered them.

Topics to cover (in rough order):

  • Situation & trigger — What's happening that makes this a priority now?
  • Who — Who has this problem? What's their role/context?
  • Functional job — What is this person trying to accomplish? (Format: "Help me [verb] [object]")
  • Emotional dimension — How do they want to feel? (Format: "Make me feel [emotion]")
  • Social dimension — How do they want to be perceived? (Skip if not contextually relevant)
  • Current alternatives — What do they do today instead? Think through the job lens — real competition, not category competition. (Netflix competes with sleep, not just Hulu.)
  • Residual pain — What's frustrating or broken about those alternatives?
  • Desired outcomes — What does measurable success look like? (Push for 2-3 specific criteria)

Synthesis

After all questions, synthesize:

  1. Job Statement: "When [situation], I want [motivation], so I can [outcome]"
  2. Forces of Progress: Assess Push + Pull vs Anxiety + Habit
  3. Opportunity Score: Rate importance and current satisfaction

Present the synthesis and ask: "Does this capture the intent accurately?"

If yes → proceed to intentSpec generation. If no → ask which part needs correction, adjust, and re-validate.

Deep Mode — Three Compounding Lenses

Phase 1: JTBD Exploration (~6-8 questions)

Same as Quick Mode above. Produces JTBD statement + Forces + Opportunity Score.

After synthesis, checkpoint: "Phase 1 complete. Does this track before we go deeper?"

Phase 2: First Principles Decomposition (~5-6 questions)

Read references/first-principles-framework.md for methodology, traps, and examples.

Cover these topics ONE question per turn. Wait for each answer before proceeding.

  • Surface assumptions — Present 3-5 candidate assumptions derived from Phase 1 answers. Ask which the user would add or challenge.
  • Trap check — For challenged assumptions, check against the 4 traps (Industry Standard, Customer Said, Technology Exists, Competitor Does It). Present findings and ask for agreement.
  • Decompose to atoms — Walk down the decomposition ladder (Surface → Functional → Emotional → Fundamental → Atomic). Ask: "Does this hit bedrock?"
  • Rebuild — Present a rebuilt direction from fundamental truths only. Ask for reaction.
  • Compare — "If we were starting from scratch today with no legacy, would we build it this way?"

Synthesis checkpoint: "Phase 2 complete. Here are the assumptions we challenged and the fundamental truths we found. Does this hold?"

Phase 3: Cross-Pollination (~4-5 questions)

Read references/cross-pollination-framework.md for the industry library and examples.

Cover these topics ONE question per turn. Wait for each answer before proceeding.

  • Strip context — Restate the core job without domain jargon. Ask: "Is this the right framing?"
  • Industry lookup — Propose 3 surprising industries as AskUserQuestion options. Ask which 1-2 to explore.
  • Extract principles — For selected industries, present how they solve it and the underlying principle. Ask which principles resonate.
  • Translate — Adapt strongest principles to the user's context. Ask: "Does this spark anything?"

Synthesis: "Phase 3 complete. Here's the combined cross-pollination insight."

IntentSpec Generation

After all phases complete:

  1. Read templates/intent-spec.md for the output structure
  2. Fill in all sections from the exploration:

- Objective — from Phase 1 situation + trigger + JTBD evidence - User Goal — job statement + dimensions + forces + opportunity score - Outcomes — from desired outcomes question, refined to be measurable - Edge Cases — inferred from alternatives, pain points, and assumptions - Verification — derived from outcomes, stated as checkable criteria - Deep Exploration (deep mode only) — assumptions table, rebuilt direction, cross-pollination table + synthesis

  1. Replace template placeholders:

- {{FEATURE_NAME}} → generated feature name - {{DATE}} → current ISO date - {{SLUG}} → feature slug

  1. Present the complete intentSpec to the user: "Here's the full intentSpec. Review each section — does this capture the intent?"
  2. If approved → write to .kiro/specs/<slug>/intent-spec.md
  3. If needs changes → adjust and re-present

Post-Write: Workflow Integration

After writing the intentSpec, ask:

"IntentSpec saved to .kiro/specs/<slug>/intent-spec.md. Continue into the spec workflow?" - Yes — run /kiro:spec-init to initialize the spec structure using this intentSpec as grounding - No — done for now — the intentSpec stands alone

If yes: respond with /kiro:spec-init "<original description>" — spec-init will detect the existing intent-spec.md and use it as grounding context.

Common Mistakes

  • Rapid-firing AskUserQuestion — Calling AskUserQuestion multiple times in one response. Claude sees a list of topics and tries to execute them all at once. STOP after each question. One AskUserQuestion per response, always.
  • Batching questions — Asking 3 questions at once collapses the dialogue. Users give shallow answers. One at a time always.
  • Transcribing instead of synthesizing — Don't parrot user words back. Elevate "it's annoying when the page loads slow" to "perceived performance degrades trust during critical decision moments."
  • Skipping the social dimension without thought — Don't auto-skip question 5. Briefly consider whether the social job is relevant, then decide.
  • Jumping to solutions — "We should add a dashboard" is solution design, not intent exploration. If the user proposes solutions, redirect: "What outcome would that dashboard achieve?"
  • Writing intentSpec before approval — Always present the full draft inline first. Writing to disk before user says "yes" creates cleanup work.
  • Weak outcomes — "Improve user experience" is not measurable. Push for specifics: "Reduce task completion from 6 clicks to 2."

Reference Materials

The following references contain the methodologies, frameworks, and calibration examples that power each exploration phase:

  • references/jtbd-framework.md — JTBD methodology, forces of progress, interview techniques, opportunity scoring, 4 calibration examples
  • references/first-principles-framework.md — decomposition ladder, 4 assumption traps, cutting questions, integrated Stanford + MIT framework, 4 calibration examples
  • references/cross-pollination-framework.md — industry inspiration library, principle extraction, adapt-don't-copy rule, 4 calibration examples
  • references/intent-engineering.md — intentSpec definition and principles from pathmode.io

平台分布

Codex

34.56%
按下载量换算32

Claude

28.45%
按下载量换算26

Cursor

19.14%
按下载量换算18

Gemini CLI

9.33%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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