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Agent Skill · 研究检索

product-discovery(产品发现)

product-discovery 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。

product-discovery 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,权限为只读;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 67,439。

总安装 245周安装 10GitHub Stars 31下载 79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
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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:product-discovery(产品发现)
来源仓库:https://github.com/ericgandrade/claude-superskills
仓库路径:skills/product-discovery
安装命令:
npx skills add https://github.com/ericgandrade/claude-superskills --skill product-discovery
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/ericgandrade/claude-superskills --skill product-discovery

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。

product-discovery 持续挖掘客户真实痛点与可行解决方案假设。维护机会-解决方案映射表,指导实验设计与快速迭代方向。避免“分析瘫痪”与“功能工厂”两种极端,平衡洞察深度与开发速度。适用于早期项目验证假设或成熟产品寻找第二增长曲线的场景。输出需包含失败经验归档机制,防止重复踩坑浪费资源。

来源仓库内路径:skills/product-discovery

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/ericgandrade/claude-superskills --skill produc…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

product-discovery 怎么安装?

在终端执行 npx skills add https://github.com/ericgandrade/claude-superskills --skill product-discover…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

product-discovery 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。

product-discovery 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

product-discovery 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。

product-discovery 用的人多吗?

公开数据是累计安装 245 次,近一周安装 10 次,下载 79 次,GitHub 31 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。

product-discovery 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
product-discovery

产品发现 · 本页

24531只读未明确通过

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

Product Discovery System

"Discovery without delivery = analysis paralysis. Delivery without discovery = feature factory."

This skill covers the Discovery System — continuously discovering which problems matter and which solutions might work. It maintains a living map of customer opportunities and tests solution ideas before committing engineering resources.

Related skills: product-strategy, product-architecture, product-delivery, ai-native-product, product-leadership


When to Use This Skill

Use this skill when:

  • Setting up a weekly discovery rhythm
  • Building or updating an Opportunity Solution Tree (OST)
  • Creating interview snapshots after customer conversations
  • Exploring multiple solution approaches
  • Designing and running assumption tests
  • Synthesizing insights across multiple interviews
  • Running an Opportunity Council meeting

Cadence: Weekly rhythm | Owner: Product Trio (PM + Designer + Tech Lead)


The Problem This Solves

Most teams either:

  1. Do discovery once, then execute for months on stale assumptions
  2. Skip discovery entirely and build what stakeholders request
  3. Do discovery but don't connect it to what actually gets built

The Discovery System creates a weekly rhythm that keeps you close to customers and ensures evidence—not opinions—drives decisions.


Philosophy

Core Beliefs

  1. Weekly rhythm over big research projects — 2-3 interviews per week beats quarterly research sprints
  2. The crossfunctional discovery — Handoffs kill learning
  3. Opportunities are problems, not solutions — "Users need faster onboarding" not "Add a wizard"
  4. Multiple solutions per opportunity — Always explore 3+ options before committing
  5. Test in hours and days, not weeks — If your test takes a month, you're testing too much

What This Framework Rejects

  • Discovery theater (interviews that don't change roadmap)
  • Solution-first thinking
  • PM does interviews alone, hands notes to designers
  • Building the first idea that comes to mind
  • Waiting for perfect data before deciding

Progress Tracking

Display progress during discovery sessions:

[████░░░░░░░░░░░░░░░░] 25% — Phase 1/4: Setting Up Discovery Rhythm
[████████░░░░░░░░░░░░] 50% — Phase 2/4: Customer Interviews & Opportunity Mapping
[████████████░░░░░░░░] 75% — Phase 3/4: Solution Exploration & Assumption Testing
[████████████████████] 100% — Phase 4/4: Synthesizing Insights & Prioritizing

Framework Components

1. Continuous Discovery Habits

The Weekly Rhythm (Minimum Viable Discovery)

ActivityFrequencyPurpose
Customer interviews2-3 per weekStay connected to real problems
Synthesis session1 per weekUpdate opportunity map
Assumption test1 per weekValidate before building

Who Does Discovery: The Product Trio

RoleContribution
Product ManagerOwns outcome, facilitates, synthesizes
Product DesignerOwns experience, visualizes, prototypes
Tech LeadOwns feasibility, estimates, identifies constraints
Principle: The trio does discovery together. If the PM does interviews alone and hands notes to designers, you've already lost 50% of the insight.

0→1 Mode:

  • Founder does interviews personally
  • 10-15 interviews before patterns emerge
  • Daily cadence if possible
  • Bias toward speed over rigor

Scaling Mode:

  • Research ops supports logistics
  • Systematic interview quotas by segment
  • Centralized insight repository
  • Quarterly synthesis reports

2. Interview Snapshots

After each interview, create a snapshot (not a transcript). Capture the essence, not every word.

Snapshot Format:

INTERVIEW SNAPSHOT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Date: [Date]
Participant: [Role, Company type, Context]
Interviewer(s): [Names]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

KEY OPPORTUNITIES (Unmet needs discovered)
• [Opportunity #1]
• [Opportunity #2]
• [Opportunity #3]

KEY QUOTE (In their words)
"[Memorable statement that captures their experience]"

QUICK FACTS
• [Relevant context about their situation]
• [Current workflow or tools]
• [Constraints or requirements]

JOBS TO BE DONE (If surfaced)
• Functional: [Task they're trying to accomplish]
• Emotional: [How they want to feel]
• Social: [How they want to be perceived]

SURPRISES
• [Anything unexpected]
• [Assumptions challenged]

FOLLOW-UPS
• [Questions for next interview]
• [Things to validate]

AI Integration for Snapshots:

  • Use AI to draft snapshot from notes
  • Always review — AI misses 20-40% of important context
  • Never let AI replace the act of listening

3. Opportunity Solution Tree (OST)

The OST is your living map connecting outcomes to opportunities to solutions to tests.

Structure:

                    OUTCOME
                    (Metric we're trying to move)
                         │
          ┌──────────────┼──────────────┐
          │              │              │
     OPPORTUNITY    OPPORTUNITY    OPPORTUNITY
     (Unmet need)   (Unmet need)   (Unmet need)
          │              │              │
     ┌────┴────┐    ┌────┴────┐    ┌────┴────┐
     │         │    │         │    │         │
  SOLUTION  SOLUTION SOLUTION SOLUTION SOLUTION SOLUTION
  (Idea)    (Idea)  (Idea)   (Idea)  (Idea)   (Idea)
     │         │
  ┌──┴──┐   ┌──┴──┐
  │     │   │     │
 TEST  TEST TEST  TEST

OST Rules:

RuleWhy
One outcome per treeDon't try to solve everything at once
Opportunities are problems, not solutions"Users struggle to..." not "Add a feature..."
Multiple solutions per opportunityAlways explore 3+ before committing
Evidence-backedEach opportunity has interview/data support
Living documentUpdate weekly as you learn

Good Opportunity Statements:

  • "Users struggle to understand which metrics matter during their first week"
  • "Managers can't quickly see which team members need attention"
  • "New users don't know what to do after signup"

Bad Opportunity Statements (These are solutions):

  • "We need a dashboard"
  • "Add an onboarding wizard"
  • "Send email reminders"

Target Opportunity Selection:

Use compare-and-contrast to select focus:

OpportunityPain SeverityFrequencyStrategic FitEvidence Strength
AHighDailyCore8 interviews
BMediumWeeklyAdjacent3 interviews
CHighMonthlyCore12 interviews
Principle: Choose ONE target opportunity at a time. Complete focus beats scattered effort.

4. Solution Exploration

For every target opportunity, generate at least 3 solution approaches before committing.

The Three Solution Types:

TypeDescriptionExample
The obvious solutionWhat everyone expects"Add an onboarding wizard"
The 10x harder solutionIf effort were no constraint"AI-powered personalized setup"
The non-product solutionPricing, process, partnership, or service"White-glove onboarding call"

Solution Categories:

CategoryWhen to Consider
Product changesFeatures, UX improvements
Pricing/packaging changesHow value is captured
Enablement changesDocumentation, training, support
Process changesHow work gets done internally
Partnership solutionsIntegrate vs. build
Principle: The best solution to a product problem is often not a product change.

Thin-Slice MVP:

Don't build the whole solution. Build the smallest thing that tests your riskiest assumption.

Full SolutionThin Slice
"Complete onboarding wizard with 10 steps, progress tracking, and personalization""Single welcome screen that asks one question and shows one recommendation"
"Full analytics dashboard with customizable widgets""One pre-built view showing the top 3 metrics"
"AI-powered recommendation engine""Rule-based suggestions for top 5 use cases"

5. Assumption Testing

Every solution has assumptions. Find the ones that would kill it if wrong.

Assumption Categories:

CategoryQuestion
DesirabilityWill users want this?
ViabilityWill this work for the business?
FeasibilityCan we build this?
UsabilityCan users figure it out?

Assumption Test Format:

ASSUMPTION TEST
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Assumption: [What we believe is true]
Risk level: [High / Medium / Low]
Test method: [How we'll test]
Success criteria: [What would confirm]
Failure criteria: [What would disprove]
Timebox: [Hours/days, not weeks]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Test Methods (Fastest to Slowest):

MethodTimeWhen to Use
Desk research30 minDoes evidence already exist?
One-question survey1 hourQuick signal from existing users
Fake door test1 dayMeasure interest before building
Concierge test1-3 daysManually deliver the value
Wizard of Oz1 weekFake backend, real frontend
Prototype test1-2 weeksClickable prototype with users
A/B test2-4 weeksLive code, statistical significance
Principle: Test in hours and days, not weeks and months. If your test takes a month, you're testing too much at once.

AI Integration for Testing:

  • Use AI to build prototypes faster (vibe coding)
  • Use AI to analyze survey responses
  • Use AI to synthesize test results
  • Don't use AI to decide — Humans interpret, AI assists

6. Opportunity Council (Scaling Mode)

Weekly cross-functional meeting that turns discovery into decisions. Prevents discovery theater.

Participants:

  • PM (facilitator)
  • Design lead
  • Engineering lead
  • Sales/CS representative (input, not veto)
  • Marketing representative (for GTM alignment)

Agenda (60 min max):

SegmentTimeFocus
New evidence review15 min2-3 key findings from this week
Opportunity prioritization20 minPromote, kill, or park opportunities
Solution shaping15 minReview prototype/test results
GTM/tech flags10 minEarly visibility on constraints

Council Rules:

  • Decisions are recorded with rationale
  • Single decider (PM) — council advises, PM decides
  • No side quests — if it's not on the OST, it waits
  • Evidence required — no "I think users want..."

0→1 Mode: Skip formal council. Founder + team informal sync.


Primary Outputs

OutputDescriptionUpdate Cadence
Opportunity Solution TreeLiving map of outcome → opportunities → solutionsWeekly
Interview snapshotsLibrary of customer evidenceAfter each interview
Test resultsWhat we learned, what we decidedAfter each test
Target opportunityCurrent focus areaWeekly review
Solution candidatesPrototypes ready for prioritizationOngoing

Templates

This skill includes templates in the templates/ directory:

  • interview-snapshot.md — Post-interview capture format
  • opportunity-solution-tree.md — OST structure and rules
  • assumption-test.md — Test design and tracking

Using This Skill with Claude

Ask Claude to:

  1. Set up discovery rhythm: "Help me design a weekly discovery cadence for [team size/stage]"
  2. Create interview guide: "Create an interview guide for understanding [JTBD/opportunity]"
  3. Draft snapshot: "Turn these interview notes into a snapshot"
  4. Build OST: "Help me build an OST for the outcome: [metric]"
  5. Reframe solutions as opportunities: "These are solutions — help me reframe as opportunities"
  6. Generate solution options: "Generate 5 solution approaches for [opportunity]"
  7. Design thin slice: "What's the thin-slice MVP for [solution]?"
  8. Create assumption test: "Design an assumption test for [hypothesis]"
  9. Synthesize interviews: "Find patterns across these [X] interview snapshots"
  10. Prepare Opportunity Council: "Create an agenda for Opportunity Council with these findings"

Connection to Other Skills

When you need to...Use skill
Define ICP and strategic contextproduct-strategy
Convert opportunities to betsproduct-architecture
Plan delivery and measurementproduct-delivery
Discover for AI productsai-native-product
Scale discovery across teamsproduct-leadership

Quick Reference: Discovery Quality Checklist

Before concluding discovery on an opportunity:

  • Evidence breadth — 5+ interviews mentioning this opportunity
  • Evidence depth — Understand functional, emotional, social jobs
  • Multiple solutions explored — 3+ approaches considered
  • Thin slice identified — Know the smallest testable version
  • Riskiest assumption named — Know what would kill this
  • Test designed — Ready to validate before building
  • Strategic fit confirmed — Aligns with strategy canvas

Anti-Patterns to Avoid

Anti-PatternWhy It FailsInstead
Discovery theaterInterviews don't change roadmapEvidence → decisions
AI replaces listeningMiss 40% of insightAI augments, humans decide
Solution-first thinkingBuild wrong thingOpportunity-first
Homer Simpson carFeature bloat from asking "what do you want?"Ask about problems, not solutions
PM does discovery aloneHandoffs kill learningTrio does discovery together
One solution per opportunityMiss better approachesAlways 3+ options
Month-long testsToo slow to learnTest in hours/days

Sources & Influences

  • Teresa Torres — Continuous Discovery Habits, Opportunity Solution Trees
  • Marty Cagan — INSPIRED, EMPOWERED
  • Rob Fitzpatrick — The Mom Test
  • Cindy Alvarez — Lean Customer Development

平台分布

Codex

39.2%
按下载量换算31

Claude

30.11%
按下载量换算24

Cursor

19.4%
按下载量换算15

Gemini CLI

8.88%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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