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Agent Skill · 研究检索

customer-discovery(客户发现)

customer-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。

customer-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 73,674 个 Skill 里排第 30,240。

总安装 2,913周安装 119GitHub Stars 85下载 933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:customer-discovery(客户发现)
来源仓库:https://github.com/guia-matthieu/clawfu-skills
仓库路径:skills/customer-discovery
安装命令:
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill customer-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/guia-matthieu/clawfu-skills --skill customer-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。

customer-discovery 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。通过 npx skills add 命令从指定 GitHub 仓库安装并使用。

安装前需确认权限范围和维护状态,注意是否会触发联网或文件读写操作。建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/customer-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/guia-matthieu/clawfu-skills --skill customer-d…;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

customer-discovery 怎么安装?

在终端执行 npx skills add https://github.com/guia-matthieu/clawfu-skills --skill customer-discovery。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

customer-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。

customer-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。

customer-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。

customer-discovery 用的人多吗?

公开数据是累计安装 2,913 次,近一周安装 119 次,下载 933 次,GitHub 85 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。

customer-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
customer-discovery

客户发现 · 本页

2,91385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

Customer Discovery

Systematically validate your business hypotheses before building anything. Master Steve Blank's Customer Development methodology that became the foundation of Lean Startup and YC's approach.

When to Use This Skill

  • Starting a new venture to avoid building something nobody wants
  • Before writing a line of code to validate problem-solution fit
  • Pivoting decisions to systematically test new directions
  • Early-stage fundraising to prove market validation
  • Product roadmap planning to prioritize based on validated customer needs
  • Entering new markets to understand unfamiliar customer segments

Methodology Foundation

AspectDetails
SourceSteve Blank - "The Four Steps to the Epiphany" (2005)
Core Principle"No business plan survives first contact with customers. Get out of the building."
Why This MattersStartups fail because they build products nobody wants. Customer Discovery replaces guessing with systematic learning before you run out of money.

What Claude Does vs What You Decide

Claude DoesYou Decide
Structures content frameworksFinal messaging
Suggests persuasion techniquesBrand voice
Creates draft variationsVersion selection
Identifies optimization opportunitiesPublication timing
Analyzes competitor approachesStrategic direction

What This Skill Does

  1. Documents business hypotheses - Turns assumptions into testable statements
  2. Designs validation experiments - Creates tests that can prove you wrong
  3. Structures customer conversations - Gets real insights, not false positives
  4. Evaluates problem-solution fit - Determines if you should proceed, pivot, or stop
  5. Creates learning roadmap - Prioritizes what to validate first
  6. Tracks validation progress - Maintains evidence-based decision making

How to Use

Start Customer Discovery for a New Idea

I'm starting customer discovery for [business idea].
Help me document my hypotheses and design validation experiments.
Use Steve Blank's Customer Development methodology.

Evaluate Customer Discovery Progress

Here's what I've learned from [X] customer interviews: [summary]
Evaluate my progress against Customer Discovery criteria.
Should I proceed, pivot, or go back to more interviews?

Design a Validation Experiment

I need to validate this hypothesis: [hypothesis]
Design a Customer Discovery experiment to test it.
What would prove it true? What would prove it false?

Instructions

When helping with Customer Discovery, follow Steve Blank's systematic approach:

Step 1: Document Your Business Model Hypotheses

## Business Model Canvas Hypotheses

Before talking to customers, document what you BELIEVE to be true.
These are GUESSES until validated.

### Customer Hypotheses
| # | Hypothesis | Confidence | Evidence |
|---|------------|------------|----------|
| C1 | Our target customer is [WHO] | Low/Med/High | [None yet] |
| C2 | They have this problem: [WHAT] | Low/Med/High | [None yet] |
| C3 | The problem is severe because [WHY] | Low/Med/High | [None yet] |
| C4 | They currently solve it by [HOW] | Low/Med/High | [None yet] |

### Value Proposition Hypotheses
| # | Hypothesis | Confidence | Evidence |
|---|------------|------------|----------|
| V1 | Our solution provides [BENEFIT] | Low/Med/High | [None yet] |
| V2 | Customers care about [FEATURE/OUTCOME] | Low/Med/High | [None yet] |
| V3 | We're differentiated by [UNIQUE VALUE] | Low/Med/High | [None yet] |

### Channel Hypotheses
| # | Hypothesis | Confidence | Evidence |
|---|------------|------------|----------|
| H1 | We can reach customers through [CHANNEL] | Low/Med/High | [None yet] |
| H2 | Customer acquisition will cost [ESTIMATE] | Low/Med/High | [None yet] |

### Revenue Hypotheses
| # | Hypothesis | Confidence | Evidence |
|---|------------|------------|----------|
| R1 | Customers will pay [PRICE] | Low/Med/High | [None yet] |
| R2 | Revenue model: [TYPE] | Low/Med/High | [None yet] |
| R3 | Lifetime value: [ESTIMATE] | Low/Med/High | [None yet] |

Critical Rule: You don't have a business until these hypotheses are validated with real evidence from real customers.


Step 2: Prioritize What to Validate First

## Hypothesis Prioritization Matrix

Prioritize by RISK and IMPACT:

| Hypothesis | If WRONG, Impact | Current Confidence | Priority |
|------------|------------------|-------------------|----------|
| [H1] | Fatal/Major/Minor | Low/Med/High | P1/P2/P3 |
| [H2] | Fatal/Major/Minor | Low/Med/High | P1/P2/P3 |

### Priority Rules:
- P1: Fatal if wrong + Low confidence → Validate FIRST
- P2: Major if wrong + Low/Med confidence → Validate SOON
- P3: Minor if wrong OR High confidence → Validate LATER

### Your P1 Hypotheses (Validate These First):
1. ________________________________
2. ________________________________
3. ________________________________

These are your "leap of faith" assumptions.

Step 3: Design Validation Experiments

## Experiment Design Template

### Hypothesis Being Tested:
"We believe [CUSTOMER SEGMENT] has [PROBLEM] and would [BEHAVIOR]."

### Experiment Type:
- [ ] Customer Interviews (Problem Discovery)
- [ ] Solution Interviews (Solution Validation)
- [ ] Landing Page Test (Demand Validation)
- [ ] Concierge MVP (Solution Validation)
- [ ] Smoke Test (Demand Validation)

### Success Criteria:
**Validated if:** [Specific, measurable outcome]
**Invalidated if:** [Specific, measurable outcome]

Example:
- Validated: 8+ of 10 customers describe this exact problem unprompted
- Invalidated: Fewer than 5 of 10 mention this problem

### Sample Size:
- Minimum: [Number] customers
- Target segment: [Description]

### Data to Collect:
1.
2.
3.

### Timeline:
- Start: [Date]
- End: [Date]
- Go/No-Go Decision: [Date]

Step 4: Conduct Customer Discovery Interviews

## Customer Discovery Interview Framework

### PHASE 1: Problem Discovery (First 5-10 interviews)

**Goal:** Understand the problem space, NOT pitch solutions

**Interview Structure:**
1. **Context** (5 min): Their role, background, day-to-day
2. **Problem Exploration** (15 min): Deep dive into the problem area
3. **Existing Solutions** (5 min): What they use today
4. **Impact** (5 min): How the problem affects them
5. **Wrap-up** (5 min): Referrals, next steps

**Key Questions:**
- "Tell me about your biggest challenge with [area]..."
- "Walk me through the last time you dealt with [problem]..."
- "What solutions have you tried? What worked/didn't work?"
- "How much time/money does this cost you?"
- "If this magically disappeared, what would change?"

**DO NOT:**
- Pitch your solution
- Ask leading questions
- Talk more than 30% of the time
- Ask "would you use/pay for..."

### PHASE 2: Solution Discovery (Next 5-10 interviews)

**Goal:** Test if your solution addresses validated problems

**Only proceed to Phase 2 if:**
- [ ] You've talked to 10+ potential customers
- [ ] Clear problem patterns emerged
- [ ] Problems are severe and frequent enough
- [ ] Customers are actively seeking solutions

**Solution Interview Structure:**
1. **Recap** (3 min): Confirm the problem you heard
2. **Solution Demo** (10 min): Show concept/prototype
3. **Reaction** (10 min): Their honest feedback
4. **Commitment** (5 min): Would they try/buy/refer?

**Key Questions:**
- "Does this solve the problem you described?"
- "What's missing?"
- "What would you pay for this?"
- "Would you be willing to pilot this?"
- "Who else should see this?"

Step 5: Evaluate Results and Decide

## Customer Discovery Scorecard

### Problem Validation

| Criteria | Threshold | Your Result | Pass? |
|----------|-----------|-------------|-------|
| # of interviews completed | 10+ | | Y/N |
| % who confirmed the problem unprompted | 70%+ | | Y/N |
| Problem severity (1-10 avg) | 7+ | | Y/N |
| Already spending money on solutions | 50%+ | | Y/N |
| Actively seeking alternatives | 30%+ | | Y/N |

**Problem Validated:** All Y → Proceed to Solution Validation
**Problem NOT Validated:** Any N → Pivot or dig deeper

### Solution Validation

| Criteria | Threshold | Your Result | Pass? |
|----------|-----------|-------------|-------|
| # of solution interviews | 10+ | | Y/N |
| Said "this solves my problem" | 70%+ | | Y/N |
| Willing to try/pilot | 50%+ | | Y/N |
| Willing to pay (some amount) | 30%+ | | Y/N |
| Gave commitment (deposit, LOI, intro) | 3+ people | | Y/N |

**Solution Validated:** All Y → Proceed to MVP
**Solution NOT Validated:** Any N → Iterate or pivot

### Decision Framework

IF Problem NOT Validated: → PIVOT: Different customer segment? → PIVOT: Different problem? → STOP: No market here?

IF Problem Validated BUT Solution NOT Validated: → ITERATE: Different solution to same problem → ITERATE: Different features/approach

IF Both Validated: → BUILD: Minimum Viable Product → SELL: Pre-sales before building


Step 6: Document Learnings and Update Hypotheses

## Customer Discovery Learning Log

### Round [#] Summary

**Dates:** [Start] - [End]
**Interviews Conducted:** [#]
**Customer Segments Covered:** [List]

### Validated Hypotheses ✅
| Hypothesis | Evidence | Confidence Now |
|------------|----------|----------------|
| [H1] | [Quotes/Data] | High |

### Invalidated Hypotheses ❌
| Hypothesis | Evidence | Next Step |
|------------|----------|-----------|
| [H2] | [Quotes/Data] | Pivot to... |

### Surprises / New Insights 🔍
| Insight | Implication |
|---------|-------------|
| [Learning] | [What this means] |

### Updated Business Model
[What changed based on learnings]

### Next Validation Round
| Hypothesis to Test | Experiment | Timeline |
|--------------------|------------|----------|
| [H3] | [Method] | [Dates] |

Examples

Example 1: B2B SaaS - HR Tech Startup

Context: Founder believes mid-size companies need better employee onboarding software.

Input:

"I want to build HR onboarding software for mid-size companies. Help me set up Customer Discovery."

Output:

Business Model Hypotheses:

#HypothesisRisk if WrongPriority
C1HR managers at 100-500 employee companies manage onboardingFatalP1
C2Current onboarding is manual, time-consuming (5+ hrs/new hire)FatalP1
C3Poor onboarding causes turnover they care aboutFatalP1
V1Automated workflows would save 3+ hours per new hireMajorP2
V2They'd pay $50-100/employee/month for thisMajorP2
H1We can reach HR managers through LinkedInMinorP3

P1 Validation Plan:

Experiment 1: Problem Discovery

  • Target: 15 HR managers at 100-500 employee companies
  • Method: 30-minute phone interviews
  • Questions:

1. "Walk me through what happens when you hire someone..." 2. "What's the biggest headache in that process?" 3. "How much time does onboarding take per new hire?" 4. "What tools are you using today?" 5. "What's the cost when onboarding goes poorly?"

Success Criteria:

  • Validated if: 10+ of 15 describe manual, time-consuming process; 8+ mention specific pain points
  • Invalidated if: Most say "onboarding is fine" or <4 hours per hire

Sample Interview Guide:

Opening: "I'm researching how mid-size companies handle employee onboarding.
Not selling anything - just learning. Mind if I ask some questions?"

Problem Exploration:
1. "When you hired your last employee, walk me through the onboarding process..."
2. "What took the most time?"
3. "Where do things typically go wrong?"
4. "Tell me about a time onboarding didn't go well..."

Current State:
5. "What tools or systems are you using for this?"
6. "What do you like about your current process?"
7. "What's frustrating about it?"

Impact:
8. "How does onboarding quality affect employee retention?"
9. "If you could wave a magic wand, what would change?"

Close:
10. "Who else should I talk to about this?"
11. "Can I follow up in a few weeks to share what I'm learning?"

Example 2: Consumer App - Fitness

Context: Founder wants to build a fitness app for busy parents.

Input:

"I did 12 customer discovery interviews. Here's my summary. Evaluate my progress."

Interview Summary:

  • 12 interviews with working parents, ages 30-45
  • 10/12 said they want to exercise more
  • 8/12 said lack of time is the main barrier
  • 6/12 have tried fitness apps before
  • Only 2/12 are currently paying for any fitness solution
  • 4/12 said they'd "probably" use a new app

Output:

Customer Discovery Evaluation:

CriteriaThresholdYour ResultPass?
Interviews completed10+12✅
Problem confirmed unprompted70%+83% (10/12)✅
Problem severity7+Unknown⚠️
Spending on solutions50%+17% (2/12)❌
Actively seeking alternatives30%+Unknown⚠️

Analysis:

Good Signs:

  • Strong problem confirmation (want to exercise, no time)
  • Consistent pattern across interviews

Warning Signs:

  • Only 17% paying for anything (low willingness to pay)
  • "Probably use" is weak signal (opinions, not commitment)
  • 6 tried apps, only 2 still paying (high churn)

Missing Data:

  • How severe is the pain? (scale 1-10)
  • What have they tried recently?
  • What would they pay? (past behavior, not predictions)

Diagnosis: Problem validated, but MARKET not validated.

The problem is real (busy parents want to exercise). But evidence suggests:

  • People SAY they want this but don't PAY for it
  • High awareness but low conversion/retention
  • "Nice to have" not "must have"

Recommendation: Go Deeper Before Building

Next 5 interviews, focus on:

  1. "Tell me about a fitness product you actually paid for and kept using..."
  2. "What made you stop using [previous app]?"
  3. "What's different about the 2 of you who are paying now vs. when you weren't?"
  4. "If I told you this app costs $20/month, what's your reaction?"
  5. "What would make fitness a MUST instead of NICE TO HAVE?"

Decision: Do not build yet. Either find a more acute pain point within this segment, or find a segment that demonstrates stronger buying behavior.


Example 3: Pivot Decision

Context: Founder validated problem but solution bombed.

Input:

"Problem validated: Small business owners hate bookkeeping. But when I showed my automated bookkeeping tool, response was lukewarm. 3 of 10 were excited. Should I pivot?"

Output:

Situation Analysis:

PhaseResult
Problem Discovery✅ Validated
Solution Discovery❌ Failed (30% < 70% threshold)

Questions Before Pivoting:

  1. Who were the 3 excited customers?

- What made them different? - Same problem, different intensity? - Different sub-segment?

  1. Why were 7 NOT excited?

- Wrong solution to right problem? - Right solution, wrong positioning? - Problem not severe enough for them? - Already have adequate solution?

  1. What specifically was lukewarm?

- "Too complex" - "Already have something" - "Doesn't solve my real problem" - "Price too high"

Pivot Decision Framework:

┌─────────────────────────────────────────────────────────┐
│                 SOLUTION FAILED                          │
│                                                          │
│  Is the problem still validated?                         │
│  └── YES → Solution Pivot (keep problem, change solution)│
│                                                          │
│  Were some customers excited?                            │
│  └── YES → Segment Pivot (narrow to excited segment)     │
│                                                          │
│  Did feedback suggest different problem?                 │
│  └── YES → Problem Pivot (keep customer, find new problem)│
│                                                          │
│  Is there any signal at all?                             │
│  └── NO → Consider stopping or major pivot               │
└─────────────────────────────────────────────────────────┘

Recommended Next Steps:

  1. Interview the 3 excited customers deeply

- What specifically excited them? - What would they pay? - Would they commit to pilot?

  1. Understand the 7 "no"s

- What would have made them excited? - What are they using instead?

  1. Consider these pivots:

- Segment Pivot: Target the excited segment only - Solution Pivot: Same problem, different solution (maybe simpler?) - Channel Pivot: Same solution, different delivery

  1. Before building anything:

- Get 3 paying pilots or LOIs - "I'll build this if you prepay $X"


Checklists & Templates

Customer Discovery Readiness Checklist

## Before Starting Customer Discovery

### Preparation
- [ ] Business Model Canvas hypotheses documented
- [ ] P1 hypotheses identified (highest risk)
- [ ] Target customer segment defined
- [ ] Interview questions drafted
- [ ] 15+ potential interviews scheduled

### Mindset
- [ ] Committed to learning, not selling
- [ ] Ready to hear "your idea is wrong"
- [ ] Willing to pivot based on evidence
- [ ] No code written yet (or willing to throw away)

### Resources
- [ ] 2-4 weeks allocated for discovery
- [ ] Note-taking system ready
- [ ] Team aligned on validation criteria

Interview Tracking Template

## Customer Discovery Interview Log

| # | Date | Name | Company | Segment | Problem Confirmed? | Solution Fit? | Commitment? | Key Quote |
|---|------|------|---------|---------|-------------------|---------------|-------------|-----------|
| 1 | | | | | Y/N | Y/N/NA | | |
| 2 | | | | | Y/N | Y/N/NA | | |
| 3 | | | | | Y/N | Y/N/NA | | |

### Running Totals
- Interviews completed: __/15
- Problem confirmed: __%
- Solution fit (if applicable): __%
- Commitments received: __

Go/No-Go Decision Template

## Customer Discovery Decision Point

**Date:** _______________
**Total Interviews:** _______________
**Hypothesis Being Tested:** _________________________________

### Evidence Summary

| Metric | Target | Actual | Pass? |
|--------|--------|--------|-------|
| Problem confirmation rate | 70%+ | | |
| Average severity score | 7+/10 | | |
| Currently paying for alternatives | 50%+ | | |
| Solution fit (if tested) | 70%+ | | |
| Concrete commitments | 3+ | | |

### Strongest Evidence FOR:
1.
2.
3.

### Strongest Evidence AGAINST:
1.
2.
3.

### Decision

- [ ] **PROCEED:** Build MVP / Start selling
- [ ] **ITERATE:** More interviews needed, adjust approach
- [ ] **PIVOT:** Change [customer/problem/solution]
- [ ] **STOP:** No viable market here

### If Proceeding, Next Steps:
1.
2.
3.

### If Pivoting, New Hypothesis:

Skill Boundaries

What This Skill Does Well

  • Structuring persuasive content
  • Applying copywriting frameworks
  • Creating draft variations
  • Analyzing competitor approaches

What This Skill Cannot Do

  • Guarantee conversion rates
  • Replace brand voice development
  • Know your specific audience
  • Make final approval decisions

References

  • Blank, Steve. "The Four Steps to the Epiphany" (2005) - Original methodology
  • Blank, Steve & Dorf, Bob. "The Startup Owner's Manual" (2012) - Expanded playbook
  • Ries, Eric. "The Lean Startup" (2011) - Build-Measure-Learn context
  • Fitzpatrick, Rob. "The Mom Test" (2013) - Interview techniques
  • Osterwalder, Alex. "Business Model Generation" (2010) - BMC framework
  • Maurya, Ash. "Running Lean" (2012) - Lean Canvas approach

Related Skills


Skill Metadata (Internal Use)

name: customer-discovery
category: validation
subcategory: methodology
version: 1.0
author: MKTG Skills
source_expert: Steve Blank
source_work: The Four Steps to the Epiphany
difficulty: intermediate
estimated_value: $5,000 startup consulting engagement
tags: [customer-development, validation, startups, YC, lean-startup, interviews]
created: 2026-01-25
updated: 2026-01-25

平台分布

Codex

35.47%
按下载量换算331

Claude

31.89%
按下载量换算298

Cursor

17.55%
按下载量换算164

Gemini CLI

9.5%
按下载量换算89

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

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安装前确认

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