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Agent Skill · 研究检索

identify-assumptions(确定假设)

identify-assumptions 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。

identify-assumptions 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 38,233。

总安装 1,591周安装 65GitHub Stars 103下载 515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:identify-assumptions(确定假设)
来源仓库:https://github.com/borghei/claude-skills
仓库路径:skills/identify-assumptions
安装命令:
npx skills add https://github.com/borghei/claude-skills --skill identify-assumptions
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/borghei/claude-skills --skill identify-assumptions

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。identify-assumptions 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合根据关键词或任务场景快速定位候选结果。通过 GitHub 安装,支持 Codex、Claude、Cursor 等宿主。安装前建议确认权限范围和维护状态。

可能触发联网、命令执行或文件读写操作。

来源仓库内路径:skills/identify-assumptions

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/borghei/claude-skills --skill identify-assumpt…;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

identify-assumptions 怎么安装?

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

identify-assumptions 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。

identify-assumptions 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。

identify-assumptions 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。

identify-assumptions 用的人多吗?

公开数据是累计安装 1,591 次,近一周安装 65 次,下载 515 次,GitHub 103 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。

identify-assumptions 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
identify-assumptions

确定假设 · 本页

1,591103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

Assumption Mapping Expert

Overview

Systematically identify, categorize, and prioritize the assumptions underlying your product decisions. This skill extends Teresa Torres' four risk categories with four additional categories for new products, and uses a devil's advocate approach from PM, Designer, and Engineer perspectives to surface hidden assumptions.

When to Use

  • After ideation, before committing to build.
  • When a product decision "feels right" but has not been validated.
  • When the team disagrees on risk or priority -- assumptions make disagreements explicit.
  • Before designing experiments -- test the riskiest assumptions first.

Risk Categories

Core 4 Categories (Existing Products)

These four categories come from Teresa Torres' *Continuous Discovery Habits* and cover the primary risks for features within an established product:

CategoryQuestion It AnswersExample Assumption
ValueWill customers want this?"Users will prefer AI-generated summaries over manual note-taking."
UsabilityCan customers figure out how to use it?"Users will understand the drag-and-drop interface without a tutorial."
ViabilityCan the business sustain this?"The feature will generate enough upgrades to justify the engineering cost."
FeasibilityCan we build this?"Our current infrastructure can handle real-time processing at scale."

Extended 8 Categories (New Products)

For new products, four additional risk categories become critical:

CategoryQuestion It AnswersExample Assumption
EthicsShould we build this? Are there unintended harms?"Collecting location data will not create privacy concerns for our target segment."
Go-to-MarketCan we reach and acquire customers?"Our target segment actively searches for solutions on Google, making SEO viable."
Strategy & ObjectivesDoes this align with where we want to go?"Entering the SMB market will not dilute our enterprise positioning."
TeamDo we have the right people and skills?"Our team can learn the required ML skills within the project timeline."

Methodology

Phase 1: Devil's Advocate Assumption Surfacing

For each product idea or decision, adopt three adversarial perspectives:

PM Devil's Advocate "I challenge whether this is worth building."

  • Is there real demand, or are we projecting our own preferences?
  • Will this move the metric we care about?
  • Can we sustain this economically?
  • Does this align with strategy, or is it a distraction?

Designer Devil's Advocate "I challenge whether users will actually use this."

  • Will users discover this feature?
  • Can they complete the task without help?
  • Does this add complexity that hurts the overall experience?
  • Are we designing for edge cases and assuming they are common?

Engineer Devil's Advocate "I challenge whether we can build and maintain this."

  • Do we have the technical skills and infrastructure?
  • What are the hidden dependencies and integration risks?
  • Can this scale if it succeeds?
  • What is the ongoing maintenance burden?

Phase 2: Categorize Each Assumption

For each assumption surfaced, assign:

FieldOptions
DescriptionClear, specific statement of what must be true
Risk CategoryValue / Usability / Viability / Feasibility / Ethics / Go-to-Market / Strategy / Team
ConfidenceHigh (we have strong evidence) / Medium (some evidence, not conclusive) / Low (gut feeling or no evidence)
Impact1-10 scale (if this assumption is wrong, how bad is it?)

Phase 3: Prioritize Using Impact x Risk Matrix

Calculate a risk score for each assumption:

Risk Score = Impact x (1 - Confidence)

Where confidence maps to: High = 0.8, Medium = 0.5, Low = 0.2

ImpactConfidenceRisk ScoreMeaning
9Low (0.2)7.2Critical -- test immediately
9High (0.8)1.8Important but well-understood
3Low (0.2)2.4Low priority
3High (0.8)0.6Ignore

Phase 4: Classify into Quadrants

Place each assumption on a 2x2 matrix:

                    HIGH IMPACT
                        |
     Proceed with       |     Test Now
     Confidence         |     (highest priority)
                        |
  ──────────────────────┼──────────────────────
                        |
     Defer              |     Investigate
     (low priority)     |     (may be important)
                        |
                    LOW IMPACT

         LOW RISK ◄─────┼─────► HIGH RISK
QuadrantImpactRiskAction
Test NowHighHighDesign an experiment immediately
ProceedHighLowMove forward with monitoring
InvestigateLowHighGather more data, may upgrade to Test Now
DeferLowLowAccept the risk, revisit if context changes

Phase 5: Suggest Tests

For each "Test Now" assumption, recommend a validation approach:

Assumption TypeSuggested Test Methods
Value assumptionsCustomer interviews, fake door test, landing page test
Usability assumptionsUsability test (5 users), prototype walkthrough
Viability assumptionsFinancial modeling, pricing experiment, unit economics analysis
Feasibility assumptionsTechnical spike, proof of concept, architecture review
Ethics assumptionsEthics review board, user consent study, regulatory consultation
Go-to-Market assumptionsChannel experiment, SEO keyword test, paid ad test
Strategy assumptionsStrategy review with leadership, competitive analysis
Team assumptionsSkills assessment, hiring timeline analysis, training feasibility

Python Tool: assumption_tracker.py

Track and prioritize assumptions using the CLI tool:

# Run with demo data
python3 scripts/assumption_tracker.py --demo

# Run with custom input
python3 scripts/assumption_tracker.py input.json

# Output as JSON
python3 scripts/assumption_tracker.py input.json --format json

Input Format

{
  "assumptions": [
    {
      "description": "Users will prefer AI summaries over manual notes",
      "category": "value",
      "confidence": "low",
      "impact": 9
    }
  ]
}

Output

Sorted by risk priority with quadrant classification and suggested actions.

See scripts/assumption_tracker.py for full documentation.

Output Format

Assumption Registry

#AssumptionCategoryConfidenceImpactRisk ScoreQuadrant
1...ValueLow97.2Test Now
2...FeasibilityMedium84.0Test Now
3...UsabilityHigh71.4Proceed
4...GTMLow32.4Investigate

Action Plan for "Test Now" Assumptions

For each assumption in the Test Now quadrant, document:

  • Assumption description
  • Why it is high risk
  • Suggested validation method
  • Owner and timeline

Use assets/assumption_map_template.md for the full template.

Integration with Other Discovery Skills

  • Use brainstorm-ideas/ to generate ideas whose assumptions you will map.
  • Feed "Test Now" assumptions into brainstorm-experiments/ for experiment design.
  • Run pre-mortem/ to catch risks that assumption mapping might miss (especially elephants).

Troubleshooting

SymptomLikely CauseResolution
All assumptions classified as "Test Now"Impact scores inflated or confidence levels consistently set to "low"Calibrate impact scoring with team; use relative ranking (force distribution across quadrants)
Validation fails with category errorCategory string does not match valid set exactlyUse lowercase: value, usability, viability, feasibility, ethics, gtm, strategy, team
Risk scores cluster around the same valueImpact and confidence values lack variance across assumptionsUse the full 1-10 impact scale; challenge the team to differentiate high vs. medium vs. low confidence with evidence
Team generates fewer than 10 assumptionsDevil's Advocate perspectives not applied systematicallyRun all three perspectives (PM, Designer, Engineer) independently before combining; use the prompts in Phase 1
"Defer" quadrant is emptyAll assumptions scored as high impact, or low-impact assumptions not capturedInclude operational and edge-case assumptions; not every assumption is existential
Suggested tests are too genericTool uses category-level test suggestions, not assumption-specific onesUse the category suggestion as a starting point; tailor the test method using brainstorm-experiments/ skill
Assumptions not updated after experimentsNo feedback loop from experiment results back to assumption trackerRe-run assumption_tracker.py with updated confidence levels after each experiment completes

Success Criteria

  • All product decisions have at least 8-15 explicit assumptions documented before build commitment
  • Every "Test Now" assumption has an assigned owner, validation method, and timeline
  • Risk scores are calculated consistently using the Impact x (1 - Confidence) formula
  • Assumptions are re-scored after each validation experiment (confidence levels update)
  • At least 80% of "Test Now" assumptions are validated or invalidated before major build decisions
  • Assumption map is reviewed and updated at every product discovery cycle (weekly or bi-weekly)
  • The team can articulate the top 3 riskiest assumptions for any active initiative

Scope & Limitations

In Scope:

  • Systematic assumption identification using PM/Designer/Engineer devil's advocate perspectives
  • 8-category risk classification (Value, Usability, Viability, Feasibility, Ethics, Go-to-Market, Strategy, Team)
  • Quantitative risk scoring using Impact x (1 - Confidence) formula
  • Quadrant classification (Test Now, Proceed, Investigate, Defer) with suggested validation methods
  • Assumption registry with priority sorting and action plan generation

Out of Scope:

  • Running validation experiments (see brainstorm-experiments/ skill)
  • Product strategy or roadmap decisions (see execution/outcome-roadmap/)
  • Technical feasibility deep-dives or architecture reviews (see engineering/ skills)
  • Financial modeling for viability assumptions (see finance/ domain skills)

Important Caveats:

  • Confidence levels (high/medium/low) map to fixed numeric values (0.8/0.5/0.2). This is a simplification; real confidence is continuous.
  • The quadrant threshold for "high impact" is set at 7/10. Adjust this threshold for your risk tolerance.
  • Assumption mapping is most effective when done collaboratively (Product Trio), not by a single person.

Integration Points

IntegrationDirectionDescription
brainstorm-ideas/Receives fromIdeas generated become the subjects whose assumptions are mapped
brainstorm-experiments/Feeds into"Test Now" assumptions become hypotheses for experiment design
pre-mortem/ComplementsPre-mortem catches risks that assumption mapping may miss (especially elephants)
execution/create-prd/Feeds intoValidated assumptions populate the PRD Assumptions section (Section 7)
execution/brainstorm-okrs/Feeds intoViability assumptions inform OKR key result selection and confidence levels
senior-pm/Feeds intoHigh-impact assumptions feed into portfolio risk registers

Tool Reference

assumption_tracker.py

Tracks, scores, and prioritizes product assumptions using an Impact x Risk matrix with quadrant classification.

FlagTypeDefaultDescription
input_filepositional(optional)Path to JSON file with assumptions array
--demoflagoffRun with built-in sample data (8 assumptions across all categories)
--formatchoicetextOutput format: text or json

Input fields per assumption:

  • description (required): Clear statement of what must be true
  • category (required): One of value, usability, viability, feasibility, ethics, gtm, strategy, team
  • confidence (required): One of high, medium, low
  • impact (required): Integer 1-10

References

  • Teresa Torres, *Continuous Discovery Habits* (2021)
  • David J. Bland & Alexander Osterwalder, *Testing Business Ideas* (2019)
  • Ash Maurya, *Running Lean* (2012)
  • Marty Cagan, *Inspired* (2018)

平台分布

Codex

35.13%
按下载量换算181

Claude

31.53%
按下载量换算162

Cursor

17.93%
按下载量换算92

Gemini CLI

8.71%
按下载量换算45

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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