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Agent Skill · 研究检索

generate-creative-ideas(产生创意)

generate-creative-ideas 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。

generate-creative-ideas 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;许可证为 MIT,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 36,120。

总安装 1,978周安装 80GitHub Stars 21下载 621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
MIT

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:generate-creative-ideas(产生创意)
来源仓库:https://github.com/thepexcel/agent-skills
仓库路径:skills/generate-creative-ideas
安装命令:
npx skills add https://github.com/thepexcel/agent-skills --skill generate-creative-ideas
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/thepexcel/agent-skills --skill generate-creative-ideas

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。

generate-creative-ideas 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。当前顶部介绍为空,原始 SKILL.md 摘录未提供。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/generate-creative-ideas

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/thepexcel/agent-skills --skill generate-creati…;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

generate-creative-ideas 怎么安装?

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

generate-creative-ideas 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。

generate-creative-ideas 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。

generate-creative-ideas 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

generate-creative-ideas 用的人多吗?

公开数据是累计安装 1,978 次,近一周安装 80 次,下载 621 次,GitHub 21 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。

generate-creative-ideas 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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 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

  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 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
generate-creative-ideas

产生创意 · 本页

1,97821只读MIT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

Creativity Skill

Workflow

1. Ask       → "What problem/challenge?"
2. Context   → Understand current state BEFORE suggesting
3. Diagnose  → Match situation to technique(s)
4. Generate  → Walk through technique step-by-step
5. Evaluate  → Score and filter ideas
6. Develop   → Shape top ideas into actionable concepts
7. Output    → Structured ideas + next actions (Thai if user uses Thai)

Context questions (step 2):

  • "What do you currently do?"
  • "What have you tried?"
  • "What's working? What's not?"
Anti-pattern: Jumping to solutions without understanding context = generic noise

Situation -> Technique Matrix

SituationTechniquesCombination Recipe
Stuck / No ideasRandom Word, Forced Connections, Oblique StrategiesRandom Word -> Forced Connections -> Dot Vote
Need breakthroughFirst Principles, Challenging Assumptions, Combinatorial EngineHMW -> Worst Idea -> SCAMPER -> Brainwriting
Improve existingSCAMPER, Reverse Brainstorming, TRIZ-AISCAMPER -> Reverse Brainstorm -> Impact/Effort
Explore systematicallyMorphological Box, Six Thinking Hats, Constraint InjectionMorphological Box -> Constraint Injection -> Clustering
Reframe problemHow Might We (HMW), Jobs to be DoneHMW -> JTBD -> First Principles
Team ideation6-3-5 Brainwriting, Multi-Persona ParallelMind Map -> Brainwriting -> Affinity -> Multi-Vote
Too many ideasImpact/Effort Matrix, Idea Clustering, NAF ScoringClustering -> NAF Quick Score -> Impact/Effort
AI ideas too similarDivergence Guard, Constraint Injection, Incubation CyclingDivergence Guard -> Domain Shift -> Incubation
Technical/engineeringTRIZ-AI, First PrinciplesTRIZ -> First Principles -> Assumption Mapping
Cross-domain innovationCombinatorial Engine, Analogical ThinkingCombinatorial Engine -> Analogical -> Constraint
Content ideas (blog/video/course)Content Pillars, Audience Pain Points, Gap AnalysisPillars -> Pain Points -> SCAMPER -> Validate
Business/product ideasJTBD, Opportunity Canvas, Lean ValidationJTBD -> HMW -> Morphological -> ICE Score

Technique Quick Reference

Divergent (Generate)

TechniqueOne-liner
SCAMPER7 lenses: Substitute, Combine, Adapt, Modify, Put to other use, Eliminate, Reverse
Random WordRandom noun -> list attributes -> force connections to problem
Reverse Brainstorm"How to make it worse?" -> Invert each idea
First PrinciplesStrip to fundamentals -> Rebuild from scratch
Six Hats6 perspectives: Facts, Feelings, Risks, Benefits, Ideas, Process
HMWReframe as "How Might We [verb] for [user] so that [outcome]?"
Morphological BoxParameters x Variations matrix -> Combine systematically
Analogies"How does [other domain] solve this?"
AssumptionsList assumptions -> Challenge/invert each
Forced ConnectionsCombine 2 unrelated concepts
Jobs to be Done"When [situation], I want [motivation], so I can [outcome]"
Oblique StrategiesRandom creative prompts to break deadlocks

AI-Optimized (Generate + Diversify)

TechniqueOne-liner
Incubation CyclingGenerate -> Pause -> Fresh restart (no prior context) -> Compare
Combinatorial EngineAbstract -> Retrieve 3 domains -> Generalize -> Combine -> Instantiate
Multi-Persona ParallelRun 4+ personas SIMULTANEOUSLY (not sequentially)
Constraint InjectionAdd random constraint -> Force novel solutions
Divergence GuardForce opposite -> Domain shift -> Absurdity injection
TRIZ-AIApply inventive principles (segmentation, nesting, dynamization, etc.)
Tree of ThoughtsExplore multiple reasoning branches simultaneously (74% vs CoT 49%)

Convergent (Evaluate + Refine)

TechniqueOne-liner
NAF Quick ScoreRate Novelty + Attractiveness + Feasibility (1-10 each)
Impact/Effort Matrix2x2: Quick Wins, Big Bets, Fill-ins, Avoid
ICE ScoringImpact x Confidence x Ease (1-10 each)
Idea ClusteringGroup similar -> Name clusters -> Pick best from each
Dot VotingEach person gets 3-5 votes -> Surface favorites
Assumption MappingMap assumptions on Importance x Certainty -> Test riskiest first

Idea Evaluation

After generating ideas, ALWAYS offer evaluation. Default to NAF Quick Score.

NAF Quick Score (Default)

CriterionQuestionScale
NoveltyHow new/surprising is this?1-10
AttractivenessHow well does it solve the problem?1-10
FeasibilityHow realistic to implement?1-10

Interpretation:

  • Total 24-30: Strong candidate -> develop further
  • Total 18-23: Promising -> refine or combine
  • Total < 18: Weak -> park or discard
  • Feasibility 8+: Worth trying (remaining 20% is implementation)
  • High N+A but low F: Reframe feasibility barriers as new problems to solve

When to Use Which Evaluation

SituationMethod
Quick screening (5+ ideas)NAF or Dot Voting
Growth experimentsICE Scoring
Data-driven product decisionsRICE Scoring
Complex multi-criteriaWeighted Scoring Matrix
Visual team alignmentImpact/Effort 2x2

Rules:

  1. NEVER evaluate during divergent phase -- generate first, judge later
  2. Take a break between generating and evaluating (different mindset)
  3. Different idea types need different criteria (incremental vs disruptive)

Details: evaluation.md


Content Creator Mode

When ideating for blog posts, videos, courses, or social media:

Step 1: Define Content Pillars (3-5 themes)

Pillar = Core theme that reflects expertise + audience needs
Example: Excel -> [Formulas, Data Viz, Automation, Tips & Tricks, Career]

Step 2: Audience-First Ideation

SourceQuestions
Pain PointsWhat frustrates them most?
QuestionsWhat do they repeatedly ask?
GapsWhat's poorly explained by competitors?
WishesWhat do they wish existed?
MistakesWhat common errors do they make?

Step 3: Generate Ideas (use any technique from matrix)

Apply creativity techniques TO the content pillars:

  • SCAMPER on existing popular content
  • Reverse brainstorm: "How to make the worst tutorial?"
  • Analogies: "How would Netflix teach Excel?"

Step 4: Validate Before Creating

[ ] Search demand? (keyword research)
[ ] Real audience pain point? (comments, surveys)
[ ] Can I add unique value? (gap analysis)
[ ] Fits my pillars? (strategy alignment)
[ ] Would I click this? (title/thumbnail test)
[ ] Can be repurposed? (1 piece -> 5+ formats)

Repurposing Chain

Blog post -> YouTube video -> Shorts/Reels -> Social posts -> Email -> Course module

Details: content-ideation.md


Idea Development Pipeline

Shape raw ideas into actionable concepts:

[Generate] -> [Cluster] -> [Evaluate] -> [Develop] -> [Validate] -> [Execute]

Quick Concept Card (for top ideas)

IDEA: [Name]
TAGLINE: [One compelling sentence]
PROBLEM: [What pain it solves]
SOLUTION: [How it works - 2-3 sentences]
TARGET USER: [Who benefits]
KEY INSIGHT: [The "aha" behind this]
EFFORT: [S / M / L]
BIGGEST RISK: [What could go wrong]
QUICKEST TEST: [How to validate cheaply]
NEXT STEP: [One concrete action]

Assumption Mapping (for important ideas)

  1. List assumptions: "What must be true for this to work?"
  2. Categorize: Desirability / Feasibility / Viability
  3. Map on 2x2: Importance (high/low) x Certainty (high/low)
  4. Test high-importance + low-certainty FIRST

Details: idea-pipeline.md


Output Formats

Use these when presenting ideas to the user:

Quick List (5+ ideas)

1. **[Idea Name]** -- [One-line description]
2. **[Idea Name]** -- [One-line description]
...

Scored List (after evaluation)

| # | Idea | N | A | F | Total |
|---|------|---|---|---|-------|
| 1 | ...  | 8 | 9 | 7 | 24    |

Concept Cards (top 3 ideas)

Use the Quick Concept Card format above for each top idea.

Comparison Matrix (deciding between options)

| Criteria     | Idea A | Idea B | Idea C |
|-------------|--------|--------|--------|
| Novelty     | 4/5    | 3/5    | 5/5    |
| Feasibility | 3/5    | 5/5    | 2/5    |
| Impact      | 5/5    | 3/5    | 4/5    |

AI Creativity Guidelines

Key Research Insights (2025-2026)

FindingImplication
Ask AI HOW to think, not WHAT to thinkProcess prompts > product prompts
Human-first ideation preserves diversityUser brainstorms first, THEN AI expands
LLMs match average creativity (52nd percentile)AI is a partner, not a replacement
Homogenization effect (g ~ -0.86)Use Divergence Guard actively
"Creative Scar" -- creativity drops after AI withdrawalDon't outsource all creative thinking
Tree of Thoughts: 74% vs CoT 49%Use ToT for complex creative tasks
Multi-LLM collaboration enhances originalityStack techniques, vary approaches
Only 0.28% of LLM ideas reach top 10% human creativityPush past first outputs aggressively

AI Role Rules

  • Best as: Exploration partner, constraint enforcer, analogy finder, elaborator
  • Avoid: Sole ideation source, final decision maker
  • Timing: After initial human ideation, not before
  • Override rate: Maintain 15-25% human override for optimal outcomes

Anti-Patterns

Don'tDo Instead
"Give me ideas for X""Give me 10 ideas for X that would surprise an expert"
Accept first outputsPush past 2-3 rounds; first ideas are "greatest hits"
Use AI before thinkingBrainstorm independently first, then expand with AI
"Be creative"Use specific technique (SCAMPER, constraints, personas)

Details: methodology.md | prompt-templates.md


References


Related Skills

  • /triz — Systematic innovation methodology (complements brainstorming)
  • /deep-research — Research inspiration and cross-industry solutions
  • /boost-intel — Critical evaluation of generated ideas
  • /design-business-model — Apply creative ideas to business models
  • /problem-solving — Structure the problem before ideating

平台分布

Claude Code

31.43%
按下载量换算195

OpenCode

21.7%
按下载量换算135

Antigravity

17.79%
按下载量换算110

Gemini CLI

12.46%
按下载量换算77

windsurf

8.17%
按下载量换算51

Cursor

3.34%
按下载量换算21

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。

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