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Agent Skill · 研究检索

idea-to-post(发布想法)

idea-to-post 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

idea-to-post 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,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 20,646。

总安装 5,141周安装 210GitHub Stars 公开资料未说明下载 1,663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-0
版本
0.1.0

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:idea-to-post(发布想法)
来源仓库:https://github.com/akira82-ai/idea-to-post
安装命令:
openclaw skills install idea-to-post
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install idea-to-post

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。

扩展零散想法为深度文章草稿,提升内容表达完整度。适用于自媒体运营、博客写作或知识分享类项目的内容生产。输入核心观点后生成段落框架、论据支撑与引用建议。需用户提供原始素材以确保扩展内容忠实于原意。idea-to-post 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

建议人工润色以避免 AI 生成文本出现事实错误或语气偏差。

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 OpenClaw;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 openclaw skills install idea-to-post;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

    它需要调用外部服务,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

idea-to-post 怎么安装?

在终端执行 openclaw skills install idea-to-post。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

idea-to-post 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

idea-to-post 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

idea-to-post 是免费的吗?

来源标注的许可证是 MIT-0。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。

idea-to-post 用的人多吗?

公开数据是累计安装 5,141 次,近一周安装 210 次,下载 1,663 次。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。

idea-to-post 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,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 OpenClaw,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
idea-to-post

发布想法 · 本页

5,141—调用外部服务MIT-0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

name
idea-to-post
description
Expand scattered ideas into in-depth posts. Use this skill when users need to "expand ideas," "generate posts," "brainstorm content," "writing assistance," "enrich thoughts," "deepen thinking," or provide scattered ideas that need systematic expression.
version
1.5.0
allowed-tools
AskUserQuestion, WebSearch, mcp__web_reader__webReader
model
claude-sonnet-4-20250514

Idea-to-Post Expansion Skill

Skill Overview

This skill helps you expand scattered ideas (a sentence, a few words, a vague thought) into 90+ point social media posts.

Target Positioning: Quality social media content, not technical documentation

It works through the following ways:

  1. Framework Internalization - Use thinking frameworks to design questions, but don't mechanically apply them
  2. Information Search & Integration - Automatically search for high-quality materials, supplement relevant data and cases
  3. Progressive Deep Questioning - Multiple-choice to set direction + open questions to enrich content, 7-10 rounds of dialogue until complete
  4. Iterative Polishing & Optimization - Reflect and optimize after generation, pursuing 90+ point quality
  5. Multi-Platform Output - Generate post versions adapted to different platforms

Expected dialogue rounds: 7-10 rounds

  • 3-4 rounds: Get direction and core viewpoints (technical documentation level)
  • 5-7 rounds: Add cases, emotions, uniqueness (social media level)
  • 7-10 rounds: Deep mining, repeated polishing (quality social media level)

Core Mechanism: Progressive Questioning + Framework Internalization

Three Core Principles

1. Internalize Frameworks, Don't Expose Them

Use thinking framework logic to design questions, but don't say "I'm using [Framework Name]":

Wrong: "I recommend using the PREP framework. Now for Point: What's your viewpoint?"
Correct: "What's the core viewpoint you want to express?"

Questions have framework thinking, but the dialogue is natural.

2. Combine Multiple Choice + Open Questions

Multiple Choice (AskUserQuestion)  Quickly lock direction
Open Questions (direct dialogue)    Deeply mine content

Multiple Choice = Skeleton | Open Questions = Flesh and blood

3. Progressive Deepening, Dynamic Adjustment

Each round of questions is based on the previous answer, naturally transitioning to the next dimension:

User: "todo is an underrated command"
    ↓
Follow-up: What does "underrated" specifically mean? (Concept deepening)
    ↓
User: "People don't know it's a conversation memory mechanism"
    ↓
Follow-up: What pain point does it solve? (Value inquiry)
    ↓
Follow-up: Any specific examples? (Case supplement)

Questioning Flow (7-10 Rounds of Dialogue + Multi-Stage Search)

┌─────────────────────────────────────────────────┐
│  User inputs idea                                │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  [Initial Search] Background information        │
│  collection                                     │
│  - Identify core concepts                        │
│  - Multi-angle search queries                    │
│  - Get background materials                      │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Rounds 1-2: Direction locking                  │
│  (mainly multiple choice)                        │
│  - Goal? Audience? Platform?                     │
│  - Quickly position article type                 │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Rounds 3-4: Core deep dive                     │
│  (open questions)                                │
│  - What's the core viewpoint?                    │
│  - What does "underrated" specifically mean?     │
│  - What pain point does it solve?                │
│  - Why do you think so?                          │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Rounds 5-6: Real cases                         │
│  (open questions, required)                      │
│  - When was the most recent time?                │
│  - What feature? What specifically was said?     │
│  - How did you feel at that moment?              │
│  - Any comparison cases? (with vs without)       │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Round 7: Emotional resonance                   │
│  (open questions, required)                      │
│  - Most frustrated/surprised moment?             │
│  - Physical reaction? Slap thigh? Long sigh?     │
│  - Turning point from "useless" to "amazing"?    │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Round 8: Uniqueness                            │
│  (open questions)                                │
│  - Any undiscovered tips?                        │
│  - Any unique usage methods?                     │
│  - Any counter-intuitive understanding?          │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  [Precision Search] Based on user's unique      │
│  viewpoints                                      │
│  - Extract unique insights/counter-intuitive     │
│    viewpoints                                    │
│  - Reverse search for supporting evidence        │
│  - Multi-angle validation                        │
│    (industry/competitors/data)                   │
│  - Try different keywords if search fails        │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Round 9: Structure confirmation                │
│  (multiple choice)                               │
│  - Article structure?                            │
│  - Style preference?                             │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Round 10: Final touches                        │
│  (mixed)                                         │
│  - Core golden sentence?                         │
│  - Call to action?                               │
│  - Anything else to add?                         │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Information completeness assessment            │
│  (90+ point standard)                            │
│  Core viewpoint                                  │
│  Real cases (required)                           │
│  Emotional resonance (required)                  │
│  Unique viewpoints (required)                    │
│  External validation (search results)            │
│  → Complete, generate content                    │
│  → Incomplete, continue questioning              │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  First draft generation                         │
│  (standard Markdown format)                      │
│  - Integrate all information                     │
│  - Use heading levels, bold, quote blocks, etc.  │
│  - Generate structured content                   │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Reflection and optimization (optional)         │
│  - What's not good enough?                       │
│  - What needs supplementing?                     │
│  - Iterate and optimize                          │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Final output (standard Markdown format)        │
└─────────────────────────────────────────────────┘

Note: Cases, emotions, and uniqueness are required and cannot be skipped. The second round of precision search is a key环节.


Integrating Framework Thinking into Questions

Framework ThinkingQuestioning ApproachExample
PointWhat's the core viewpoint?"What's the core viewpoint you want to express?"
ReasonWhy do you think so?"Why do you think so? What's the reason?"
WhatWhat specifically?"What does this specifically refer to?"
WhyWhy is it important?"What pain point does it solve?"
HowHow is it done?"How is it implemented?"
ExampleAny examples?"Any specific cases?"
SituationInitial state?"What was the initial state?"
ComplicationWhat conflict?"What challenge appeared?"

Frameworks are thinking tools, not questioning templates.


Quick Framework Selection Guide

Automatic Recommendation Rules

Based on keywords in your input, the system will automatically recommend frameworks:

KeywordsRecommended Framework
Why, essence, original intention, mission, valueGolden Circle
Problem, challenge, dilemma, turning point, storySCQA
Promotion, publicity, conversion, sales, marketingAIDA
Viewpoint, opinion, think, should, suggestPREP
Deep dive, root cause, trace back, underlying5-Why
Innovation, breakthrough, disrupt, reconstruct, essenceFirst Principles
Product, feature, advantage, selling point, characteristicFBA
Other or unclear5W1H (default)

Framework Introduction

For detailed framework explanations, refer to references/thinking-frameworks.md


Usage Flow

Core Flow: Keep Questioning Until Complete

┌─────────────────────────────────────────────────┐
│  User inputs idea                                │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  First round: Direction questions                │
│  - Keyword analysis based on topic               │
│  - Recommend thinking framework                  │
│  - Confirm target platform and audience          │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  User answers                                    │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Second round: Framework deep dive               │
│  - Ask core elements based on selected framework │
│  - Focus on 1-2 key questions per round          │
│  - Dynamically adjust subsequent questions       │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Third round: Detail supplement                  │
│  - Ask missing details based on available info   │
│  - Cases, data, emotional points, etc.           │
│  - Interactive design and call to action         │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
            ...Loop...
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Skill judges: Information completeness          │
│  assessment                                      │
│  - Is core viewpoint clear?                      │
│  - Is supporting material sufficient?            │
│  - Are emotional resonance points clear?         │
│  - Is interactive design specific?               │
│                                                 │
│  → Incomplete: Continue questioning              │
│  → Complete: Enter generation phase              │
└────────────────┬────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────┐
│  Integrate information to generate post          │
│  - Original idea                                 │
│  - Search materials                              │
│  - User answers                                  │
│  - Framework structure                           │
└─────────────────────────────────────────────────┘

Information Search (Multi-Stage Execution, Required)

Search must be performed in multiple stages to ensure precise external validation is collected:

Stage 1: Initial Background Search (Before questioning begins)

Execute initial information search before questioning starts:

  1. Identify core concepts in the topic
  2. Build multi-angle search queries
  3. Use WebSearch to get relevant materials
  4. Use WebFetch to read key pages in depth
  5. Organize search results for later use
Stage 2: Precision Deep Search (After collecting core viewpoints)

This is the most critical step - after collecting the user's core viewpoints and unique insights, you must perform a second round of precision search:

  1. Build search terms based on user's unique viewpoints

- Extract unique insights/counter-intuitive viewpoints proposed by user - Use these viewpoints as keywords for reverse search - Look for supporting or refuting evidence

  1. Multi-angle validation search

- Search industry reports, news media - Search competitors/international cases - Search data support

  1. Handling search failures

- If one search fails/is limited, try different keyword combinations - Use more generic or more specific search terms - Try English search terms for international perspective - Record search status, inform user (e.g., if search was restricted)

Search Keyword Strategy
StageSearch FocusExample Keywords
Initial searchBackground info, basic facts"Qianwen 3 release", "AI e-commerce assistant"
Precision searchUser unique viewpoint validation"AI vs e-commerce conflict", "traffic distribution AI impact"
Comparison searchInternational cases, competitor analysis"ChatGPT e-commerce", "foreign AI shopping assistant"

Search results must be integrated into final content as external validation.

Even if search is limited, try multiple different keywords and inform user of search status.

Question Design Principles

1. Question Based on Topic

Questions must be closely tied to the core topic of user input, don't ask irrelevant questions.

2. Framework-Based Design

Use framework thinking to design questions, but don't say "I'm using [Framework Name]".

3. Mix Multiple Choice + Open Questions

  • Multiple Choice: When there are clear options, need quick classification
  • Open Questions: When need stories/experiences/emotions/details
  • Mixed: After AskUserQuestion "Other" option, continue follow-up questions

4. Progressive Deepening, Dynamic Adjustment

Each round is based on the previous answer, naturally transitioning to the next dimension. Not mechanically following a template.

5. Focus on 1-2 Questions Per Round

Avoid information overload, give user thinking space.

6. Complete Information Before Stopping

Check core dimensions, question what's missing, only generate when complete.

Completeness Judgment Standards (90+ Point Target)

When judging whether information is complete, the skill checks the following dimensions:

Must Be Complete (Continue questioning if missing, cannot skip)
DimensionCheck ItemDescription
Core ViewpointIs the core viewpoint to be expressed clear?The soul of the article
Target AudienceIs it clear who it's written for?Determines expression style
Publishing PlatformIs it clear where to publish?Determines content format
Real CasesAre there specific examples/experiences?Social media required
Emotional ResonanceAre there resonance points/emotional hooks?Social media required
Unique ViewpointsAre there insights others haven't mentioned?90+ point required
Should Be Complete (Try to question)
DimensionCheck ItemDescription
External ValidationIs there search material to support?Adds persuasiveness
Nice to Have (Better if present)
DimensionCheck ItemDescription
Interactive DesignIs there a clear call to action?Guide reader participation
Style PreferenceWhat style?Professional/humorous/story-based

90+ Point Content Standards

ScoreCharacteristicsWhat's Missing
60-70 pointsClear structure, clear viewpointsLacks real cases, emotional resonance
80-85 points+ Real cases, emotional resonanceLacks uniqueness, external validation
90+ points+ Unique viewpoints, external validationNothing missing, polished

Only enter generation phase when all "must complete" dimensions are present.

Real cases, emotional resonance, and unique viewpoints are the three pillars of social media content - all are essential.


Output Format

Markdown Format Specifications (Required)

All post content must be output in standard Markdown format, including:

Format ElementUse CaseExample
Heading LevelsMain title H1, sections H2-H4# Title ## Section
Bold EmphasisCore viewpoints, keywords**Core viewpoint**
Quote BlocksGolden sentences, key assertions> Quote content
ListsParallel points, step descriptions- Item 1
Horizontal RulesSeparate different parts---
Code BlocksTechnical content, data `code`

Pre-output checklist:

  • [ ] Clear heading levels (H1 main title, H2 sections, H3 subsections)
  • [ ] Bold emphasis on core viewpoints
  • [ ] Quote blocks for golden sentences/key assertions
  • [ ] Long content in bullet points
  • [ ] Horizontal rules between sections
  • [ ] Overall format follows standard Markdown syntax

Universal Structure

# [Main Title] Engaging title based on core viewpoint

## [Hook] Attention-grabbing opening

Body content...

---

## [Body Part 1] Expand based on framework structure

- Framework-guided hierarchical content
- Search data-supported viewpoints
- Specific cases and stories

> Core golden sentence in quote block

---

## [Body Part 2] Continue expansion

More content...

---

## [Conclusion] Call to action or summary reinforcement

Concluding content...

---

**[Tags]** #topic1 #topic2 #topic3

**[Reference Materials]** Data sources cited (if search was used)

Platform-Adapted Versions

PlatformWord CountCharacteristics
WeChat Official Account2000+In-depth long articles, clear sections, image suggestions
Xiaohongshu500-1000Practical content, emoji embellishment, list-style
Twitter/Weibo140-280Concise and powerful, one-sentence core, golden sentence style
LinkedIn/Maimai1000-1500Professional workplace, industry insights, case support

For detailed structure explanations, refer to references/post-structures.md


High-Quality Information Sources

The system prioritizes the following types of high-quality sources when searching:

Source TypeExamples
Academic ResourcesarXiv, Google Scholar, CNKI
Industry ReportsMcKinsey, Gartner, iResearch
Professional TechnicalOfficial docs, tech blogs, GitHub
News MediaCaixin, 36Kr, TechCrunch
Knowledge PlatformsWikipedia, Zhihu high-voted, Medium

For detailed data source lists, refer to references/data-sources.md


Best Practices

  1. Provide sufficient context - Even for scattered ideas, try to include key points you care about
  2. Answer questions honestly - Your answers during interactive questioning directly affect post quality
  3. Define target platform - Knowing which platform you're posting to can generate more suitable content
  4. Leverage search results - Materials searched by the system can greatly enrich your content
  5. Compare multiple versions - Compare outputs from different versions, choose the most suitable
  6. Iterate and optimize - Continue questioning and optimizing based on generated results

Examples

See examples/ directory for complete usage examples:

  • basic-usage.md - Basic usage examples
  • advanced-usage.md - Advanced scenario examples

Reference Documents

  • references/thinking-frameworks.md - Detailed framework explanations
  • references/questioning-strategy.md - Continuous progressive questioning strategy (core)
  • references/questioning-modes.md - Questioning mode selection guide (new)
  • references/question-templates.md - Question template library
  • references/post-structures.md - Post structure guide
  • references/data-sources.md - High-quality data source list

平台分布

OpenClaw

90.79%
按下载量换算1,510

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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