idea-to-post
npx skills add https://github.com/akira82-ai/idea-to-post --skill idea-to-post
Agent 安装分布
Skill 文档
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:
- Framework Internalization – Use thinking frameworks to design questions, but don’t mechanically apply them
- Information Search & Integration – Automatically search for high-quality materials, supplement relevant data and cases
- Progressive Deep Questioning – Multiple-choice to set direction + open questions to enrich content, 7-10 rounds of dialogue until complete
- Iterative Polishing & Optimization – Reflect and optimize after generation, pursuing 90+ point quality
- 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"
â
Follow-up: What does "underrated" specifically mean? (Concept deepening)
â
User: "People don't know it's a conversation memory mechanism"
â
Follow-up: What pain point does it solve? (Value inquiry)
â
Follow-up: Any specific examples? (Case supplement)
Questioning Flow (7-10 Rounds of Dialogue + Multi-Stage Search)
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â User inputs idea â
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â [Initial Search] Background information â
â collection â
â - Identify core concepts â
â - Multi-angle search queries â
â - Get background materials â
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â Rounds 1-2: Direction locking â
â (mainly multiple choice) â
â - Goal? Audience? Platform? â
â - Quickly position article type â
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â Rounds 3-4: Core deep dive â
â (open questions) â
â - What's the core viewpoint? â
â - What does "underrated" specifically mean? â
â - What pain point does it solve? â
â - Why do you think so? â
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â Rounds 5-6: Real cases â
â (open questions, required) â
â - When was the most recent time? â
â - What feature? What specifically was said? â
â - How did you feel at that moment? â
â - Any comparison cases? (with vs without) â
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â Round 7: Emotional resonance â
â (open questions, required) â
â - Most frustrated/surprised moment? â
â - Physical reaction? Slap thigh? Long sigh? â
â - Turning point from "useless" to "amazing"? â
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â Round 8: Uniqueness â
â (open questions) â
â - Any undiscovered tips? â
â - Any unique usage methods? â
â - Any counter-intuitive understanding? â
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â [Precision Search] Based on user's unique â
â viewpoints â
â - Extract unique insights/counter-intuitive â
â viewpoints â
â - Reverse search for supporting evidence â
â - Multi-angle validation â
â (industry/competitors/data) â
â - Try different keywords if search fails â
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â Round 9: Structure confirmation â
â (multiple choice) â
â - Article structure? â
â - Style preference? â
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â Round 10: Final touches â
â (mixed) â
â - Core golden sentence? â
â - Call to action? â
â - Anything else to add? â
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âââââââââââââââââââââââââââââââââââââââââââââââââââ
â Information completeness assessment â
â (90+ point standard) â
â Core viewpoint â
â Real cases (required) â
â Emotional resonance (required) â
â Unique viewpoints (required) â
â External validation (search results) â
â â Complete, generate content â
â â Incomplete, continue questioning â
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â First draft generation â
â (standard Markdown format) â
â - Integrate all information â
â - Use heading levels, bold, quote blocks, etc. â
â - Generate structured content â
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â Reflection and optimization (optional) â
â - What's not good enough? â
â - What needs supplementing? â
â - Iterate and optimize â
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â Final output (standard Markdown format) â
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Note: Cases, emotions, and uniqueness are required and cannot be skipped. The second round of precision search is a keyç¯è.
Integrating Framework Thinking into Questions
| Framework Thinking | Questioning Approach | Example |
|---|---|---|
| Point | What’s the core viewpoint? | “What’s the core viewpoint you want to express?” |
| Reason | Why do you think so? | “Why do you think so? What’s the reason?” |
| What | What specifically? | “What does this specifically refer to?” |
| Why | Why is it important? | “What pain point does it solve?” |
| How | How is it done? | “How is it implemented?” |
| Example | Any examples? | “Any specific cases?” |
| Situation | Initial state? | “What was the initial state?” |
| Complication | What 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:
| Keywords | Recommended Framework |
|---|---|
| Why, essence, original intention, mission, value | Golden Circle |
| Problem, challenge, dilemma, turning point, story | SCQA |
| Promotion, publicity, conversion, sales, marketing | AIDA |
| Viewpoint, opinion, think, should, suggest | PREP |
| Deep dive, root cause, trace back, underlying | 5-Why |
| Innovation, breakthrough, disrupt, reconstruct, essence | First Principles |
| Product, feature, advantage, selling point, characteristic | FBA |
| Other or unclear | 5W1H (default) |
Framework Introduction
For detailed framework explanations, refer to references/thinking-frameworks.md
Usage Flow
Core Flow: Keep Questioning Until Complete
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â User inputs idea â
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â First round: Direction questions â
â - Keyword analysis based on topic â
â - Recommend thinking framework â
â - Confirm target platform and audience â
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â User answers â
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â Second round: Framework deep dive â
â - Ask core elements based on selected framework â
â - Focus on 1-2 key questions per round â
â - Dynamically adjust subsequent questions â
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â Third round: Detail supplement â
â - Ask missing details based on available info â
â - Cases, data, emotional points, etc. â
â - Interactive design and call to action â
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...Loop...
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â Skill judges: Information completeness â
â assessment â
â - Is core viewpoint clear? â
â - Is supporting material sufficient? â
â - Are emotional resonance points clear? â
â - Is interactive design specific? â
â â
â â Incomplete: Continue questioning â
â â Complete: Enter generation phase â
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âââââââââââââââââââââââââââââââââââââââââââââââââââ
â Integrate information to generate post â
â - Original idea â
â - Search materials â
â - User answers â
â - Framework structure â
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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:
- Identify core concepts in the topic
- Build multi-angle search queries
- Use WebSearch to get relevant materials
- Use WebFetch to read key pages in depth
- 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:
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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
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Multi-angle validation search
- Search industry reports, news media
- Search competitors/international cases
- Search data support
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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
| Stage | Search Focus | Example Keywords |
|---|---|---|
| Initial search | Background info, basic facts | “Qianwen 3 release”, “AI e-commerce assistant” |
| Precision search | User unique viewpoint validation | “AI vs e-commerce conflict”, “traffic distribution AI impact” |
| Comparison search | International 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)
| Dimension | Check Item | Description |
|---|---|---|
| Core Viewpoint | Is the core viewpoint to be expressed clear? | The soul of the article |
| Target Audience | Is it clear who it’s written for? | Determines expression style |
| Publishing Platform | Is it clear where to publish? | Determines content format |
| Real Cases | Are there specific examples/experiences? | Social media required |
| Emotional Resonance | Are there resonance points/emotional hooks? | Social media required |
| Unique Viewpoints | Are there insights others haven’t mentioned? | 90+ point required |
Should Be Complete (Try to question)
| Dimension | Check Item | Description |
|---|---|---|
| External Validation | Is there search material to support? | Adds persuasiveness |
Nice to Have (Better if present)
| Dimension | Check Item | Description |
|---|---|---|
| Interactive Design | Is there a clear call to action? | Guide reader participation |
| Style Preference | What style? | Professional/humorous/story-based |
90+ Point Content Standards
| Score | Characteristics | What’s Missing |
|---|---|---|
| 60-70 points | Clear structure, clear viewpoints | Lacks real cases, emotional resonance |
| 80-85 points | + Real cases, emotional resonance | Lacks uniqueness, external validation |
| 90+ points | + Unique viewpoints, external validation | Nothing 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 Element | Use Case | Example |
|---|---|---|
| Heading Levels | Main title H1, sections H2-H4 | # Title ## Section |
| Bold Emphasis | Core viewpoints, keywords | **Core viewpoint** |
| Quote Blocks | Golden sentences, key assertions | > Quote content |
| Lists | Parallel points, step descriptions | - Item 1 |
| Horizontal Rules | Separate different parts | --- |
| Code Blocks | Technical 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
| Platform | Word Count | Characteristics |
|---|---|---|
| WeChat Official Account | 2000+ | In-depth long articles, clear sections, image suggestions |
| Xiaohongshu | 500-1000 | Practical content, emoji embellishment, list-style |
| Twitter/Weibo | 140-280 | Concise and powerful, one-sentence core, golden sentence style |
| LinkedIn/Maimai | 1000-1500 | Professional 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 Type | Examples |
|---|---|
| Academic Resources | arXiv, Google Scholar, CNKI |
| Industry Reports | McKinsey, Gartner, iResearch |
| Professional Technical | Official docs, tech blogs, GitHub |
| News Media | Caixin, 36Kr, TechCrunch |
| Knowledge Platforms | Wikipedia, Zhihu high-voted, Medium |
For detailed data source lists, refer to references/data-sources.md
Best Practices
- Provide sufficient context – Even for scattered ideas, try to include key points you care about
- Answer questions honestly – Your answers during interactive questioning directly affect post quality
- Define target platform – Knowing which platform you’re posting to can generate more suitable content
- Leverage search results – Materials searched by the system can greatly enrich your content
- Compare multiple versions – Compare outputs from different versions, choose the most suitable
- Iterate and optimize – Continue questioning and optimizing based on generated results
Examples
See examples/ directory for complete usage examples:
basic-usage.md– Basic usage examplesadvanced-usage.md– Advanced scenario examples
Reference Documents
references/thinking-frameworks.md– Detailed framework explanationsreferences/questioning-strategy.md– Continuous progressive questioning strategy (core)references/questioning-modes.md– Questioning mode selection guide (new)references/question-templates.md– Question template libraryreferences/post-structures.md– Post structure guidereferences/data-sources.md– High-quality data source list