founder-content

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name: founder-content description: Complete content creation and multiplication system for founders and indie hackers. Use for writing social posts, repurposing content, creating threads, build-in-public updates, or content planning. Triggers on "write a post", "create content", "repurpose this", "thread", "build in public", "ship update", "content calendar", "turn this into posts". inputs:

• name: idea type: string required: true description: The core idea, topic, or update to turn into platform-native content
• name: target_platforms type: list required: true description: Platforms to generate content for (e.g. ["twitter", "linkedin", "reddit"])
• name: source_data type: json required: false description: Optional structured data (repo README, stats, metrics) to enrich the content outputs:
• name: drafts type: json description: Dict of platform-native content drafts keyed by platform name (e.g. {"twitter": "...", "linkedin": "..."})

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Founder Content System

Everything for creating and multiplying content as a solo founder.

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Master Content Creation Workflow

Core Principle: Research → Extract → Adapt → Write

Every piece of content must go through this workflow.

Step 1: Research Hot Content (REQUIRED)

Before writing ANY content, research what's working:

1. Search for viral/high-engagement posts on target platform
2. Find 3-5 top-performing posts on similar topic
3. Note: hook structure, format, engagement type, tone
4. Identify what makes them work (specifics, emotion, contrarian angle)

Search patterns:

• [platform] [topic] viral
• site:[platform].com [topic] lessons learned
• [topic] founder thread high engagement

Step 2: Extract Winning Patterns

What to Extract	Why
Hook formula	First line determines if people read
Number usage	Specifics add credibility ($400→$180)
Emotion triggers	What makes people react (cringe, saved, wasted)
Story arc	How tension and payoff are structured
CTA design	What drives comments vs likes

Step 3: Adapt with Your Voice

Core Voice Principles:

• Authentic — real stories, not theory
• Sharp — specific numbers, direct claims
• Self-deprecating — own failures openly
• No fluff — substance over motivation

Adaptation Rules:

1. Keep the winning hook structure
2. Replace content with YOUR real stories
3. Add specific numbers ($3,000 wasted, saved $1,000+)
4. Include genuine emotion (still cringe, learned the hard way)
5. Avoid: vague claims, motivational fluff, humblebragging

Step 4: Platform-Specific Polish

Platform	Key Adaptation
Twitter/X	Punchy, <280 chars, threads for depth
LinkedIn	Longer, professional vulnerability, spaced lines
Xiaohongshu	Conversational Chinese, emotional words, 2K images

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Build-in-Public Workflow

Step 1: Gather Context

From GitHub (auto mode):

• Recent commits since last post
• PR titles and descriptions
• Release notes if tagged

From user input (manual mode):

• What shipped (feature/fix/improvement)
• Who it helps
• Why now
• One metric (optional)
• One lesson learned

Step 2: Extract the Story

Every post answers 5 questions:

1. What changed? (the ship)
2. Who benefits? (the user)
3. Why it matters now? (the context)
4. One proof (metric, example, before/after)
5. One takeaway (lesson or insight)

Step 3: Render for Each Platform

Twitter/X: Under 280 chars, concise, slightly spicy, one insight + one proof

LinkedIn: 8-20 lines with spacing, narrative + framework + takeaway

Xiaohongshu: Chinese-first, structure: 背景→步骤→结果→踩坑→总结

Step 4: Quality Check

• No identical cross-posts
• Each post has a takeaway
• No banned patterns
• Metrics/proof included where possible

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Repurposing Framework

Core Principle: One Excellent Piece → 7-10 Platform-Native Derivatives

Step 1: Evaluate Source

High-Value (prioritize): Evergreen topics, top performers, content with data/frameworks, long-form (>1000 words)

Skip: Trend-based, low performers, thin content

Step 2: Extract Atomic Units

Element	What to Extract
Hook	Opening line, attention-grabber
Stats	Numbers, percentages, metrics
Frameworks	Step processes, models
Quotes	Memorable phrases
Stories	Anecdotes, case studies
Takeaways	Key lessons, actionable tips

Step 3: Apply STEPPS (from Contagious)

Every derivative needs at least one:

1. Social Currency — Makes sharer look smart
2. Triggers — Connected to daily habits
3. Emotion — Evokes awe, surprise, anger
4. Public — Visible behavior
5. Practical Value — Useful, saves time/money
6. Stories — Narrative that carries message

Step 4: Distribution Schedule

Day 0: Original published
Day 1-2: Tease/announcement
Day 3-7: First wave derivatives
Week 2-3: Second wave
Week 4+: Evergreen rotation

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Content Pillars

Good pillars for a founder/builder:

1. Your Tech/Product — What you're building, how it works
2. Building in Public — Process > results, real learnings, metrics
3. Founder Perspective — Unique angle (background, market, journey)
4. Systems Thinking — Workflows, optimization, productivity

Weekly mix: 2-3 posts from pillars 1-2, 1 post from pillars 3-4

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Voice Rules

Always:

• Include one takeaway per post
• Adapt content per platform
• Use metrics when available

Never:

• Motivational fluff ("believe in yourself")
• Humblebragging / name-dropping
• Vague claims ("game-changing", "revolutionary")
• Thought-leader cringe
• Dunking on competitors by name

Voice Summary:

• Authentic (not performative)
• Direct, earned confidence
• Self-deprecating humor
• Specific details
• No inspirational soup

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Thread Formula

Tweet 1 (Hook): Surprising stat or contrarian take
Tweet 2-5: One key point per tweet with proof
Tweet 6: Common mistake / "what most get wrong"
Tweet 7: The solution/framework
Tweet 8: CTA + callback to original

Write 10-15 versions of hook before publishing.

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Example Transformation

Input (GitHub commit):

feat: connect MCP to content scheduler
- Added automatic content storage
- Triggers on GitHub push
- Posts to 3 platforms

Output:

Twitter/X:

Just wired up Claude → DB → auto-posting pipeline. GitHub push now triggers content across 3 platforms. Surprising part: 80% of the work was tone adaptation, not infra.

LinkedIn:

Shipped: automated "build in public" pipeline

What it does: GitHub commits → AI-adapted posts → 3 platforms

What I learned: The hard part isn't automation—it's maintaining authentic voice at scale.

Xiaohongshu:

标题：用 Claude + MCP 搭了个自动发帖系统

背景：每次提交都想分享，但手动发三个平台太累

做法：Claude 读 commit → 生成三版本 → 自动发

踩坑：以为难点是技术，其实是语气适配

总结：自动化不是复制粘贴，是让机器学会"变脸"

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Platform Defaults

Platform	Language	Cadence	Format
Twitter/X	English	3-5/week	<280 chars, threads rare
LinkedIn	English	1-2/week	8-20 lines, spaced
Xiaohongshu	Chinese + EN tools	2/week	干货 + 踩坑 mix