Founder Content

Complete content creation and multiplication system for solo founders and indie hackers. Use for any content task including 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", or any content creation request.

Founder Content System

Everything for creating and multiplying content as a solo founder or indie hacker.

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Master Content Creation Workflow (Must Follow)

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 is working:

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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 Founder Voice

Brand Voice Principles:

• 真实 (authentic) — real stories, not theory
• 硬 (sharp) — specific numbers, direct claims
• 带点自嘲 (self-deprecating) — own failures openly
• 不鸡汤 (no fluff) — substance over motivation

Adaptation Rules:

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

Step 4: Platform-Specific Polish

Platform	Key Adaptation
Twitter/X	Punchy, <280 chars, threads for depth
LinkedIn	Longer, professional vulnerability, spaced lines
小红书	口语化, 情绪词 (亏麻了/稳了), search-optimized titles

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Quick Reference

Task	Section
Write posts from scratch	Build-in-Public Workflow
Multiply existing content	Repurposing Framework
Thread formula	Thread Formula
Voice rules	Voice Rules
Platform defaults	Platform Defaults

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Build-in-Public Workflow

Step 1: Gather Context

From version control (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

小红书: 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 (see Voice Rules below)
• 小红书 passes sensitivity check
• 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: Make It Stick (SUCCESs)

• Simple — One core idea
• Unexpected — Break patterns
• Concrete — Specific details
• Credible — Proof points
• Emotional — Care about individual
• Stories — People remember stories

Step 5: Schedule Distribution

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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

Suggested pillar mix for technical founders:

1. Technical Build — AI, tools, architecture decisions, tech comparisons
2. Building in Public — Process over results, real learnings, metrics
3. Cross-Cultural Founder — Unique perspective from background/geography
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
• Keep 小红书 titles search-optimized

Never:

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

Voice Summary:

• 真实 (authentic, not performative)
• 硬 (direct, earned confidence)
• 带点自嘲 (self-deprecating humor)
• 细节感强 (specific details)
• 不鸡汤 (no inspirational soup)

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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
小红书	Chinese primary	2/week	干货 + 踩坑 mix

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Thread Formula

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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 (commit message):

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

Output:

Twitter/X:

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Just wired up Claude → database → auto-posting pipeline.
Push to repo now triggers content across 3 platforms.
The surprising part: 80% of the work was tone adaptation, not infra.

LinkedIn:

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Shipped: automated "build in public" pipeline

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

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

Next: adding human-in-the-loop approval.

小红书:

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标题：用 Claude + 数据库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 → 数据库存储 → 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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Input/Output Examples

Example 1: Build-in-Public Update

User Input:

"We just shipped a feature that lets users generate brand-consistent ad creative in one click."

Expected Output:

Twitter/X:

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Shipped: One-click brand-consistent ad creative.

Before: 3 rounds of "make it more on-brand" feedback.
After: 90% first-pass approval rate.

The hard part wasn't the AI. It was teaching it what "on-brand" actually means.

LinkedIn:

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We just shipped one-click brand-consistent ad creative.

Here's what changed:

Before:
• Designer creates ad
• Brand team reviews
• "Can you make it more on-brand?"
• 3 rounds of revision
• Finally approved

After:
• AI generates with brand memory
• 90% first-pass approval
• Designer focuses on strategy, not iteration

The insight: "On-brand" isn't subjective. It's computable.

We spent 6 months building the math behind brand consistency. Now it takes one click.

What's the most frustrating feedback loop in your creative process?

Example 2: Content Repurposing

User Input:

"Turn this blog post into social content: [paste 1000-word article]"

Response Pattern:

1. Extract atomic units: hooks, stats, frameworks, quotes, stories, takeaways
2. Apply STEPPS framework to select the most shareable elements
3. Generate platform-native content for each platform

Example Output Structure:

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## Extracted Atomic Units:
- Hook: [most contrarian/surprising claim]
- Stat: [most specific number]
- Framework: [step-by-step model]
- Quote: [most memorable phrase]
- Takeaway: [core lesson]

## Derivatives:

**Twitter Thread (7 tweets):**
1/ [Hook tweet]
2/ [Supporting data]
[continues...]

**LinkedIn Post:**
[Full expanded version]

**小红书:**
标题：[search-optimized Chinese title]
[Full Chinese adaptation]

## Distribution Schedule:
- Day 1: Twitter thread (9 AM local)
- Day 2: LinkedIn (8 AM local)
- Day 3: 小红书 (8 PM local)

Example 3: Cross-Platform Adaptation

User Input:

"This tweet performed well — adapt it for LinkedIn and 小红书."

Expected Output:

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## Original Tweet Analysis:
- Hook type: Contrarian ("Everyone thinks X, but Y")
- Key element: Specific number ($3,000 wasted)
- Engagement driver: Relatable failure story

## LinkedIn Version:
[Expanded with more context, spaced lines, professional framing, ends with question]

## 小红书 Version:
[Chinese adaptation with 口语化 tone, 情绪词, structured as 背景→经过→结果→教训]

## Adaptation Notes:
- LinkedIn: Added "Here's what I learned" framework
- 小红书: Localized dollar amounts to local currency context
- Both: Kept the core contrarian insight

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Author

Canlah AI — Run performance marketing without breaking your brand.

• GitHub: github.com/PHY041
• All Skills: clawhub.ai/PHY041