Complete content creation and multiplication system for solo founders and indie hackers. Use for any content task including writing social posts, repurposing...
Everything for creating and multiplying content as a solo founder or indie hacker.
Core Principle: Research → Extract → Adapt → Write
Every piece of content must go through this workflow:
Before writing ANY content, research what is 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] viralsite:[platform].com [topic] lessons learned[topic] founder thread high engagement| 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 |
Brand Voice Principles:
Adaptation Rules:
| Platform | Key Adaptation |
|---|---|
| Twitter/X | Punchy, <280 chars, threads for depth |
| Longer, professional vulnerability, spaced lines | |
| 小红书 | 口语化, 情绪词 (亏麻了/稳了), search-optimized titles |
| 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 |
From version control (auto mode):
From user input (manual mode):
Every post answers 5 questions:
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
Core Principle: One Excellent Piece → 7-10 Platform-Native Derivatives
High-Value (prioritize): Evergreen topics, top performers, content with data/frameworks, long-form (>1000 words)
Skip: Trend-based, low performers, thin content
| 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 |
Every derivative needs at least one:
Day 0: Original published
Day 1-2: Tease/announcement
Day 3-7: First wave derivatives
Week 2-3: Second wave
Week 4+: Evergreen rotation
Suggested pillar mix for technical founders:
Weekly mix: 2-3 posts from pillars 1-2, 1 post from pillars 3-4
Always:
Never:
Voice Summary:
| Platform | Language | Cadence | Format |
|---|---|---|---|
| Twitter/X | English | 3-5/week | <280 chars, threads rare |
| English | 1-2/week | 8-20 lines, spaced | |
| 小红书 | Chinese primary | 2/week | 干货 + 踩坑 mix |
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.
Input (commit message):
feat: connect database MCP to content scheduler
- Added automatic content storage
- Triggers on push
- Posts to 3 platforms
Output:
Twitter/X:
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:
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:
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"
User Input:
"We just shipped a feature that lets users generate brand-consistent ad creative in one click."
Expected Output:
Twitter/X:
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:
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?
User Input:
"Turn this blog post into social content: [paste 1000-word article]"
Response Pattern:
Example Output Structure:
## 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)
User Input:
"This tweet performed well — adapt it for LinkedIn and 小红书."
Expected Output:
## 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
ZIP package — ready to use