Skill: phy041/phy-founder-content (version 1.0.0)

--- name: founder-content description: 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. metadata: {"category": "content-creation", "platforms": ["twitter-x", "linkedin", "xiaohongshu"], "features": ["build-in-public", "repurposing", "multi-platform", "voice-guide"]} --- # Founder Content System Everything for creating and multiplying content as a solo founder or indie hacker. --- ## 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: ``` 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 | --- ## Quick Reference | Task | Section | |------|---------| | Write posts from scratch | [Build-in-Public Workflow](#build-in-public-workflow) | | Multiply existing content | [Repurposing Framework](#repurposing-framework) | | Thread formula | [Thread Formula](#thread-formula) | | Voice rules | [Voice Rules](#voice-rules) | | Platform defaults | [Platform Defaults](#platform-defaults) | --- ## 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 --- ## 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 ``` Day 0: Original published Day 1-2: Tease/announcement Day 3-7: First wave derivatives Week 2-3: Second wave Week 4+: Evergreen rotation ``` --- ## 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 --- ## 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) --- ## 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 | --- ## 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. --- ## Example Transformation **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" ``` --- ## 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:** ``` 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? ``` ### 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:** ``` ## 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:** ``` ## 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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