--- name: content-blitz description: 4.7 Native One-Shot: From delivery case/theme to 30-day content calendar + multi-platform drafts + digital human scripts in a single 1M context session. Low-conscientiousness friendly. Used to feed the acquisition layer after delivery. version: "1.0" lastUpdated: "2026-04-24" inspiredBy: - source: "Claude 4.7 · 1M context feature" note: "Leverage 1M context to complete atomic workflows in one session, replacing the multi-step chain from the 4.5 era" - source: "Operating Framework content-engine pattern (the methodology source)" note: "Multi-platform content derived from a single case, but rebuilt as a 4.7 single-session version" designPrinciples: - "Atomic workflow first: produce 30 days in one go, avoid state sync costs across sessions" - "Platform-specific: Xiaohongshu / WeChat blog / X / digital human scripts each have their own section, no template reuse" - "Low-conscientiousness friendly: batch output without relying on weekly maintenance" usedBy: - marketing dependencies: - path: "docs/system-memory.md" why: "Brand voice + pricing tiers + founder background" - path: "customers//case.md" why: "Content source (optional, use theme instead when no customer)" --- # Content Blitz · Atomic Content Workflow ## Why This Skill Exists The Operating Framework's `content-engine` was designed for the 4.6 era—requiring multi-turn conversations (write prompt → generate → review → refine). With **Claude 4.7 + 1M context**, the entire flow can **complete in a single session**. This is especially friendly for low-conscientiousness founders: no weekly maintenance rhythm needed, batch generate 30 days of inventory in one shot. ## When to Use - A project just delivered, need to feed back into the acquisition layer - Need to batch produce a week/month of content - Have an inspiration burst and want to "dump everything in your head into content at once" **Do not use**: without a brand voice file (build the voice section in `docs/system-memory.md` first). ## Input Checklist (must be complete) 1. **Content source** (at least one): - A delivered project → read `products//` directory - A theme / insight / story → founder pastes it in - A long-form article → read file path 2. **Brand voice**: voice snippet from `docs/system-memory.md` 3. **Target platforms** (all selected by default, can trim): - Xiaohongshu (digital human short video script + copy) - WeChat moments - WeChat blog (optional) - Video account (optional) 4. **Target cadence**: - 15 days / 30 days / 60 days ## Output (all produced in one session) ### A. Master file: `products//content-pack.md` Contains all versions + publishing calendar. ### B. Publishing calendar table | Date | Platform | Title | Content Type | Draft Link | |---|---|---|---|---| | Day 1 | Xiaohongshu | ... | Digital human 30s | #section 1 | | Day 2 | Moments | ... | Text | #section 2 | | ... | ... | ... | ... | ... | ### C. Individual drafts (each = one section) For each date and piece: - **Title** (platform-native) - **Body** (brand voice compliant) - **Digital human script** (if video): includes shot cues + lines + image suggestions - **Hashtags** (Xiaohongshu: 5-8 required) - **CTA**: direct to WeChat / WeChat blog ## Content Structure Principles ### 70-20-10 Mix For 30 days = 30 pieces: - **70% technical value** (21 pieces): niche SaaS tools, AI applications, specific operations - **20% case stories** (6 pieces): anonymized delivery stories, measurable results - **10% personal** (3 pieces): city life, learning notes, tool comparisons ### Viral Templates (for Xiaohongshu) Generate using these structures by default, cycle through them: - **Counterintuitive hook + numerical evidence**: e.g., "The dashboard I spent 3 hours on—the seller said after 1 week they no longer needed an ad team" - **VS comparison**: traditional method vs AI method, side-by-side - **Before/After**: specific before/after changes + data - **Tutorial**: step-by-step with demo GIF / screenshots - **Pitfall guide**: N traps I fell into ### Digital Human Script Format ``` [Shot 1 · 0-5 sec] Person center frame + hook subtitle Lines: "Today I'll tell you..." Image suggestion: [Shot 2 · 5-15 sec] Problem scenario Lines: "If you are..." Image suggestion: [Shot 3 · 15-25 sec] Solution demonstration Lines: "Here's what I did..." Image suggestion: [Shot 4 · 25-30 sec] CTA Lines: "DM me on my homepage to chat" Image suggestion: ``` ## Quality Checklist (run after generation) - [ ] All content passes brand voice check (no "empower the ecosystem" fluff) - [ ] At least 1 **specific number** (time, amount, percentage) every 5 pieces - [ ] Every CTA points to the same destination (WeChat / DM) - [ ] No real customer names or unauthorized data leaked - [ ] Xiaohongshu hashtags are all relevant (no unrelated trending tags stacked) - [ ] Digital human script lines read naturally (can be delivered within 30 seconds) ## Execution Flow ``` Input: project path OR theme + days + platform list ↓ Read: voice section from docs/system-memory.md Read: input source (project products directory / user pasted text) ↓ One-shot generate: publishing calendar + all drafts (all 30 pieces) ↓ Self-check: run quality checklist ↓ Write: products//content-pack.md ↓ Output: founder receives full content pack + one recommendation (which to post first, why) ``` ## Limitations / When Not to Use - Not suitable for **real-time data** content (trend tracking, holiday tie-ins)—that needs a separate run - Not suitable for **serious knowledge products** (course scripts)—precision requirements too high - Not suitable for companies with **existing content teams**—multi-person review workflow conflicts with this ## Cost Note One run of 30 pieces: Opus approximately ¥20-40 (includes 1M context loading and output). Batch production vs piecemeal 3 pieces at a time: **cost is similar, but your mental load is 10x lower**. This is the best value investment in the 4.7 era. ## Prohibitions - Do not run without a brand voice file (refuse execution directly, require building it first) - Do not automate publishing (generate drafts only, you handle publishing) - Do not mix content across projects (case A's story is not reused for project B)