# Content Creation > Full-stack content creation pipeline: idea or reference to published blog post, audio narration, video, and social media distribution. Orchestrates research, reference extraction, storytelling, AI visual assets (Nano Banana, Veo 3.1), TTS audio (Voicebox, kokoro-tts, Edge TTS), Remotion video, and social copy into a complete content package. Use when: (1) creating a blog post, case study, or writing entry, (2) turning an idea or experience into structured narrative, (3) using a reference post/video (X, LinkedIn, YouTube) as inspiration, (4) generating visual content (AI images, clips, screenshots, GIFs), (5) generating audio narration for posts, (6) creating social media content (X threads, Instagram carousels) from long-form writing, (7) packaging and distributing content across platforms. Triggers on 'new post', 'blog post', 'case study', 'content creation', 'write about', 'content pipeline', 'social content from post', 'use this as reference', 'generate audio', 'tts', 'narration'. - Skill: `broomva/content-creation` (Agent Skill, multi-file: 77 files) - Install (CLI): `npx skillmds@latest add broomva/content-creation` - Raw SKILL.md: https://api.skillmd.com/api/skills/broomva/content-creation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: broomva (https://skillmd.com/u/broomva) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/broomva/content-creation --- # Content Creation Pipeline Eight-phase workflow: reference/idea → published multimedia content package with social distribution. ``` REFERENCE (optional) → RESEARCH → NARRATIVE → VISUAL ASSETS → AUDIO → VIDEO → SOCIAL → DEPLOY ``` ## Setup & Onboarding Before using the pipeline, the agent MUST check which tools are available and guide the user through setup for any missing ones. Run the checklist below at the start of every content creation session. ### Prerequisite Check (run this first) ```bash # Check what's already available echo "=== Required ===" which yt-dlp && echo "✓ yt-dlp" || echo "✗ yt-dlp — needed for video download" which ffmpeg && echo "✓ ffmpeg" || echo "✗ ffmpeg — needed for video processing" echo "" echo "=== API Keys ===" [ -n "$GEMINI_API_KEY" ] && echo "✓ GEMINI_API_KEY set" || echo "✗ GEMINI_API_KEY — needed for Nano Banana, Veo 3.1, Gemini analysis" [ -n "$FAL_KEY" ] && echo "✓ FAL_KEY set" || echo "✗ FAL_KEY — optional, for fal.ai multi-provider" [ -n "$ELEVENLABS_API_KEY" ] && echo "✓ ELEVENLABS_API_KEY set" || echo "✗ ELEVENLABS_API_KEY — optional, for voiceover" echo "" echo "=== TTS (Audio Narration) ===" which kokoro-tts && echo "✓ kokoro-tts" || echo "✗ kokoro-tts — pip install kokoro-tts" curl -sf http://localhost:17493/health > /dev/null 2>&1 && echo "✓ Voicebox server running" || echo "✗ Voicebox — optional, for premium TTS (voicebox.sh)" which edge-tts && echo "✓ edge-tts" || echo "✗ edge-tts — pip install edge-tts (fallback TTS)" echo "" echo "=== Optional ===" which agent-browser && echo "✓ agent-browser" || echo "✗ agent-browser — optional, for screenshots" which nano-banana && echo "✓ nano-banana CLI" || echo "✗ nano-banana CLI — optional, Gemini SDK works without it" which xurl && echo "✓ xurl" || echo "✗ xurl — optional, for X posting" ``` ### Tier 1: Core (required for any content creation) | Tool | Install | Purpose | |------|---------|---------| | **yt-dlp** | `brew install yt-dlp` | Download video from X, YouTube, any platform | | **ffmpeg** | `brew install ffmpeg` | Video processing, format conversion, GIF creation | | **Remotion** | `bun add remotion @remotion/cli` (per project) | Programmatic video composition | ### Tier 2: AI Generation (required for AI-powered assets) | Tool | Setup | Purpose | |------|-------|---------| | **GEMINI_API_KEY** | Get free key at [aistudio.google.com](https://aistudio.google.com) → API keys | Nano Banana images, Veo 3.1 video, Gemini analysis, embeddings | | **@google/genai** | `bun add @google/genai` (per project) | SDK for all Gemini models | **How to get GEMINI_API_KEY:** 1. Go to [aistudio.google.com](https://aistudio.google.com) 2. Click "Get API key" → "Create API key" 3. Set it: `export GEMINI_API_KEY="your-key"` (add to `.zshrc` for persistence) 4. Free tier: ~500 image gen/day, video gen included, no credit card needed ### Tier 3: Enhanced (optional, for specific features) | Tool | Setup | Purpose | When needed | |------|-------|---------|-------------| | **FAL_KEY** | Sign up at [fal.ai](https://fal.ai), get key from dashboard | Multi-provider: Veo, Sora, Kling via one API | When you want to swap between video models | | **ELEVENLABS_API_KEY** | Sign up at [elevenlabs.io](https://elevenlabs.io) | AI voiceover generation | When video needs narration | | **kokoro-tts** | `pip install kokoro-tts` | CLI TTS generation (82M model) | Default audio narration for posts | | **Voicebox** | [voicebox.sh](https://voicebox.sh/) or `docker compose up` | Premium local TTS with voice cloning | When best quality audio needed | | **edge-tts** | `pip install edge-tts` | Microsoft Neural voices (free, unofficial) | Fallback when no GPU available | | **agent-browser** | `npm install -g @anthropic-ai/agent-browser` | Screenshots, web interaction | When capturing live app screenshots | | **nano-banana CLI** | `npm install -g @the-focus-ai/nano-banana` | Quick CLI image generation | Convenience; SDK works without it | | **xurl** | `brew install --cask xdevplatform/tap/xurl` | Post directly to X | When publishing X threads | | **TweetSave MCP** | `claude mcp add -s user tweetsave -- npx -y mcp-remote https://mcp.tweetsave.org/sse` | Read X posts from Claude Code | For reference extraction without yt-dlp | | **mcp-veo3** | `uvx mcp-veo3 --output-dir ~/Videos/Generated` | Veo 3.1 via MCP | When generating video from Claude Code | | **@aeven/nanobanana-mcp** | Add to Claude MCP config | Nano Banana via MCP | When generating images from Claude Code | ### Agent Behavior When the skill is triggered, the agent should: 1. **Run the prerequisite check** silently (don't dump output unless something is missing) 2. **If critical tools are missing** (yt-dlp, ffmpeg, GEMINI_API_KEY), tell the user what's needed and offer to install/configure: - For CLI tools: offer the `brew install` command - For API keys: explain where to get them and how to set them - For npm packages: offer to install in the current project 3. **If optional tools are missing**, proceed without them and mention alternatives: - No `agent-browser`? Use FxTwitter API + yt-dlp for extraction - No `FAL_KEY`? Use `@google/genai` directly - No `xurl`? Generate the post copy for manual publishing 4. **Never fail silently** — if an API call fails due to missing key, explain which key is needed and how to get it 5. **Adapt the pipeline** to available tools — use the best available method, not the ideal one ## Phase 0: Reference Extraction (when user provides a link) When the user provides a URL to a post, video, or thread as creative reference, extract and analyze it before anything else. See [references/x-content-extraction.md](references/x-content-extraction.md) and [references/reference-based-content-creation.md](references/reference-based-content-creation.md) for full details. ### Step 1: Extract content from the link **X/Twitter posts** (fastest — no auth required): ```bash # Extract tweet text, images, video URLs, engagement metrics TWEET_ID="2034332847893574080" # from the URL path curl -s "https://api.fxtwitter.com/status/$TWEET_ID" | jq . # Download video directly yt-dlp "https://x.com/user/status/$TWEET_ID" -o reference_video.mp4 # Or via FxTwitter direct download curl -sL "https://d.fxtwitter.com/user/status/$TWEET_ID" -o reference_video.mp4 ``` **YouTube / other platforms:** ```bash yt-dlp "URL" -o reference_video.mp4 ``` **Any URL with agent-browser** (screenshot + text extraction): ```bash agent-browser open "URL" && agent-browser wait --load networkidle agent-browser screenshot reference_screenshot.png --full agent-browser get text body > reference_text.txt ``` ### Step 2: Analyze with Gemini (video understanding + style extraction) Upload the downloaded video to Gemini for deep analysis: ```typescript import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY }); const file = await ai.files.upload({ file: "reference_video.mp4", config: { mimeType: "video/mp4" } }); const analysis = await ai.models.generateContent({ model: "gemini-2.5-flash", contents: [ { fileData: { fileUri: file.uri, mimeType: file.mimeType } }, { text: `Analyze this video as a content creation reference. Extract: 1. Visual Style: color palette (hex values), lighting, camera angles, framing 2. Pacing: shot durations, rhythm, fast/slow sections with timestamps 3. Transitions: types used (cuts, fades, zooms) and when 4. Text Overlays: fonts, positioning, animation, timing 5. Structure: hook (first 3s), body, CTA placement 6. Audio: music style, SFX, voiceover style 7. Engagement Hooks: techniques for retention Return as structured JSON.` }, ], }); ``` For **text/image posts**, use Gemini or Claude directly on the extracted text + screenshot to analyze hook type, structure, messaging, and CTA pattern. ### Step 3: Generate a style brief The analysis produces a **style brief** that guides all downstream phases: - **Narrative phase**: match hook type, structure, CTA pattern - **Visual phase**: match color palette, typography, layout - **Video phase**: match pacing, transitions, aspect ratio, duration - **Social phase**: match platform conventions and engagement patterns ### Multimodal Embedding (for similarity search) Use Gemini Embedding 2 to find similar content in your library: ```typescript // Embed a reference video into the same space as your content const embedding = await ai.models.embedContent({ model: "gemini-embedding-2-preview", contents: [{ inlineData: { mimeType: "video/mp4", data: videoBase64 } }], config: { outputDimensionality: 768 }, }); // Compare with cosine similarity against your content embeddings ``` Supports text, images, video (up to 120s), audio (up to 80s), and PDFs in a single vector space. --- ## Phase 1: Research Gather evidence, capture production state, pull metrics. Never write without data. **Compounding skills:** `/deep-research`, `/agent-browser`, `/competitor-intel`, curl/API data pulls. **Checklist:** - [ ] Core claim identified (what are you proving?) - [ ] 3-5 validating data points gathered - [ ] Production screenshots captured (full-page + detail) - [ ] Metrics pulled from APIs (never fabricate numbers) - [ ] Target audience and their concerns identified - [ ] Reference post analyzed (if provided) — style brief generated ## Phase 2: Narrative Structure using a proven framework. See [references/storytelling.md](references/storytelling.md) for full guides. | Content Type | Framework | Structure | |-------------|-----------|-----------| | Case study | PSI | Challenge → Solution → Quantified results | | Industry highlight | ABT | Context AND, BUT challenge, THEREFORE outcome | | Technical deep dive | 1-3-1 | One idea, three evidence points, one takeaway | | Product launch | Pixar Spine | Once upon a time... Every day... Until one day... | | Data story | Data Arc | Context → Tension → Resolution | ### Blog Post Structure ``` frontmatter (title, summary, date, published, tags) Hook (1-2 sentences — open loop or surprising claim) Hero media (video or key image) Numbers section (table with headline metrics) Problem section + image Solution section + image + progressive detail image Evidence section + dashboard screenshots + data visualizations Context section + variant screenshots Practice section + animated GIF Generalization section Closing (memorable one-liner) ``` **Rules:** Lead with numbers. One image per ~300 words. Bold key terms on first use. 3-4 sentence paragraphs max. Use `