Atomizekit

atomizekit — keyless brand content engine

One topic → one canonical blog post → native drafts for every platform + on-brand image cards + a voiced short + a data reel → AI-citation tracking that tells you what to write next. Keyless (no AI/platform API keys — you are the engine via Claude Code). Draft-only: nothing publishes or posts; a human posts each piece.

FIRST-RUN RULE (do this before anything else)

Resolve config in this order: <cwd>/.claude/atomizekit.config.json (host project), then the skill's brand/brand.json (standalone fallback). If neither exists, or if cwd is a project (has package.json/.git) and it has no host config of its own, run commands/setup.md first — do not silently reuse a brand/brand.json left by a different project. Setup analyzes the host codebase, interviews the user, and writes <cwd>/.claude/atomizekit.config.json. Never guess brand facts. Scripts fail loud with this same instruction when config is missing.

Throughout, <skill> = this skill's directory; run scripts from the user's project root (cwd) so blog/distribution/data land in their repo.

The pipeline (in order)

1. Topic. If the user gave one, use it. If not: pull the measured gap queue from <cwd>/data/geo/next-topics.json if it exists (what you're NOT cited for yet), and/or research trends (the last30days skill if installed, else WebSearch); cluster into 4–6 candidates and let the user pick (AskUserQuestion).
2. Write the blog — commands/write.md → a GEO-structured draft at <blogDir>/<slug>.mdx. Draft only; never set status: published.
3. Atomize — python3 <skill>/scripts/atomize.py --slug <slug> scaffolds the configured platforms under <distributionDir>/<slug>/ + a post.json. Then write each variant natively per references/playbook.md (one insight per platform, canonical link in each, one woven feature except Reddit). Fill post.json stat/statLabel (one REAL number from the blog) and scenes[] (from the YouTube variant) for the video.
4. Visuals — commands/visuals.md. All output lands in <distributionDir>/<slug>/media/ in the user's project.
   • node <skill>/scripts/render_cards.mjs <slug> → 4 PNGs. Keyless, Node-only — always available.
   • Video needs the hyperframes toolchain: gate on npx hyperframes doctor --json (FFmpeg 7+, Chrome green) first, and the voiced renders below need pip install kokoro-onnx soundfile (local TTS). If those aren't present, ship the cards + text drafts and tell the user what to install for video — don't fake a render.
   • node <skill>/scripts/render_reel.mjs <slug> → voiced stat reel.
   • node <skill>/scripts/render_short.mjs <slug> → voiced kinetic-text short.
5. Report every draft + media path. Remind: draft-only, the user posts each by hand. Do NOT commit/publish unless asked.

Separate cadence — Measure (not part of each content job): on a schedule you choose, node <skill>/scripts/geo/measure.ts runs prompts × N through the (stubbed, keyless) citation providers and writes data/geo/next-topics.json, which step 1 of the NEXT job reads. Run it periodically, not at the end of every piece.

Commands & references

File	What
commands/setup.md	First-run: codebase analysis + brand interview → config
commands/write.md	GEO-structured blog draft
commands/atomize.md	Scaffold platform variants from the blog
commands/visuals.md	Render image cards + reel + voiced short
commands/dashboard.md	Local review dashboard: node scripts/dashboard.mjs — see all drafts + media, mark posted
references/playbook.md	Per-platform voice rules (2026-researched) + brand weave + AI-tell avoidance
references/geo-writing.md	How content earns AI-answer citations (structure, schema, data)

Brand config

All brand specifics live in ONE file (.claude/atomizekit.config.json in the host project, or brand/brand.json in the skill): name, domain, canonicalPattern, blogDir, distributionDir, audience, voice, author, colors, fonts, logo, eyebrow, platforms, and features (the advice→product map for the brand weave). brand/brand.template.json is the annotated contract. /setup writes it; every script reads it via scripts/lib_brand.mjs (host config wins, so one install serves many projects).

Prerequisites

• Node ≥ 22.15, Python 3 (always).
• FFmpeg 7+, Chrome (for any video render). Gate on npx hyperframes doctor --json.
• Optional: pip install kokoro-onnx soundfile for voiced renders (local Kokoro TTS — still no key). PEXELS_API_KEY (free) for stock VIDEO beds; without it, keyless Openverse CC0 images are the fallback.

Honest limits

• Video needs a doctor-green hyperframes toolchain; images (takumi) need only Node.
• Posting is manual by design — no platform credentials here, ever. This keeps the system inside every platform's 2026 ToS (penalties attach to mass/undisclosed automation, not to human-reviewed native content).
• GEO citation providers are stubbed keyless; wiring real Perplexity/Gemini/GSC keys is optional and left to the user (the stubs are deterministic for testing).
• Test the skill after install: npm install && npm test (from <skill>) should be green and render 4 demo cards.