atomizekit

name	atomizekit
description	Use when the user wants to create and distribute brand content — a blog post plus native posts for X, LinkedIn, Reddit, Instagram, YouTube, Medium, and Substack, with on-brand image cards and voiced short-form video — or wants to set up a keyless content engine for their brand or codebase, or get their content cited by AI answer engines (ChatGPT, Perplexity, Google AI). Triggers include "set up atomizekit", "make content about X", "turn this blog into posts", "atomize this article", "content for my brand", "AI SEO / GEO / get cited by AI".
license	MIT
metadata	{"requires":{"env":[]}}

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.