Content Engine

Content Engine

Full-stack AI content studio: compile visual identity once, generate premium content at scale, distribute everywhere.

COMPILE → GENERATE → POST-PRODUCE → DISTRIBUTE → MEASURE → REFINE

Commands

Architecture

Four sub-skills, each handling one layer:

[content-engine-dna]       Visual DNA Compiler
        ↓                  raw/ → compiled/ (brand DNA, character sheets, style guides)
[content-engine-cinema]    Cinematic Generation Layer
        ↓                  compiled identity → tool-specific prompts → generation
[content-engine-autopilot] Browser Orchestration
        ↓                  Playwright drives tools OR API calls → organized output
[content-engine-loop]      Content Loop + Distribution
                           compounds /blog-post + /content-creation + /social-intelligence

Quick Start

1. Compile Brand Identity

Drop reference assets into knowledge/raw/:

• Brand campaign photos → knowledge/raw/brand-assets/
• Character face references → knowledge/raw/character-refs/
• Style inspiration (mood boards, reference reels) → knowledge/raw/style-inspiration/

Then compile:

/content-engine compile

This analyzes all raw assets via Gemini multimodal and produces compiled identity files in knowledge/compiled/ with tool-specific prompt fragments.

2. Generate Content

Write a scene brief or use a campaign plan:

/content-engine generate --brand acme --character luna --scenes 5 --format reels

The engine:

1. Reads compiled identity (brand DNA + character sheet + style guide)
2. Selects the best tool per the tool priority matrix
3. Injects compiled identity into tool-specific prompts
4. Generates via API (fal.ai, @google/genai) or browser automation
5. Runs post-production (upscale → grade)
6. Organizes output with manifest.json tracking

3. Run a Full Campaign

/content-engine campaign "Mediterranean lifestyle, 10 summer scenes, golden hour, reels + carousel"

Orchestrates all four skills end-to-end: compile (if needed) → generate scenes → post-produce → adapt for platforms → distribute.

4. Distribute

/content-engine loop

Compounds existing skills for multi-platform distribution:

• /blog-post — Writing + 6 platform adaptations
• /content-creation — TTS, Remotion video, media pipeline
• /social-intelligence — Distribution + engagement monitoring
• /brainrot-for-good — High-retention short-form video
• /brand-icons — OG images, social cards

Setup & Prerequisites

Required

# Check prerequisites
echo "=== Required ==="
which ffmpeg && echo "ok ffmpeg" || echo "MISSING: brew install ffmpeg"
echo ""
echo "=== API Keys ==="
[ -n "$GEMINI_API_KEY" ] && echo "ok GEMINI_API_KEY" || echo "MISSING: needed for Gemini analysis + Veo 3.1"
[ -n "$FAL_KEY" ] && echo "ok FAL_KEY" || echo "MISSING: needed for Nano Banana 2, Kling via fal.ai"
echo ""
echo "=== Higgsfield CLI (recommended for agent workflows) ==="
which higgsfield && echo "ok higgsfield $(higgsfield version 2>/dev/null | head -1)" || echo "MISSING: curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh"
echo ""
echo "=== Browser Automation ==="
which agent-browser && echo "ok agent-browser" || echo "MISSING: needed for autopilot mode"

Higgsfield: two integration paths

Higgsfield offers BOTH a CLI and an MCP. Pick based on your runtime:

CLI path (recommended for content-engine):

# Install
curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh

# Auth (interactive, opens browser)
higgsfield auth login

# Verify
higgsfield account status

# Capabilities exposed via three skill-level wrappers:
#   higgsfield-generate         — 30+ models (Nano Banana 2, Soul V2, Veo 3.1, Kling 3.0, Seedance 2.0, Flux 2)
#   higgsfield-product-photoshoot — Brand-quality product images with mode-specific enhancement
#   higgsfield-soul-id          — Train Soul Character refs for consistent face/identity

MCP path (for Claude Desktop):

1. Open Claude settings → Connectors → Add custom connector
2. Name: Higgsfield
3. URL: https://mcp.higgsfield.ai
4. Click Add → Connect → authenticate via Higgsfield account

Both paths use the same Higgsfield credit pool. No API key needed for either; auth is via your Higgsfield account.

Optional (enhance quality)

• Topaz Gigapixel AI — CLI upscaling (falls back to Real-ESRGAN)
• ComfyUI — Local node-based pipelines with LoRA style-locking
• Weavy account — Scene variation with character consistency
• Artlist.io — AI-powered music matching

Tool Session Setup

For browser-automated tools (legacy path; prefer the CLI when available):

/content-engine autopilot setup higgsfield   # browser fallback if CLI not available
/content-engine autopilot setup weavy

This launches Chrome, you log in manually, and the session is saved for future automated use.

Knowledge Architecture

Karpathy Compile-Then-Query Pattern

knowledge/
├── raw/              # Immutable source material (never modified by LLM)
│   ├── brand-assets/     # Campaign photos, logos, style guides
│   ├── character-refs/   # Face photos, pose references
│   ├── style-inspiration/# Mood boards, reference reels
│   └── scene-briefs/     # Scene descriptions
├── compiled/         # LLM-compiled identity files (the "wiki")
│   ├── brands/           # Per-brand DNA (.md)
│   ├── characters/       # Per-character sheets (.md)
│   └── styles/           # Compiled style guides (.md)
└── schema.md         # Compilation rules + templates

raw/ is source code. compiled/ is executable. The LLM is the compiler.

Every compiled file:

• Traces provenance to raw sources
• Contains tool-specific prompt fragments
• Is human-reviewable Markdown
• Gets actively maintained via lint

Mapping to Existing Patterns

Tool Priority Matrix

Generation Modes

Mode 1: API-First (fastest, programmatic — preferred for agent workflows)

• higgsfield CLI (via higgsfield-generate, higgsfield-product-photoshoot, higgsfield-soul-id) → 30+ models including Soul V2, Nano Banana 2, Veo 3.1, Kling 3.0, Seedance 2.0, Flux 2, GPT Image 2; plus Marketing Studio (avatar + product ad modes); plus Soul Character training
• fal.ai → Nano Banana 2, Kling, Veo (alternate provider when models overlap)
• @google/genai → Veo 3.1, Gemini image (Google-native path)
• All three callable directly from Claude Code, no browser needed

Mode 2: MCP-driven (for Claude Desktop / IDE plugins)

• Higgsfield MCP at https://mcp.higgsfield.ai — same models, GUI-native auth, can't be scripted from Claude Code
• ComfyUI MCP (planned extension)

Mode 3: Browser-Automated (tools without APIs or MCP)

• Playwright drives Weavy, Artlist, Soul Cinema (legacy — prefer CLI now)
• Auth persisted via saved session state
• Batch generation with organized output

Mode 4: Local Pipeline (maximum control)

• ComfyUI + Stable Diffusion + LoRA
• Full node-based control over every generation step
• Topaz CLI for upscaling

Mode 3: Local Pipeline (maximum control)

• ComfyUI + Stable Diffusion + LoRA
• Full node-based control over every generation step
• Topaz CLI for upscaling

Output Organization

output/{campaign-slug}/
├── raw/          # Direct generation output
├── upscaled/     # After Topaz/Real-ESRGAN pass
├── graded/       # After color grading
└── manifest.json # Prompts, identity refs, tool used, timestamps

Extension Points

Extensions live in extensions/. Each extension:

• Has its own SKILL.md declaring which pipeline stage it hooks into
• Hook points: pre-generation, post-generation, post-production, distribution
• Can read from compiled/ but only writes to its own output namespace
• Registered in extensions/README.md

Planned Extensions

• OpenCaptions — Intent-driven captions (post-production hook)
• ComfyUI MCP — Direct tool calls for node pipelines
• LoRA Training — Compiled DNA as training data for custom models

Reference Library — distilled craft

Deep, copy-paste-ready craft references live alongside each sub-skill. The cluster below was distilled from the full AI Video Creators course (BRO-1525 — 12.7 h video + 54 lessons; source playbook in broomva/workspace docs/reference/ai-video-creators-course/). Load the one that matches the task; the prompt structure and craft laws are durable even as model versions rotate.

Compounding Skills

This skill compounds on the existing broomva content ecosystem:

Research Sources

Built from analysis of:

• viznfr — Claude Code + Playwright autopilot, Nano Banana character sheets, brand DNA extraction
• ohneis652 — ComfyUI node pipelines, LoRA style-locking, Soul Cinema start-frame doctrine, 25 design styles
• AI Video Creators (Skool) — fully captured & distilled (BRO-1525): ReelEngine/PromptEngine prompt craft, image-first pipeline, Visual-DNA art direction, character consistency, motion control, audio/edit craft, and the monetization playbook. See the Reference Library above + broomva/workspace docs/reference/ai-video-creators-course/.
• MemPalace — Spatial hierarchy, AAAK compression, MCP-native memory
• Karpathy LLM Wiki — raw/wiki/schema 3-layer architecture, compile-then-query, active linting