ai-marketing-skills/content-ops/README.md
Alfred Claw a96d0d8889 Initial commit: 6 AI marketing skill categories
- growth-engine: Autonomous experiment engine (Karpathy autoresearch for marketing)
- sales-pipeline: RB2B router, deal resurrector, trigger prospector, ICP learner
- content-ops: Expert panel, quality gate, editorial brain, quote miner
- outbound-engine: Cold outbound optimizer, lead pipeline, competitive monitor
- seo-ops: Content attack briefs, GSC optimizer, trend scout
- finance-ops: CFO briefing, cost estimate, scenario modeler

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AI Content Ops

Ship content that scores 90+ every time. Automatically.

Most content teams publish and pray. This pipeline scores, gates, and iterates every piece of content through an AI expert panel before it goes live. Nothing ships below 90/100.

What's Inside

🎯 Expert Panel (SKILL.md)

Claude Code skill that auto-assembles a panel of 7-10 domain experts tailored to whatever you're scoring. Works on:

  • Blog posts, social content, email sequences
  • Landing pages, ads, CTAs
  • Strategy docs, pitch decks, charts
  • Recruiting outreach, vendor evaluations
  • Literally anything that needs a quality gate

The panel scores your content, identifies weaknesses, revises, and loops until every expert scores 90+. Max 3 rounds. Includes a 1.5x-weighted AI Writing Detector that catches all 24 known AI writing patterns.

🚦 Content Quality Gate (scripts/content-quality-gate.py)

CI/CD-style gate for your content pipeline. Runs the quality scorer on a batch of drafts and filters out anything below threshold. Nothing publishes without passing.

📊 Content Quality Scorer (scripts/content-quality-scorer.py)

Automated scoring engine with 5 dimensions:

  • Voice similarity (35%) — matches your brand voice patterns
  • Specificity (25%) — real numbers, named entities, concrete examples
  • AI slop penalty (20%) — detects and penalizes 50+ banned AI words and 8 AI writing patterns
  • Length appropriateness (10%) — platform-specific character limits
  • Engagement potential (10%) — hooks, CTAs, debate invitations

🧠 Editorial Brain (scripts/editorial-brain.py)

Two-pass LLM analysis for finding clip-worthy moments in video transcripts:

  1. Pass 1: Scans transcript chunks for candidate moments (hook → build → payoff arcs)
  2. Pass 2: Deep-scores each candidate on hook/build/payoff/clean-cut (0-100)
  3. Only 90+ clips get cut

Fundamentally different from keyword matching. Thinks like a human editor.

⛏️ Quote Mining Engine (scripts/quote-mining-engine.py)

Scans podcast RSS feeds and meeting notes to extract quotable, contrarian, viral-worthy moments. Scoring heuristics:

  • Contrarian signals (wrong, myth, overrated, secret...)
  • Specificity signals ($amounts, percentages, multipliers)
  • Emotional triggers (fear, love, shocking, AI...)
  • Shareability signals (how to, framework, lesson learned...)

🔄 Content Transform (scripts/content-transform.py)

Repurposes long-form content into platform-native formats:

  • X threads/posts — punchy, data-driven, with ASCII diagrams
  • LinkedIn posts — hook before the fold, story arc, engagement CTA
  • YouTube Short scripts — HOOK/SETUP/PAYOFF/CTA structure with visual cues
  • Newsletter sections — scannable, value-dense, "why this matters"

Includes optional expert panel integration for iterative quality improvement.

Quick Start

# 1. Install dependencies
pip install -r requirements.txt

# 2. Set up environment
cp .env.example .env
# Edit .env with your API keys

# 3. Score a batch of content drafts
python scripts/content-quality-scorer.py --input drafts.json --verbose

# 4. Run the quality gate
python scripts/content-quality-gate.py --input drafts.json --threshold 70

# 5. Mine quotes from your podcast RSS
python scripts/quote-mining-engine.py --days 90 --min-score 60

# 6. Find clip-worthy moments in a video
python scripts/editorial-brain.py --url "https://youtube.com/watch?v=..." --min-score 90

# 7. Transform content atoms into platform drafts
python scripts/content-transform.py --atoms atoms.json --top-n 10

Configuration

All scripts use environment variables for configuration. See .env.example for the full list.

Voice Customization

The quality scorer and content transformer use configurable voice patterns. Edit these in your .env or pass custom config files:

  • VOICE_MARKERS — regex patterns that signal your brand voice
  • BANNED_WORDS — AI slop vocabulary to penalize
  • PLATFORM_LIMITS — character limits per platform

Scoring Weights

Adjust scoring weights via a JSON config file:

{
  "weights": {
    "voice_similarity": 0.35,
    "specificity": 0.25,
    "slop_penalty": 0.20,
    "length_appropriateness": 0.10,
    "engagement_potential": 0.10
  },
  "threshold": 70
}

Expert Panel Domains

Pre-built expert panels included:

  • experts/humanizer.md — AI writing detection (24 patterns, mandatory)
  • experts/x-articles.md — X/Twitter long-form posts
  • experts/linkedin.md — LinkedIn posts
  • experts/newsletter.md — Email newsletters
  • experts/youtube-shorts.md — YouTube Shorts scripts
  • experts/instagram.md — Instagram visual content
  • experts/podcast-quotes.md — Podcast quote cards
  • experts/recruiting.md — Recruiting outreach
  • experts/seo-strategy.md — SEO strategy docs

Scoring rubrics:

  • scoring-rubrics/content-quality.md — Blog, social, email, scripts
  • scoring-rubrics/strategic-quality.md — Strategy and analysis
  • scoring-rubrics/conversion-quality.md — Landing pages, ads, CTAs
  • scoring-rubrics/visual-quality.md — Charts, infographics, slides
  • scoring-rubrics/evaluation-quality.md — Candidate/vendor evaluations

Input Formats

Content Drafts (for scorer/gate)

{
  "drafts": [
    {
      "id": "draft-001",
      "platform": "x",
      "draft": "Your content text here..."
    }
  ]
}

Content Atoms (for transformer)

{
  "atoms": [
    {
      "id": "atom-001",
      "content": "Long-form source content...",
      "tags": ["AI", "marketing"],
      "platforms_missing": ["x", "linkedin"],
      "repurpose_score": 8
    }
  ]
}

Architecture

Content Source → Content Transform → Quality Scorer → Quality Gate → Publish
                      ↑                    ↓
                Expert Panel ←── Revision Loop (max 3 rounds)

The pipeline is modular. Use any script standalone or wire them together.

License

MIT