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sonpiaz

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I built 50 AI skills for affiliate marketing — here is the social intelligence layer

#opensource #ai #marketing #webdev

The Problem

Most AI-written affiliate content is generic. The AI picks a format by default, writes from training data, and hopes it converts. No engagement signals. No real sources. No data on what's actually working.

I wanted to fix that.

What I Built

affiliate-skills is a collection of 50 SKILL.md files — each one tells any AI exactly how to execute an affiliate marketing task. Input/output schemas, step-by-step workflows, error handling, and chaining metadata.

The v1.2 update adds a social intelligence layer: 5 new skills that bring real engagement data into the content creation pipeline.

The 5 New Skills

1. trending-content-scout (S1 Research)

Scans YouTube, TikTok, X, and Reddit for top-performing content by engagement score.

What it returns:

  • Top 20 content sorted by engagement
  • Winning formats (comparison: 45%, tutorial: 25%, review: 20%)
  • Best hooks ("I replaced my $5K video team" → engagement: 42.3)
  • Content gaps (nobody comparing HeyGen vs Synthesia on TikTok)
  • Engagement benchmark (median views, top 10% threshold)

Engagement Score Formula:

engagement_score = (likes × 2 + comments × 3 + shares × 5) / max(views, 1) × 1000
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Shares weighted 5x (strongest viral signal). Comments 3x (high-intent). Likes 2x (low-effort positive).

2. content-angle-ranker (S1 Research)

Generates 8-12 content angle candidates and scores each on 4 dimensions:

angle_score = (platform_fit × 0.25) + (competition_level × 0.30) + 
              (engagement_prediction × 0.30) + (creator_fit × 0.15)
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Output: ranked list with a clear #1 recommendation and ready-to-use parameters for the next skill.

3. traffic-analyzer (S1 Research)

Evaluates any website's traffic health before you commit to promoting their program. Calculates a Traffic Health Score 0-100, compares multiple domains side-by-side.

4. content-research-brief (S2 Content)

Collects 5-10 real source articles, auto-tags them (AI, Funding, SaaS, Tools, Trends), extracts key stats and quotes, and generates 3+ unique content angles — each backed by a different primary source.

5. infographic-generator (S2 Content)

Generates complete infographic specs: layout, data points, copy, color scheme. Platform-optimized (LinkedIn 1080×1350, IG square, Twitter landscape). Output as structured spec or self-contained HTML/CSS.

The Flywheel

Every skill knows what comes next. Data flows forward through the funnel and back through analytics:

S1 RESEARCH → S2 CONTENT → S3 BLOG & SEO → S4 OFFERS & LANDING
     ↑                                              ↓
     │                                        S5 DISTRIBUTION
     │                                              ↓
     └──────── S6 ANALYTICS ◀────────────────────────┘
                    ↓
              S7 AUTOMATION → SCALE
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Closed loop. S6 feeds back to S1. Your performance data refines the next scout run.

Data-driven. By the time you write, you already know what format, hook, and platform to use.

Research-backed. Content skills don't write from thin air. content-research-brief collects real articles first.

API-Optional Design

All skills work with just web_search and web_fetch. No API keys required.

But if you have APIs, the data gets better:

social_data_config:
  youtube:
    provider: "youtube-data-api"
    api_key: "AIzaSy..."
  tiktok:
    provider: "rapidapi"
    api_key: "YOUR_KEY"
    host: "tiktok-api23.p.rapidapi.com"
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Supported: YouTube Data API v3, RapidAPI, SerpAPI, Apify, or any custom API with field mapping.

Feedback Protocol

When a skill underperforms, it auto-generates a skill_feedback block:

skill_feedback:
  skill: "trending-content-scout"
  status: "partial_failure"
  issue_type: "data_quality"
  step_failed: "Step 2"
  description: "TikTok returned 0 results"
  severity: "low"
  suggestion: "Add alternative search queries"
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9 auto-detection triggers. Issue taxonomy: data_quality, hallucination, chain_break, schema_mismatch, etc.

Try It

Any AI, no install:

Paste into Claude, ChatGPT, or Gemini:

"Scout trending content about AI video tools on YouTube and TikTok. 
Show me top content by engagement, winning formats, and content gaps."
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Claude Code / Pi:

git clone https://github.com/Affitor/affiliate-skills.git ~/.claude/skills/affiliate-skills
cd ~/.claude/skills/affiliate-skills && ./setup
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Full repo: github.com/Affitor/affiliate-skills

50 skills. 8 stages. MIT license. Works with any AI.

What does your content research workflow look like? Would love to hear how others approach data-driven content creation.

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