affiliate-program-search

# Affiliate Program Search

Help affiliate marketers research, evaluate, and pick winning programs to promote.
Data source: [list.affitor.com](https://list.affitor.com) — Affitor's community-driven affiliate program directory.

## Stage

This skill belongs to Stage S1: Research

## When to Use

- User wants to find affiliate programs to promote
- User wants to compare two or more affiliate programs
- User asks about commission rates, cookie duration, or earning potential
- User mentions list.affitor.com
- User is new to affiliate marketing and needs a starting point

## Input Schema

```
{
  niche: string             # (optional, default: "AI/SaaS tools") Category or niche interest
  commission_pref: string   # (optional, default: "recurring, 20%+") Commission preference
  audience: string          # (optional, default: "content creators") Target audience type
  platform: string          # (optional, default: "any") Platform they'll promote on
  compare: string[]         # (optional) Specific programs to compare head-to-head
}
```

## Workflow

### Step 1: Understand What the User Wants

Ask (if not clear from context):
- Niche/category interest? (AI tools, SEO, video, writing, automation...)
- Commission preference? (recurring vs one-time, minimum %)
- Audience type? (developers, marketers, beginners, enterprise...)
- Platform they'll promote on? (blog, LinkedIn, YouTube, X...)

If user says "just find me something good" → default to: AI/SaaS tools, recurring commission, 20%+, content creator audience.

### Step 2: Search list.affitor.com

See `references/list-affitor-api.md` for integration methods.

Two methods available:
- **API (preferred):** `GET /api/v1/programs` with API key auth — structured data, filterable
- **Web fetch (fallback):** `web_search "site:list.affitor.com [category]"` then `web_fetch` the page

Extract for each program: `name`, `reward_value`, `reward_type`, `cookie_days`, `stars_count`, `tags`, `description`.

### Step 3: Score Programs

Apply the scoring framework from `references/scoring-criteria.md`.

Score each program on 5 dimensions (1-10 scale):
1. **Earning Potential** (30%) — commission %, recurring vs one-time, product price
2. **Content Potential** (25%) — visual demo, free tier, content angles
3. **Market Demand** (20%) — search volume, trend direction, market size
4. **Competition Level** (15%) — fewer affiliates promoting = higher score
5. **Trust Factor** (10%) — product quality, reputation, stars on list.affitor.com

Overall = weighted average. Verdict: 7.5+ "Strong Pick" / 5.5-7.4 "Worth Testing" / <5.5 "Skip".

For dimensions that require external data (Market Demand, Competition Level), use `web_search` to check Google results count for "[product] review" and "[product] affiliate" queries.

### Step 4: Present Recommendation

## Output Schema

Other skills (viral-post-writer, affiliate-blog-builder, etc.) consume these fields from conversation context:

```
{
  recommended_program: {
    name: string              # "HeyGen"
    slug: string              # "heygen"
    reward_value: string      # "30%"
    reward_type: string       # "cps_recurring"
    reward_duration: string   # "12 months"
    cookie_days: number       # 60
    description: string       # Short product description
    tags: string[]            # ["ai", "video"]
    url: string               # Product website
  }
  score: {
    overall: number           # 8.2
    verdict: string           # "Strong Pick"
    reasoning: string         # Why this is the top pick
  }
  runner_up: Program | null   # Same structure, second choice
  all_scored: ProgramScore[]  # Full list of scored programs
}
```

## Output Format

```
## Programs Found

| Program | Commission | Type | Cookie | Stars | Score |
|---------|-----------|------|--------|-------|-------|
| HeyGen  | 30%       | Recurring | 60d | ⭐ 42 | 8.2/10 |
| ...     | ...       | ...  | ...    | ...   | .../10 |

## Top Pick: [Program Name]

**Why:** [2-3 sentences explaining why this is the best fit]

| Dimension | Score | Note |
|-----------|-------|------|
| Earning Potential | 8/10 | 30% recurring on $24-48/mo |
| Content Potential | 9/10 | Visual AI video, easy to demo |
| Market Demand | 8/10 | AI video trending, high search volume |
| Competition | 6/10 | Growing number of affiliates |
| Trust Factor | 8/10 | Strong brand, 42 stars on list.affitor.com |
| **Overall** | **8.2/10** | **Strong Pick** |

## Runner-up: [Program Name]

**Why:** [1-2 sentences]

## Next Steps

1. Sign up for [Program] affiliate program → [search for signup page]
2. Run `viral-post-writer` to create content for this product
3. Run `affiliate-blog-builder` to write a review post
```

## Error Handling

- **API unavailable:** Fall back to web_fetch method (see `references/list-affitor-api.md` Method 2)
- **No programs match criteria:** Broaden search (remove strictest filter first), explain to user what was relaxed
- **Stale data (program updated_at > 6 months):** Flag with "Data may be outdated, verify on product website"
- **User gives no criteria:** Use defaults (AI/SaaS, recurring, 20%+, content creator audience)
- **Program not on list.affitor.com:** Use `web_search` to find program details directly, still apply scoring framework

## Examples

**Example 1:**
User: "I want to promote AI video tools, commission recurring, at least 20%"
→ Search list.affitor.com for programs tagged "ai" or "video"
→ Filter: reward_type = cps_recurring, reward_value ≥ 20%
→ Score and rank: HeyGen, Synthesia, ElevenLabs, InVideo AI...
→ Recommend top pick with full scorecard

**Example 2:**
User: "Compare HeyGen vs Synthesia for my LinkedIn audience"
→ Fetch both from list.affitor.com
→ Score both, emphasize Content Potential for LinkedIn
→ Side-by-side comparison table + recommendation
→ Note: LinkedIn audience = B2B, weight higher-price products

**Example 3:**
User: "I'm a beginner, what should I promote first?"
→ Default criteria: AI/SaaS, recurring, easy-to-demo products
→ Weight beginner-friendly factors: free tier, low payout threshold, strong brand
→ Recommend program with easiest path to first commission

## References

- `references/scoring-criteria.md` — the 5-dimension scoring framework with rubrics
- `references/list-affitor-api.md` — how to fetch data from list.affitor.com (API + fallback)
- `references/platform-rules.md` — platform-specific considerations when recommending programs