Competitor Spy

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 2515ce9634da · 15 Oct 2026Report an issue or request a re-scan

• PublisherAffitor GitHub organizationOwner of the repository this skill is read from
• Source folderAffitor/affiliate-skills/skills/research/competitor-spyThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict2515ce9634daA new audit runs when the source changes
• Repository statusNot archivedLast push 2 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Reverse-engineer successful affiliate strategies from competitors. Use this skill when the user asks about spying on competitors, researching what other affiliates promote, analyzing competitor affiliate sites,…

---
name: competitor-spy
description: >
  Reverse-engineer successful affiliate strategies from competitors.
  Use this skill when the user asks about spying on competitors, researching what
  other affiliates promote, analyzing competitor affiliate sites, understanding
  how top affiliates in a niche make money, or says "what programs does X promote",
  "how does [site] make money", "what affiliate strategy does this site use",
  "spy on competitor affiliates", "reverse engineer affiliate site", "copy what
  works in my niche", "who are the top affiliates in X niche", "what content
  gets traffic in my niche", "competitor affiliate analysis".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "research", "niche-analysis", "program-discovery", "competitive-analysis"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S1-Research
---

# Competitor Spy

Analyze competitor affiliate sites, YouTube channels, and social profiles to
surface which programs they promote, what content drives their traffic, and
which strategies are worth replicating. Outputs an actionable reverse-engineering
report so you can skip years of trial and error.

## Stage

This skill belongs to Stage S1: Research

## When to Use

- User wants to know what programs are working in a specific niche
- User has a competitor site/channel in mind and wants to understand their strategy
- User is entering a new niche and wants a shortcut to what works
- User wants to find underserved content gaps a competitor hasn't covered
- User asks "how do top affiliates in [niche] make money?"

## Input Schema

```
{
  competitor_url: string      # (optional) Direct URL to competitor site, channel, or profile
  niche: string               # (optional) Niche to analyze if no specific competitor given
  platform: string            # (optional) "blog" | "youtube" | "tiktok" | "twitter" | "newsletter"
  depth: string               # (optional, default: "standard") "quick" | "standard" | "deep"
  focus: string               # (optional) "programs" | "content" | "traffic" | "all"
}
```

## Workflow

### Step 1: Identify Competitors to Analyze

If `competitor_url` is provided, skip to Step 2.

If only `niche` is provided, find 3-5 top competitors:
1. `web_search "best [niche] affiliate sites"` — look for review/comparison sites
2. `web_search "[niche] review site affiliate"` — find review-first monetization models
3. `web_search "[niche] blog affiliate income report"` — income reports reveal programs
4. Note: YouTube — `web_search "youtube [niche] affiliate site:youtube.com"` to find channels

Pick 3 competitors that are clearly affiliate-driven (review pages, comparison tables,
"best X" content, Amazon links, affiliate disclaimers visible).

### Step 2: Identify Affiliate Programs They Promote

For each competitor site/channel:

**Method A — Link analysis:**
- `web_fetch [competitor_url]` and scan for outbound links
- Look for: `?ref=`, `?via=`, `/go/`, `aff_id=`, `?affiliate=`, `shareasale.com`,
  `impact.com`, `partnerstack.com`, `awin.com`, `cj.com`, `linktr.ee`
- These patterns indicate affiliate links

**Method B — Content analysis:**
- Look at their top content: "Best X", "X vs Y", "X Review", "X Alternatives"
- Every product featured prominently = likely affiliate relationship
- Products mentioned with a CTA button ("Try X Free", "Get X") = strong affiliate signal

**Method C — Disclosure scan:**
- Search page for "affiliate", "commission", "sponsored", "partner" disclosures
- These legally required disclosures often appear at top/bottom and reveal programs

**Method D — Income reports (if available):**
- `web_search "[site name] income report affiliate"` — some affiliates publish earnings
- `web_search "[creator name] how I make money affiliate"` — creator transparency posts

Extract for each program found: name, estimated prominence (primary/secondary/mentioned),
content type promoting it, and whether it appears on openaffiliate.dev.

### Step 2.5: Analyze Competitor Content Engagement (data-driven)

For each competitor, scan their recent content performance across social platforms.
This reveals not just WHAT they create, but HOW WELL it performs.

**With API (optional — see `shared/references/social-data-providers.md`):**
- Search YouTube/TikTok for competitor brand name or channel
- Get views, likes, comments, shares for their top 10-20 content pieces
- Calculate engagement_score for each: `(likes × 2 + comments × 3 + shares × 5) / max(views, 1) × 1000`
- Identify which content format gets them the highest engagement
- Compare their engagement against `trending-content-scout` benchmark (if available)

**Without API (default):**
- `web_search "[competitor name] youtube channel"` → find their channel
- `web_fetch` channel page → extract view counts from visible videos
- `web_search "[competitor name] tiktok"` → find top videos with view counts
- `web_search "[competitor name] best video"` → find their highest-performing content
- Note: approximate data, but reveals relative performance patterns

**Extract for each competitor:**
- **Avg engagement score** — how well does their content perform overall?
- **Strongest platform** — where do they get the most traction?
- **Weakest platform** — which platforms are they ignoring? (gap to exploit)
- **Top performing content** — their 3-5 best pieces by engagement
- **Format that works for them** — which content format gets them the most engagement?

Add these to the competitor assessment table in Step 5:

| Dimension | Score (1-10) | Assessment |
|-----------|-------------|------------|
| Content Engagement | — | How well does their content perform? High = proven demand, low = weak execution |
| Platform Strength | — | Which platform are they strongest on? Which are they ignoring? |

### Step 3: Analyze Their Content Strategy

For each competitor, extract:

**Content patterns:**
- Most common f

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-15
   ✓SAFE B 89 / 100

   first audit · source 2515ce9634da

Questions about Competitor Spy

• github.com/Affitor/affiliate-skills/tree/main/skills/research/co — the audited source at commit 2515ce9634da and every finding aboveObserved · 15 Oct 2026

Spotted something wrong? Report a correction · Methodology