content-research-brief

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• SKILL.md

SKILL.md (first lines)

# Content Research Brief

Research a topic by collecting 5-10 real source articles, auto-tagging them by theme,
extracting key data points, and synthesizing unique content angles. The output is a
structured research brief that any downstream content skill can consume.

**The problem this solves:** Most AI-written affiliate content is generic because it's
written from the model's training data — not from real, current sources. This skill
forces research-first content creation: find real articles, extract real data, then
write from those sources. The result is content with specific stats, real quotes, and
current information that readers (and Google) actually value.

Inspired by the [content-pipeline](https://github.com/Affitor/content-pipeline) approach:
Topic → Search → Select sources → Synthesize → Write with context.

## Stage

This skill belongs to Stage S2: Content — but acts as the research foundation for all content skills.

## When to Use

- Before writing any article, blog post, or long-form content
- When you need current data and stats about a topic (not just AI-generated claims)
- When creating comparison content (need real feature/pricing data from sources)
- When writing about a product launch, funding round, or industry trend
- After `trending-content-scout` identifies a topic — research it deeper
- When you want unique angles: N sources → N different content pieces

## Input Schema

```yaml
topic: string                  # (required) "HeyGen AI video tool", "email marketing trends 2024"
source_count: number           # (optional, default: 7) How many sources to collect (3-10)
source_types: string[]         # (optional, default: ["news", "blog"])
                               # Options: "news" | "blog" | "linkedin" | "youtube" | "reddit" | "academic"
freshness: string              # (optional, default: "month") "day" | "week" | "month" | "year" | "any"
product: object                # (optional) Focus research on a specific product
  name: string                 # "HeyGen"
  url: string                  # "https://heygen.com"
language: string               # (optional, default: "en") "en" | "vi" | any ISO 639-1 code
angle_count: number            # (optional, default: 3) How many unique content angles to generate
```

## Workflow

### Step 1: Search for Sources

Execute multiple searches to find diverse, high-quality sources:

```
Primary search:
  web_search "[topic]" → top results
  
Source-type-specific searches:
  IF "news" in source_types:
    web_search "[topic] news [current year]" → recent news articles
  IF "blog" in source_types:
    web_search "[topic] blog review analysis" → in-depth blog posts
  IF "linkedin" in source_types:
    web_search "[topic] site:linkedin.com" → LinkedIn posts/articles
  IF "youtube" in source_types:
    web_search "[topic] site:youtube.com" → YouTube videos with descriptions
  IF "reddit" in source_types:
    web_search "[topic] site:reddit.com" → Reddit discussions with real user opinions
  IF "academic" in source_types:
    web_search "[topic] research study data statistics" → data-heavy sources

Product-specific (if product provided):
  web_search "[product.name] review [current year]"
  web_search "[product.name] alternatives comparison"
  web_search "[product.name] pricing features"
  web_search "[product.name] news launch update"
```

Collect 15-20 search results, then filter down to `source_count` best sources.

### Step 2: Fetch and Extract Source Content

For each selected source:
1. `web_fetch [url]` → extract full article text
2. If fetch fails (paywall, timeout) → use search snippet as summary, note limitation
3. Extract from each source:
   - **Title** and **URL**
   - **Published date** (if available)
   - **Key data points**: stats, numbers, percentages, dollar amounts
   - **Key quotes**: noteworthy statements from experts or users
   - **Main argument/thesis**: what is this source's core message?
   - **Unique information**: what does this source have that others don't?

### Step 3: Auto-Tag Sources

Tag each source with 1-3 theme tags:

| Tag | Trigger Keywords |
|-----|-----------------|
| **AI** | artificial intelligence, machine learning, GPT, neural, model |
| **Funding** | raised, funding, series A/B/C, investment, valuation, IPO |
| **SaaS** | software, subscription, platform, B2B, enterprise |
| **Tools** | tool, app, feature, integration, API, plugin |
| **Trends** | trend, growing, emerging, future, prediction, forecast |
| **Startup** | startup, founder, launch, early-stage, bootstrapped |
| **Growth** | revenue, ARR, users, growth, scale, market share |
| **Industry** | market, industry, sector, regulation, compliance |
| **Pricing** | pricing, cost, free tier, discount, plan, subscription |
| **Comparison** | vs, versus, alternative, compare, switch, migrate |
| **Tutorial** | how to, guide, step-by-step, tutorial, walkthrough |
| **Opinion** | I think, in my experience, hot take, unpopular opinion |

### Step 4: Extract Key Data Points

From all sources combined, extract a master list of:

**Stats & Numbers:**
- Revenue/valuation figures
- User counts / growth rates
- Market size data
- Performance metrics
- Pricing data points

From Affitor/affiliate-skills, MIT.

More from Affitor/affiliate-skills