| name | content-research-brief |
| description | Research trending topics, collect source articles, and generate a structured research brief for content creation. Stop writing from thin air ā write from real sources. Use this skill when the user wants to research a topic before writing, collect sources for an article, create a research-backed content brief, or says "research [topic] for me", "find sources about [keyword]", "content brief for [topic]", "what's the latest on [product]", "research before writing", "collect articles about [keyword]", "trending news about [topic]", "gather sources for my article", "brief me on [topic]", "what are people saying about [product]", "news roundup for [keyword]", "research brief", "source collection", "content research", "prep research for writing".
|
| license | MIT |
| version | 1.0.0 |
| tags | ["affiliate-marketing","content-creation","research","content-brief","source-collection"] |
| compatibility | Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent |
| metadata | {"author":"affitor","version":"1.0","stage":"S2-Content"} |
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 ā 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 approach:
Topic ā Search ā Select sources ā Synthesize ā Write with context.
Stage
This skill belongs to Stage S2: Content ā 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 ā research it deeper
- When you want unique angles: N sources ā N different content pieces
Input Schema
topic: string
source_count: number
source_types: string[]
freshness: string
product: object
name: string
url: string
language: string
angle_count: number
Workflow
Step 1: Search for Sources
Execute multiple searches to find diverse, high-quality sources:
Primary search:
web_search "[topic]" ā top results
Source-type-specific searches:
IF "news" in source_types:
web_search "[topic] news [current year]" ā recent news articles
IF "blog" in source_types:
web_search "[topic] blog review analysis" ā in-depth blog posts
IF "linkedin" in source_types:
web_search "[topic] site:linkedin.com" ā LinkedIn posts/articles
IF "youtube" in source_types:
web_search "[topic] site:youtube.com" ā YouTube videos with descriptions
IF "reddit" in source_types:
web_search "[topic] site:reddit.com" ā Reddit discussions with real user opinions
IF "academic" in source_types:
web_search "[topic] research study data statistics" ā 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:
web_fetch [url] ā extract full article text
- If fetch fails (paywall, timeout) ā use search snippet as summary, note limitation
- 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
Quotes & Insights:
- Expert opinions
- User testimonials (from Reddit, reviews)
- Founder/CEO statements
- Analyst predictions
Facts & Features:
- Product features mentioned across multiple sources
- Recent updates/launches
- Integration ecosystem
- Competitive positioning
Step 5: Synthesize Unique Angles
From the collected sources, generate angle_count unique content angles.
Angle generation rules:
- Each angle must use a DIFFERENT primary source as its foundation
- All angles use ALL sources as context (richer data)
- Each angle must have a distinct hook and perspective
- At least one angle should be contrarian or non-obvious
For each angle:
Angle:
title: string
primary_source: string
hook: string
key_data: string[]
format_suggestion: string
unique_value: string
Step 6: Compile Research Brief
Organize everything into a structured brief that downstream skills can consume.
Step 7: Self-Validation
Before presenting output, verify:
If any check fails, fix before delivering. Do not flag checklist to user.
Output Schema
output_schema_version: "1.0.0"
topic: string
sources_collected: number
sources_fetched: number
sources:
- title: string
url: string
published_date: string | null
tags: string[]
key_data_points: string[]
key_quotes: string[]
main_thesis: string
unique_info: string
fetch_status: "full" | "snippet"
master_data:
stats: string[]
quotes: string[]
facts: string[]
timeline: string[]
angles:
- title: string
primary_source: string
hook: string
key_data: string[]
format_suggestion: string
unique_value: string
recommended_next_skill: string
Output Format
## Content Research Brief: [Topic]
š **[X] sources collected** | [Y] fully fetched | Freshness: [month]
š·ļø **Top tags:** AI (5), Tools (3), Pricing (2), Comparison (2)
---
### š° Sources
| # | Title | Tags | Date | Status |
|---|-------|------|------|--------|
| 1 | [Title](url) | AI, Tools | Mar 2024 | ā
Full |
| 2 | [Title](url) | Pricing, Comparison | Feb 2024 | ā
Full |
| 3 | [Title](url) | Trends, Growth | Mar 2024 | ā ļø Snippet |
| ... | ... | ... | ... | ... |
---
### š Key Data Points (from sources)
**Stats:**
- [Stat 1] ā Source: [#1]
- [Stat 2] ā Source: [#3]
- [Stat 3] ā Source: [#2, #5]
**Quotes:**
- "[Quote]" ā [Person], [Role] (Source: [#4])
- "[Quote]" ā [Person] (Source: [#2])
**Key Facts:**
- [Fact 1] ā mentioned in [X] sources
- [Fact 2] ā mentioned in [Y] sources
---
### šÆ Content Angles (ready to write)
#### Angle 1: "[Title]"
- **Primary source:** [#2] ā [title]
- **Hook:** "[Opening line]"
- **Key data:** [stat 1], [stat 2], [quote]
- **Best format:** LinkedIn post
- **Unique value:** [Why this isn't generic]
ā Run: `viral-post-writer` with angle: "[this angle]"
#### Angle 2: "[Title]"
- **Primary source:** [#5] ā [title]
- **Hook:** "[Opening line]"
- **Key data:** [stat 3], [fact 1]
- **Best format:** Blog article
- **Unique value:** [Why this is different from Angle 1]
ā Run: `affiliate-blog-builder` with angle: "[this angle]"
#### Angle 3: "[Title]" (Contrarian)
- **Primary source:** [#7] ā [title]
- **Hook:** "[Opening line]"
- **Key data:** [counter-stat], [user complaint from Reddit]
- **Best format:** Twitter thread
- **Unique value:** Goes against the dominant narrative ā [reasoning]
ā Run: `twitter-thread-writer` with angle: "[this angle]"
---
### š Next Steps
1. **Pick an angle** and run the suggested content skill
2. **Combine angles** ā use `content-pillar-atomizer` to turn one angle into 15+ pieces
3. **Add visuals** ā use `infographic-generator` to create a data infographic from the key stats
Error Handling
- Topic too vague: Ask user to narrow down. "'Marketing' is too broad. Can you specify? e.g., 'email marketing automation tools' or 'TikTok marketing for SaaS'."
- Few sources found: If <3 sources, note: "Limited sources available for this topic. The brief may lack depth. Consider broadening the topic or checking if it's too niche."
- Most sources behind paywalls: Use search snippets. Note: "[X] sources couldn't be fully fetched (paywalls). Brief uses search snippets for those. Data may be less detailed."
- Sources are all from the same perspective: Note bias. "Warning: all [X] sources are positive reviews. No critical perspectives found. Consider adding 'reddit' or 'opinion' to source_types for balanced content."
- Outdated sources: If freshness filter returns old results, widen the time range and note: "Most recent sources are from [date]. This topic may not have recent coverage."
- Non-English topic: Research in the specified language. Note if source diversity is limited in that language.
Examples
Example 1:
User: "Research HeyGen for a LinkedIn post"
ā topic: "HeyGen AI video", source_types: ["news", "blog", "linkedin"], freshness: "month"
ā Collect 7 sources: 2 news (HeyGen raises $60M), 3 blog reviews, 2 LinkedIn posts
ā Tags: AI (7), Funding (2), Tools (5), Comparison (1)
ā Key stats: "$60M Series A", "40K+ businesses", "Avatar 3.0 launch"
ā Angles: (1) "HeyGen just raised $60M ā here's what it means for AI video" (LinkedIn),
(2) "I tested HeyGen vs Synthesia for 30 days" (blog), (3) "AI video tools are killing
the $45B video production industry" (Twitter thread)
Example 2:
User: "Brief me on email marketing trends, I want to write a comparison blog post"
ā topic: "email marketing trends 2024", source_types: ["news", "blog", "reddit"]
ā Collect 8 sources covering: AI personalization, interactive emails, privacy changes, deliverability
ā Angles focused on comparison: "ConvertKit vs Mailchimp in 2024: the real differences after
Apple Mail Privacy Protection"
Example 3:
User: "Research what people are really saying about ClickUp on Reddit"
ā topic: "ClickUp", source_types: ["reddit", "blog"], freshness: "month"
ā 4 Reddit threads (raw opinions), 3 blog reviews
ā Unique angle: Reddit users love the free tier but hate the learning curve ā
"ClickUp: the free tool that takes a month to learn (and why it's still worth it)"
Feedback & Issue Reporting
When this skill produces unexpected, incomplete, or incorrect output, generate a
skill_feedback block (see shared/references/feedback-protocol.md for full schema).
Skill-specific failure modes:
- Most sources paywalled: <3 sources fully fetched. Report as
data_quality, list which URLs failed.
- All sources same perspective: No balanced/critical viewpoints found. Report as
data_quality, note bias direction.
- Hallucinated stats: Agent generated a stat not from any fetched source. Report as
hallucination, critical severity.
- Angles not unique: All 3 angles are rephrased versions of the same take. Report as
wrong_output.
Auto-detect triggers:
sources_fetched < 3 (most failed)
- All source
tags are identical (no diversity)
- Any data point in
master_data.stats cannot be traced to a specific source URL
angles array has <2 entries
Report issues: GitHub Issues | Discussions
References
shared/references/social-data-providers.md ā API configuration for enhanced search
shared/references/flywheel-connections.md ā master flywheel connection map
shared/references/ftc-compliance.md ā source attribution and disclosure requirements
shared/references/feedback-protocol.md ā issue detection and reporting standard
Flywheel Connections
Feeds Into
viral-post-writer (S2) ā research brief with angles, data points, and quotes
affiliate-blog-builder (S3) ā deep research for long-form articles
tiktok-script-writer (S2) ā key stats and hooks for video scripts
twitter-thread-writer (S2) ā data-rich thread material
reddit-post-writer (S2) ā real user opinions for authentic Reddit content
content-pillar-atomizer (S2) ā research brief as the pillar to atomize
infographic-generator (S2) ā key stats and data for visual content
comparison-post-writer (S3) ā multi-source comparison data
listicle-generator (S3) ā curated sources for listicle content
Fed By
trending-content-scout (S1) ā trending topics and content gaps to research deeper
niche-opportunity-finder (S1) ā niche keywords to research
content-angle-ranker (S1) ā recommended angle to research supporting data
competitor-spy (S1) ā competitor strategies to research and counter
Feedback Loop
- S6
performance-report shows which content with research briefs outperforms non-researched content ā reinforces research-first workflow
chain_metadata:
skill_slug: "content-research-brief"
stage: "content"
timestamp: string
suggested_next:
- "viral-post-writer"
- "affiliate-blog-builder"
- "infographic-generator"
- "content-pillar-atomizer"