Agent skill

Content Research Brief

by Affitor in Affitor/affiliate-skills

Research trending topics, collect source articles, and generate a structured research brief for content creation.

MITAuto-check passedMarketing & SEO

Install Content Research Brief

skills CLI
$ npx skills add Affitor/affiliate-skills --skill content-research-brief -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Affitor/affiliate-skills content-research-brief --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Affitor/affiliate-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/content/content-research-brief .claude/skills/content-research-brief && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
content-research-brief
GitHub stars
700
Token cost
~4.1k tokens
SKILL.md length
1,230 words
Files
1
Skills in repo
50
Repo updated
First seen
Licence
MIT

At a glance

Research trending topics, collect source articles, and generate a structured research brief for content creation.

  • Works in 7 steps: Search for Sources → Fetch and Extract Source Content → Auto-Tag Sources → …
  • The user wants to research a topic before writing
  • SKILL.md covers Stage, When to Use, Input Schema and Workflow, plus 7 more sections
  • Reaches heygen.com

What it does

Content Research Brief is an agent skill from Affitor/affiliate-skills. Research trending topics, collect source articles, and generate a structured research brief for content creation. Stop writing from thin air — 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…

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

It sits in Marketing & SEO, covering Deep research, Content strategy and Keyword research. The repository describes itself as: 50 AI agent skills for affiliate marketing. Research trending content, write data-backed posts, generate infographics, build landing pages, deploy — full flywheel with social… The licence is MIT.

When your agent uses it

  • The user wants to research a topic before writing
  • Collect sources for an article
  • Create a research-backed content brief
  • Says research [topic] for me

Example prompts

  • “research [topic] for me”
  • “find sources about [keyword]”
  • “content brief for [topic]”
  • “/content-research-brief”

Requirements

  • Compatibility (from SKILL.md): Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Search for Sources
  2. Fetch and Extract Source Content
  3. Auto-Tag Sources
  4. Extract Key Data Points
  5. Synthesize Unique Angles
  6. Compile Research Brief
  7. Self-Validation

What it can do on your machine

Read from SKILL.md and the folder at commit e43bfae. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • heygen.com

    Also links to:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent

    From compatibility in the SKILL.md frontmatter.

Context cost

Content Research Brief loads about 4.1k tokens when it runs. Until then it costs about 193 tokens; SKILL.md has 1,230 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~193
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Affitor/affiliate-skills at commit e43bfae, republished under its MIT licence (© Affitor). 1,230 words, ~4,148 tokens.

Download SKILL.mdSave it as .claude/skills/content-research-brief/SKILL.md (or your agent's skills folder).
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 — 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".
compatibility
Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent
license
MIT
version
1.0.0
tags
affiliate-marketing, content-creation, research, content-brief, source-collection
metadata.author
affitor
metadata.version
1.0
metadata.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 — 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 → 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:

TagTrigger Keywords
AIartificial intelligence, machine learning, GPT, neural, model
Fundingraised, funding, series A/B/C, investment, valuation, IPO
SaaSsoftware, subscription, platform, B2B, enterprise
Toolstool, app, feature, integration, API, plugin
Trendstrend, growing, emerging, future, prediction, forecast
Startupstartup, founder, launch, early-stage, bootstrapped
Growthrevenue, ARR, users, growth, scale, market share
Industrymarket, industry, sector, regulation, compliance
Pricingpricing, cost, free tier, discount, plan, subscription
Comparisonvs, versus, alternative, compare, switch, migrate
Tutorialhow to, guide, step-by-step, tutorial, walkthrough
OpinionI 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:

  1. Each angle must use a DIFFERENT primary source as its foundation
  2. All angles use ALL sources as context (richer data)
  3. Each angle must have a distinct hook and perspective
  4. At least one angle should be contrarian or non-obvious

For each angle:

yaml
Angle:
  title: string                # Specific, could be a headline
  primary_source: string       # Which source drives this angle
  hook: string                 # Opening line
  key_data: string[]           # 2-3 data points from sources that support this angle
  format_suggestion: string    # "linkedin_post" | "blog_article" | "tiktok_script" | "twitter_thread"
  unique_value: string         # What makes this angle different from generic AI-written content
Step 6: Compile Research Brief

Organize everything into a structured brief that downstream skills can consume.

Step 7: Self-Validation

Before presenting output, verify:

  • All sources are real URLs (not hallucinated)
  • Data points are attributed to specific sources
  • At least 3 sources were successfully fetched (not just search snippets)
  • Angles are genuinely different from each other (not rephrased versions)
  • Tags accurately reflect source content
  • Brief includes both positive and critical/balanced perspectives

If any check fails, fix before delivering. Do not flag checklist to user.

Output Schema

yaml
output_schema_version: "1.0.0"
topic: string
sources_collected: number
sources_fetched: number                # how many were fully fetched vs snippet-only
sources:
  - title: string
    url: string
    published_date: string | null
    tags: string[]                     # ["AI", "Tools", "Pricing"]
    key_data_points: string[]          # extracted stats and numbers
    key_quotes: string[]               # notable quotes
    main_thesis: string                # 1-sentence summary
    unique_info: string                # what's unique about this source
    fetch_status: "full" | "snippet"   # transparency
master_data:
  stats: string[]                      # all stats across all sources, deduplicated
  quotes: string[]                     # all notable quotes
  facts: string[]                      # key facts and features
  timeline: string[]                   # chronological events if applicable
angles:
  - title: string
    primary_source: string
    hook: string
    key_data: string[]
    format_suggestion: string
    unique_value: string
recommended_next_skill: string

Output Format

markdown
## Content Research Brief: [Topic]

📚 **[X] sources collected** | [Y] fully fetched | Freshness: [month]
🏷️ **Top tags:** AI (5), Tools (3), Pricing (2), Comparison (2)

---

### 📰 Sources

| # | Title | Tags | Date | Status |
|---|-------|------|------|--------|
| 1 | [Title](url) | AI, Tools | Mar 2024 | ✅ Full |
| 2 | [Title](url) | Pricing, Comparison | Feb 2024 | ✅ Full |
| 3 | [Title](url) | Trends, Growth | Mar 2024 | ⚠️ Snippet |
| ... | ... | ... | ... | ... |

---

### 📊 Key Data Points (from sources)

**Stats:**
- [Stat 1] — Source: [#1]
- [Stat 2] — Source: [#3]
- [Stat 3] — Source: [#2, #5]

**Quotes:**
- "[Quote]" — [Person], [Role] (Source: [#4])
- "[Quote]" — [Person] (Source: [#2])

**Key Facts:**
- [Fact 1] — mentioned in [X] sources
- [Fact 2] — mentioned in [Y] sources

---

### 🎯 Content Angles (ready to write)

#### Angle 1: "[Title]"
- **Primary source:** [#2] — [title]
- **Hook:** "[Opening line]"
- **Key data:** [stat 1], [stat 2], [quote]
- **Best format:** LinkedIn post
- **Unique value:** [Why this isn't generic]
→ Run: `viral-post-writer` with angle: "[this angle]"

#### Angle 2: "[Title]"
- **Primary source:** [#5] — [title]
- **Hook:** "[Opening line]"
- **Key data:** [stat 3], [fact 1]
- **Best format:** Blog article
- **Unique value:** [Why this is different from Angle 1]
→ Run: `affiliate-blog-builder` with angle: "[this angle]"

#### Angle 3: "[Title]" (Contrarian)
- **Primary source:** [#7] — [title]
- **Hook:** "[Opening line]"
- **Key data:** [counter-stat], [user complaint from Reddit]
- **Best format:** Twitter thread
- **Unique value:** Goes against the dominant narrative — [reasoning]
→ Run: `twitter-thread-writer` with angle: "[this angle]"

---

### 🚀 Next Steps

1. **Pick an angle** and run the suggested content skill
2. **Combine angles** — use `content-pillar-atomizer` to turn one angle into 15+ pieces
3. **Add visuals** — 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.
Show full SKILL.md (469 more words)Show less

Examples

Example 1: User: "Research HeyGen for a LinkedIn post" → topic: "HeyGen AI video", source_types: ["news", "blog", "linkedin"], freshness: "month" → Collect 7 sources: 2 news (HeyGen raises $60M), 3 blog reviews, 2 LinkedIn posts → Tags: AI (7), Funding (2), Tools (5), Comparison (1) → Key stats: "$60M Series A", "40K+ businesses", "Avatar 3.0 launch" → Angles: (1) "HeyGen just raised $60M — 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" → topic: "email marketing trends 2024", source_types: ["news", "blog", "reddit"] → Collect 8 sources covering: AI personalization, interactive emails, privacy changes, deliverability → 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" → topic: "ClickUp", source_types: ["reddit", "blog"], freshness: "month" → 4 Reddit threads (raw opinions), 3 blog reviews → Unique angle: Reddit users love the free tier but hate the learning curve → "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 — API configuration for enhanced search
  • shared/references/flywheel-connections.md — master flywheel connection map
  • shared/references/ftc-compliance.md — source attribution and disclosure requirements
  • shared/references/feedback-protocol.md — issue detection and reporting standard

Flywheel Connections

Feeds Into
  • viral-post-writer (S2) — research brief with angles, data points, and quotes
  • affiliate-blog-builder (S3) — deep research for long-form articles
  • tiktok-script-writer (S2) — key stats and hooks for video scripts
  • twitter-thread-writer (S2) — data-rich thread material
  • reddit-post-writer (S2) — real user opinions for authentic Reddit content
  • content-pillar-atomizer (S2) — research brief as the pillar to atomize
  • infographic-generator (S2) — key stats and data for visual content
  • comparison-post-writer (S3) — multi-source comparison data
  • listicle-generator (S3) — curated sources for listicle content
Fed By
  • trending-content-scout (S1) — trending topics and content gaps to research deeper
  • niche-opportunity-finder (S1) — niche keywords to research
  • content-angle-ranker (S1) — recommended angle to research supporting data
  • competitor-spy (S1) — competitor strategies to research and counter
Feedback Loop
  • S6 performance-report shows which content with research briefs outperforms non-researched content → reinforces research-first workflow
yaml
chain_metadata:
  skill_slug: "content-research-brief"
  stage: "content"
  timestamp: string
  suggested_next:
    - "viral-post-writer"
    - "affiliate-blog-builder"
    - "infographic-generator"
    - "content-pillar-atomizer"

© Affitor, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/content/content-research-brief of Affitor/affiliate-skills.

Open the folder on GitHubat commit e43bfae

Compare with similar skills

Content Research Brief next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Content Research Brief compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Content Research Brief this skillAffitor/affiliate-skills700—~4.1kAutomated safety check: PassMIT
SEO Keyword ClusteringAgriciDaniel/claude-seo19k2 repos~3.3kAutomated safety check: PassMIT
SEO Content Brief GeneratorAgriciDaniel/claude-seo19k2 repos~2.6kAutomated safety check: PassMIT
Strategic SEO PlanningAgriciDaniel/claude-seo19k5 repos~1.1kAutomated safety check: PassMIT
Blog ClusterAgriciDaniel/claude-blog2.3k1 repos~4.9kAutomated safety check: PassMIT
SEO Content Briefseranking/seo-skills160—~2.5kAutomated safety check: PassMIT

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Questions about Content Research Brief

What does Content Research Brief do?

Research trending topics, collect source articles, and generate a structured research brief for content creation. Content Research Brief is an agent skill from Affitor/affiliate-skills. Research trending topics, collect source articles, and generate a structured research brief for content creation.

When should I use Content Research Brief?

Content Research Brief fits situations like: the user wants to research a topic before writing; collect sources for an article; create a research-backed content brief; says research [topic] for me.

How do I install Content Research Brief in Claude Code?

Run `npx skills add Affitor/affiliate-skills --skill content-research-brief -a claude-code`. Or copy the skill folder (skills/content/content-research-brief in Affitor/affiliate-skills) into .claude/skills/content-research-brief in your project. Claude Code loads it when a task matches its description.

How do I install Content Research Brief in Codex?

Run `npx skills add Affitor/affiliate-skills --skill content-research-brief -a codex`. Or copy the skill folder (skills/content/content-research-brief in Affitor/affiliate-skills) into .agents/skills/content-research-brief in your project. Codex loads it when a task matches its description.

Can I use Content Research Brief in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Affitor/affiliate-skills --skill content-research-brief -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/content-research-brief, .gemini/skills/content-research-brief, .github/skills/content-research-brief and .opencode/skills/content-research-brief in your project.

What does Content Research Brief need to run?

SKILL.md names no scripts, command-line tools or credentials: Content Research Brief is instructions for the agent only. Compatibility (from SKILL.md): Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent.

Does Content Research Brief access the network?

SKILL.md names 2 domains. In commands or code: heygen.com; the agent is likely to contact it when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.

Is Content Research Brief safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Content Research Brief use?

Content Research Brief is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Content Research Brief use?

About 4.1k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Content Research Brief?

Skills that share tags, products or a category with Content Research Brief: SEO Keyword Clustering (AgriciDaniel/claude-seo, 19k stars), SEO Content Brief Generator (AgriciDaniel/claude-seo, 19k stars), Strategic SEO Planning (AgriciDaniel/claude-seo, 19k stars) and Blog Cluster (AgriciDaniel/claude-blog, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Content Research Brief?

Affitor (a GitHub organization) maintains it in Affitor/affiliate-skills, which has 700 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on September 15, 2026.

Source: Affitor/affiliate-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.