yt-research
Research competitor YouTube channels, niches, and trending topics for your content strategy. Use this skill whenever the user says "research channels", "analyze competitors", "find trending topics", "niche analysis", "competitive research", "what are other creators doing", "scrape YouTube channels", or wants to understand the competitive landscape for a specific tool or topic area. Use when working with yt research. Trigger with 'yt', 'research'.
Best use case
yt-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Research competitor YouTube channels, niches, and trending topics for your content strategy. Use this skill whenever the user says "research channels", "analyze competitors", "find trending topics", "niche analysis", "competitive research", "what are other creators doing", "scrape YouTube channels", or wants to understand the competitive landscape for a specific tool or topic area. Use when working with yt research. Trigger with 'yt', 'research'.
Teams using yt-research should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/yt-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How yt-research Compares
| Feature / Agent | yt-research | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Research competitor YouTube channels, niches, and trending topics for your content strategy. Use this skill whenever the user says "research channels", "analyze competitors", "find trending topics", "niche analysis", "competitive research", "what are other creators doing", "scrape YouTube channels", or wants to understand the competitive landscape for a specific tool or topic area. Use when working with yt research. Trigger with 'yt', 'research'.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agent for YouTube Script Writing
Find AI agent skills for YouTube script writing, video research, content outlining, and repeatable channel production workflows.
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
SKILL.md Source
# YouTube Research You are conducting competitive research for a YouTube channel. Your goal is to analyze competitor channels, identify content gaps, discover trending topics, and surface opportunities aligned with the creator's strategy. ## Before You Start You need from the user: 1. **Research focus** - What niche, tool, or topic area to research (e.g., "AI tools for professionals", "MCP integrations", "productivity software") 2. **Competitor channels** (optional) - Specific YouTube channel URLs to analyze 3. **Specific angle** (optional) - Is there a particular feature, update, or trend they want to investigate? If the user provided context already, confirm your understanding and proceed. ## The Research Process ### Step 1: Scope the Research Define the research boundaries: - Which channels to scrape (user-provided + discovered competitors) - Which topics/keywords to search for - Time horizon (recent 30 days, 90 days, or all-time) Tell the user the plan: "I'll analyze [N] channels and search for [keywords]. This will involve web research and data collection." ### Step 2: Collect Channel Data Use web research to collect: - Channel metadata (subscribers, total videos, posting frequency) - Recent videos (last 30-50 per channel): titles, views, likes, comments, publish dates - Video tags and categories where available If Apify MCP is available, spawn `yt-scraper` sub-agent for bulk data collection. ### Step 3: Analyze Channels For each channel, analyze: - Engagement pattern analysis (what gets views vs what doesn't) - Content type distribution (tutorials, reviews, updates, opinions) - Title pattern analysis (what structures and words correlate with views) - Outlier video identification (3x+ above channel average) - Topic coverage map (what's covered, what's missing) If analyzing 4+ channels, spawn `channel-analyzer` sub-agents (3 channels per agent) for parallel processing. ### Step 4: Identify Opportunities Using the analysis results, identify: **Content Gaps:** - Topics the audience searches for but competitors cover poorly - Topics that are developer-focused everywhere but could be made accessible - Recent tool updates/features with no quality coverage yet **Trending Signals:** - Tools/features getting increasing search interest - Topics with recent outlier videos (sudden view spikes) - Community discussions (Reddit, forums) indicating unmet demand **Strategic Fit:** - Which opportunities align with the creator's content pillars? - Which serve the target audience? - Which have the best effort-to-impact ratio? ### Step 5: Export Results Generate two outputs: 1. **`niche-analysis.json`** - Structured data with per-channel metrics, outlier videos, content gaps, and opportunity scores 2. **`niche-report.md`** - Human-readable research report with: - Executive summary (3-5 key findings) - Per-channel analysis highlights - Top 10 content opportunities ranked by potential - Recommended next steps Present the report to the user: "Here's the research report. Key findings:" - [Top 3 findings] "What would you like to do?" - Move to ideation with these insights - Research additional channels - Dig deeper into a specific finding - Export and save for later ## Key Principles - **Strategy-first** - Every finding must connect back to the creator's goals and audience. Don't surface opportunities that don't serve the target audience. - **Data over opinion** - Ground insights in actual view counts, engagement rates, and search data. "This seems popular" is useless. "This video got 3.2x the channel average with 45K views in 2 weeks" is useful. - **Actionable outputs** - Every content gap should translate directly into a potential video idea. Don't just say "competitors don't cover X" - say "competitors don't cover X, and here's evidence that people are searching for it." - **Respect rate limits** - When using APIs, handle timeouts gracefully and never hammer endpoints. - **Save everything to disk** - Persist all collected data and analysis results as JSON files immediately. Never hold large datasets only in conversation context. ## Overview Research competitor YouTube channels, niches, and trending topics for your content strategy. ## Prerequisites - Access to the content environment or API - Required CLI tools installed and authenticated - Familiarity with content concepts and terminology ## Instructions 1. Assess the current state of the content configuration 2. Identify the specific requirements and constraints 3. Apply the recommended patterns from this skill 4. Validate the changes against expected behavior 5. Document the configuration for team reference ## Output - Configuration files or code changes applied to the project - Validation report confirming correct implementation - Summary of changes made and their rationale ## Error Handling | Error | Cause | Resolution | |-------|-------|------------| | Authentication failure | Invalid or expired credentials | Refresh tokens or re-authenticate with content | | Configuration conflict | Incompatible settings detected | Review and resolve conflicting parameters | | Resource not found | Referenced resource missing | Verify resource exists and permissions are correct | ## Examples **Basic usage**: Apply yt research to a standard project setup with default configuration options. **Advanced scenario**: Customize yt research for production environments with multiple constraints and team-specific requirements. ## Resources - Official content documentation - Community best practices and patterns - Related skills in this plugin pack
Related Skills
Research Proposal Generator
Generate high-quality academic research proposals for PhD applications following Nature Reviews-style academic writing conventions.
creating-github-issues-from-web-research
This skill enhances Claude's ability to conduct web research and translate findings into actionable GitHub issues. It automates the process of extracting key information from web search results and formatting it into a well-structured issue, ready for team action. Use this skill when you need to research a topic and create a corresponding GitHub issue for tracking, collaboration, and task management. Trigger this skill by requesting Claude to "research [topic] and create a ticket" or "find [information] and generate a GitHub issue".
persona-researcher
Organize research — manage references, notes, and collaboration.
Autoresearch
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.
apify-market-research
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
../../../engineering/autoresearch-agent/skills/status/SKILL.md
No description provided.
../../../engineering/autoresearch-agent/skills/run/SKILL.md
No description provided.
../../../engineering/autoresearch-agent/skills/resume/SKILL.md
No description provided.
../../../product-team/research-summarizer/SKILL.md
No description provided.
../../../engineering/autoresearch-agent/skills/loop/SKILL.md
No description provided.
autoresearch-agent
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.
content-trend-researcher
Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis