Analyzes competitor benchmark data and viewer feedback to identify content patterns and strategic opportunities.

Install

mkdir -p .claude/skills/channel-research && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/17454" && unzip -o skill.zip -d .claude/skills/channel-research && rm skill.zip

Installs to .claude/skills/channel-research

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Use when /benchmark と /viewer-voice のデータからタイトル・動画尺・投稿・コメントを含むチャンネル全体を徹底分析するとき。「競合分析」「チャンネルリサーチ」「TTP 対象抽出」で発動。サムネイルだけの深掘りは /thumbnail-research。データ収集・更新は /benchmark(未実行なら先に案内)
173 chars✓ has a “when” trigger
Advanced

Key capabilities

  • Analyze benchmark and viewer voice data
  • Identify TTP patterns for channel strategy
  • Generate channel research reports
  • Create thumbnail text profiles
  • Map viewer desires to content strategies

How it works

The agent delegates data analysis to a subagent to generate a research report and a thumbnail profile based on identified TTP patterns.

Inputs & outputs

You give it
Benchmark and viewer voice data files
You get back
Channel research report and thumbnail text profile

When to use channel-research

  • Conducting competitor analysis
  • Identifying TTP benchmarks for new video series
  • Analyzing viewer comments to refine channel strategy
  • Generating thumbnail design patterns

About channel-research

Reads benchmark and viewer voice data to produce a channel research report. Identifies TTP (Tactics, Techniques, and Procedures) patterns for channel strategy and content direction.

Use when /benchmark と /viewer-voice の TTP ベンチマークデータを徹底分析するとき。「競合分析」「チャンネルリサーチ」「TTP 対象抽出」で発動。データ収集・更新は /benchmark(未実行なら先に案内)

When not to use it

  • When benchmark data is missing
  • When viewer voice data is missing
  • When performing thumbnail-only research

Prerequisites

benchmark_*.jsoncomments_*.jsondocs/benchmarks/*.md

Limitations

  • Requires specific data files in the data directory
  • Does not perform thumbnail-only deep dives
  • Requires individual reports to exist in docs/benchmarks

How it compares

This workflow uses a specific TTP (Tactics, Techniques, and Procedures) methodology to abstract successful patterns rather than copying surface-level elements.

Compared to similar skills

channel-research side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
channel-research (this skill)09dReviewAdvanced
market-sizing-analysis732moNo flagsIntermediate
exploratory-data-analysis151moReviewIntermediate
model-compare77moReviewAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

market-sizing-analysis

wshobson

This skill should be used when the user asks to "calculate TAM", "determine SAM", "estimate SOM", "size the market", "calculate market opportunity", "what's the total addressable market", or requests market sizing analysis for a startup or business opportunity.

73142

exploratory-data-analysis

K-Dense-AI

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

15114

model-compare

rawwerks

Compare 3D CAD models using boolean operations (IoU, Dice, precision/recall). Use when evaluating generated models against gold references, diffing CAD revisions, or computing similarity metrics for ML training. Triggers on: model diff, compare models, IoU, intersection over union, model similarity, CAD comparison, STEP diff, 3D evaluation, gold reference, generated model, precision recall 3D.

783

astropy

davila7

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

682

statistical-analysis

anthropics

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

848

datacommons-client

davila7

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.

637

Search skills

Search the agent skills registry