market-analysis-guide
Structured frameworks for market sizing, competitive analysis, and strategic ...
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill market-analysis-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Market Analysis Guide A comprehensive skill for conducting rigorous market analysis in academic and applied research contexts. This guide covers quantitative market sizing, competitive landscape mapping, and strategic positioning frameworks grounded in peer-reviewed methodologies. ## Market Sizing Methodologies Market sizing is the foundation of any credible market analysis. There are two primary approaches, and robust research typically employs both for triangulation. **Top-Down Approach (TAM/SAM/SOM)** Start with the total addressable market and narrow systematically: ``` TAM (Total Addressable Market) -> SAM (Serviceable Available Market) -> SOM (Serviceable Obtainable Market) Example calculation: TAM = Global higher-education EdTech spend = $340B (2025, HolonIQ) SAM = AI-powered research tools segment = $12B SOM = Realistic capture in Year 3 = $120M (1% of SAM) ``` **Bottom-Up Approach** Build estimates from unit economics: ```python # Bottom-up market sizing users_in_target_segment = 8_000_000 # global PhD + postdoc researchers adoption_rate = 0.05 # 5% in first 3 years avg_revenue_per_user = 180 # USD/year bottom_up_estimate = users_in_target_segment * adoption_rate * avg_r
- Market Sizing Methodologies
- Competitive Analysis Frameworks
- Porter's Five Forces
- SWOT and TOWS Matrix
- Data Collection and Validation
- Reporting and Visualization
What does the market-analysis-guide skill do?
Structured frameworks for market sizing, competitive analysis, and strategic ...
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill market-analysis-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.