Market research
Community-maintained distribution of reusable AI coding agents, commands, skills, hooks, and cross-harness workflows.
npx -y skills add loulanyue/awesome-claude-notes --skill market-researchAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
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Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
SKILL.md
2.2 KB, 391 tokens by cl100k_base, as published. Nobody here has run it
Market Research
Produce research that supports decisions, not research theater.
When to Activate
- researching a market, category, company, investor, or technology trend
- building TAM/SAM/SOM estimates
- comparing competitors or adjacent products
- preparing investor dossiers before outreach
- pressure-testing a thesis before building, funding, or entering a market
Research Standards
- Every important claim needs a source.
- Prefer recent data and call out stale data.
- Include contrarian evidence and downside cases.
- Translate findings into a decision, not just a summary.
- Separate fact, inference, and recommendation clearly.
Common Research Modes
Investor / Fund Diligence
Collect:
- fund size, stage, and typical check size
- relevant portfolio companies
- public thesis and recent activity
- reasons the fund is or is not a fit
- any obvious red flags or mismatches
Competitive Analysis
Collect:
- product reality, not marketing copy
- funding and investor history if public
- traction metrics if public
- distribution and pricing clues
- strengths, weaknesses, and positioning gaps
Market Sizing
Use:
- top-down estimates from reports or public datasets
- bottom-up sanity checks from realistic customer acquisition assumptions
- explicit assumptions for every leap in logic
Technology / Vendor Research
Collect:
- how it works
- trade-offs and adoption signals
- integration complexity
- lock-in, security, compliance, and operational risk
Output Format
Default structure:
- executive summary
- key findings
- implications
- risks and caveats
- recommendation
- sources
Quality Gate
Before delivering:
- all numbers are sourced or labeled as estimates
- old data is flagged
- the recommendation follows from the evidence
- risks and counterarguments are included
- the output makes a decision easier
Gives 1 of the 12 instructions most research analysis skills give in 391 tokens
Counted across 1,063 of the 1,754 authors here whose files we hold, read 2026-08-07
- generate a markdown reportin 32 of 1063, across 23 files
- cite each claim's sourcehere, and in 30 of 1063, across 15 files
- define the ideal customer profilein 20 of 1063, across 2 files
- search for companies matching the criteriain 20 of 1063, across 2 files
- assign a fit score from one to tenin 20 of 1063, across 2 files
- analyze the codebase to understand the productin 19 of 1063, across 1 file
- ask clarifying questions about the value propositionin 19 of 1063, across 1 file
- look for signals of immediate needin 19 of 1063, across 1 file
- identify the target decision maker rolein 19 of 1063, across 1 file
- suggest a personalized contact strategyin 19 of 1063, across 1 file
- provide conversation starters for outreachin 19 of 1063, across 1 file
- format results in a scannable markdown templatein 19 of 1063, across 1 file
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.