market-research
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this
Profile →npx skills add alirezarezvani/claude-skills --skill market-research --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-research Upstream market-research methodology: market sizing, survey/sampling design, and segmentation. The discipline here is **method + assumptions**: a TAM is never a single number, a survey is never powered only in aggregate, and a segment is never a demographic slice. ## Purpose Market-research analysts, product marketers, and strategy teams need rigorous evidence *before* anyone optimizes a campaign or sets a strategy. This skill structures three methodology decisions: Three deterministic tools: 1. `market_sizer.py` — Computes TAM/SAM/SOM by **both** top-down and bottoms-up methods side-by-side, reports the divergence, and flags failed triangulation. Never returns a single number. 2. `sample_size_planner.py` — Survey sample size from confidence, margin of error, and expected proportion, with the finite-population correction and **per-segment minimums** (a survey powered overall is not powered per reported segment). 3. `segmentation_scorer.py` — Scores candidate segments against Kotler's five criteria and enforces a substantiality + accessibility gate; a slice that is too small or unreachable is dropped. ## When to use Invoke this skill when: - A board or exec asks "h
- Purpose
- When to use
- Workflow
- Scripts
- Onboarding & customization
- Optimize with autoresearch (opt-in)
- References
- Assumptions
- Anti-patterns
- Distinct from
- Quick examples
- Forcing-question library (Matt Pocock grill discipline)
python3 scripts/onboard.py # interactive (also: --defaults, --set key=value, --reset) python3 scripts/onboard.py --show # see the questions + current effective config python3 scripts/market_sizer.py --sample python3 scripts/sample_size_planner.py --population 62000 --confidence 0.95 --moe 0.05 python3 scripts/segmentation_scorer.py --sample --output json
What does the market-research skill do?
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill market-research --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 alirezarezvani/claude-skills, a repository with 23,791 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.