Slash Command

/cs-market-research

Market research methodology. Size a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single number), plan a survey sample size with finite-population correction and per-segment minimums, and score candidate segments against Kotler's criteria. Outputs always show method + assumptions. Direct invocation of the market-research skill.

Invocation

How this command is triggered — by the user, by Claude, or both

Slash command

/research-ops-skills:cs-market-research <market context: total market value, customer count, price, survey params, candidate segments>

Model invocable

No pre-commands

Context Preview

The summary Claude sees in its command listing — used to decide when to auto-load this command

# /cs:market-research — TAM/SAM/SOM + survey sampling + segmentation

Run the `market-research` skill on this input:

**$ARGUMENTS**

## Three-tool workflow

1. **`market_sizer.py`** — Compute TAM/SAM/SOM by BOTH top-down (total market value × fractions) and bottoms-up (customers × price × adoption) methods side-by-side. Reports divergence and flags failed triangulation. Industry tuning via `--profile`. 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-...

Command Content

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Last CommitJun 10, 2026

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/cs:market-research — TAM/SAM/SOM + survey sampling + segmentation

Run the market-research skill on this input:

$ARGUMENTS

Three-tool workflow

market_sizer.py — Compute TAM/SAM/SOM by BOTH top-down (total market value × fractions) and bottoms-up (customers × price × adoption) methods side-by-side. Reports divergence and flags failed triangulation. Industry tuning via --profile. Never returns a single number.
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).
segmentation_scorer.py — Score candidate segments against Kotler's measurable / substantial / accessible / differentiable / actionable criteria. Enforces a substantiality + accessibility gate; drops demographic slices that are too small or unreachable.

Output

TAM/SAM/SOM both ways + triangulation flag + assumptions
Survey n (overall + per-segment floors)
Segment scores with TARGET / WATCH / DROP verdicts
Top 3 next actions

Hard rule

A market size always travels with its method (both ways) and assumptions — never a single unsourced number.

First run + optimization

Onboard first: python3 skills/market-research/scripts/onboard.py (market profile, survey confidence, margin of error, sizing method) — saved config pre-configures every tool. --show lists the questions.
Optimize (opt-in): only if the user asks to reconcile the sizing/run a loop, hand off to autoresearch via skills/market-research/scripts/ar_evaluator.py (tam_divergence, lower is better).

Distinct from

marketing-skill/campaign-analytics — that measures a live campaign. This is upstream methodology.
marketing-skill/marketing-strategy-pmm — that sets positioning/GTM. This sizes and segments the market.
commercial/pricing-strategist — that sets price. This sizes the market.