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claude-skills/docs/commands/cs-market-research.md
Claude 82c5aea9f0
Merge origin/dev: reconcile docs redesign with upstream skill changes 
- Resolve conflicts: keep redesigned skills index, take dev's cs-aeo link
  fix, union of DOMAIN_SEO_CONTEXT entries in generate-docs.py
- Regenerate catalog on the merged tree (dev's agent/command description
  updates, removed ai-seo/release-manager/command-guide, restructured
  universal-scraping-architect)
- Update counters to post-merge truth from scripts/derive_counters.py:
  345 skills, 78 plugins (14 bundles + 64 standalone), 570+ Python tools
- Add redirects for upstream-removed pages (ai-seo -> aeo,
  release-manager -> changelog-generator, command-guide -> engineering index)
- Add compliance-os bundle to bundle tables; rebuild 78-plugin table from
  marketplace.json
- Teach the generator to rewrite repo-root-relative source links to GitHub
  URLs — mkdocs build --strict now passes with zero warnings

https://claude.ai/code/session_015bYZ97nV4oRb3LbxCRFVcP
2026-06-11 15:52:42 +00:00

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title description
/cs-market-research — Slash Command for AI Coding Agents 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. Slash command for Claude Code, Codex CLI, Gemini CLI.

/cs-market-research

:material-console: Slash Command :material-github: Source

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-segment minimums (a survey powered overall is not powered per reported segment).

  3. 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.