market-research

# 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 "how big is this market?" and you need a defensible, triangulated answer.
- You are fielding a survey and need a sample size that holds up per segment, not just overall.
- You have a list of candidate segments and need to know which are real markets vs demographic slices.
- You are synthesizing competitive intelligence and need a methodological backbone.

**Do NOT use this skill to**: measure a live campaign (attribution, ROAS, CPA → `marketing-skill/campaign-analytics`), build demand-gen / paid-media plans (`marketing-skill/marketing-demand-acquisition`), set positioning / GTM strategy (`marketing-skill/marketing-strategy-pmm`), or set pricing (`commercial/pricing-strategist`).

## Workflow

1. **Write the brief** — Fill `assets/market_research_brief_template.md` (objective, the decision this informs, sizing approach, sampling plan, assumptions register).
2. **Size the market** — Run `market_sizer.py --input market.json --method both --profile {b2b-saas|consumer|enterprise|marketplace|hardware|services}`. Reconcile the top-down/bottoms-up delta before quoting anything.
3. **Plan the survey** — Run `sample_size_planner.py --input survey.json`. Fund the per-segment floors, not just the overall n.
4. **Score the segments** — Run `segmentation_scorer.py --input segments.json --profile <same>`. Drop segments failing the substantiality/accessibility gate.
5. **Assemble the evidence pack** — Combine into a brief. Every number carries its method + assumptions + confidence.

## Scripts

| Script | Purpose | Profiles |
|---|---|---|
| `scripts/market_sizer.py` | TAM/SAM/SOM top-down AND bottoms-up + triangulation flag | b2b-saas, consumer, enterprise, marketplace, hardware, services |
| `scripts/sample_size_planner.py` | Survey n + FPC + per-segment minima | n/a (parameter-driven) |
| `scripts/segmentation_scorer.py` | Kotler 5-criteria scoring + gate | b2b-saas, consumer, enterprise, marketplace, hardware, services |

All three: stdlib-only, `--help`, `--sample`, `--output {human,json}`.

## Onboarding & customization

Run the onboarding questionnaire **once before you start** — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior.

```bash
python3 scripts/onboard.py            # interactive (also: --defaults, --set key=value, --reset)
python3 scripts/onboard.py --show     # see the questions + current effective config
```

Answers are saved to `~/.config/research-ops/market-research.json` (global) or `./.research-ops/market-research.json` (`--scope project`) and are read automatically by `config_loader.py`. They set the default market **profile**, the default survey **confidence** and **margin of error**, and the default **sizing method**. CLI flags always override saved config; `RESEARCH_OPS_NO_CONFIG=1` ignores it.

**The four questions:** market profile · survey confidence · margin of error · sizing method.

## Optimize with autoresearch (opt-in)

This skill ships an **isolated, opt-in** bridge to `engineering/autoresearch-agent`. Only when you ask to "optimize" / "reconcile the sizing" / "run a loop" does an autoresearch experiment iteratively reconcile your market model so top-down and bottoms-up triangulate. `scripts/ar_evaluator.py` is the ground-truth evaluator; it prints `tam_divergence: <fraction>` (**lower** is better).

```bash
/ar:setup --domain custom --name tam-triangulation \
  --target market.json \
  --eval "python3 ar_evaluator.py --target market.json" \
  --metric tam_divergence --direction lower
/ar:loop custom/tam-triangulation
```

Isolated: no hard dependency — autoresearch runs only on demand, and the loop edits `market.json`, never the evaluator.

## References

- `references/market_sizing_canon.md` — TAM/SAM/SOM frameworks (Bessemer, a16z); top-down vs bottoms-up; Fermi estimation; market-model conventions; common sizing fallacies.
- `references/survey_methodology.md` — Cochran *Sampling Techniques*; Dillman *Tailored Design Method*; Groves *Survey Methodology*; question-wording bias (Schuman & Presser); AAPOR standards.
- `references/segmentation_and_ci.md` — Kotler segmentation criteria; needs-based vs firmographic; Porter Five Forces; SCIP ethics; Christensen JTBD; conjoint/MaxDiff primer.

## Assumptions

- The sizer reports both methods but cannot validate your inputs — a top-down "1% of a $40B market" is only as good as the cited source and the serviceable fraction.
- Sample-size uses the conservative p=0.5 (maximum variance) unless you supply an expected proportion.
- Segment scores are inputs you provide; the tool enforces the gates and the weighting, it does not gather the underlying evidence.
- Competitive intelligence must follow the SCIP code of ethics — no misrepresentation, no protected information.

## Anti-patterns

- **A single TAM number with no method.** Always triangulate top-down against bottoms-up.
- **Spurious precision.** Size to the decision's tolerance; "$3.7142B" implies a confidence you do not have.
- **Powering only the total.** Each reported segment needs its own sample floor.
- **Leading or double-barreled survey questions.** Pre-test wording against the bias literature.
- **Calling a demographic slice a segment.** It must be substantial AND accessible.

## Distinct from

| Neighbor | Scope | Difference |
|---|---|---|
| `marketing-skill/campaign-analytics` | Attribution, ROAS, CPA, funnel of a live campaign | That **measures spend deployed**; this is **upstream methodology** |
| `marketing-skill/marketing-demand-acquisition` | Demand-gen, paid media, channel mix | That **runs acquisition**; this **builds the evidence** |
| `marketing-skill/marketing-strategy-pmm` | Positioning, GTM, category | That **sets strategy**; this **sizes and segments the market** |
| `commercial/pricing-strategist` | Pricing model + WTP + packaging | That **sets price**; this **sizes the market** |
| `product-research` (sibling) | User/product discovery methods | That studies **users**; this studies **the market** |

## Quick examples

```bash
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
```

The sample market triangulates a ~$1.47B top-down SAM against the bottoms-up figure and flags the divergence; the segmentation sample drops the "solopreneurs who might want analytics" slice for failing the substantiality and accessibility gates.

## Forcing-question library (Matt Pocock grill discipline)

Walked one at a time by `/cs-grill-research-ops` or the orchestrator. Recommended answer + canon citation per question. Never bundled.

1. **"Is your TAM top-down or bottoms-up — and have you computed it both ways to triangulate?"**
   Recommended: both; reconcile the delta before quoting a number.
   Canon: Bessemer / a16z market-sizing; Fermi estimation.

2. **"What decision will this market size actually drive — and at what precision does it matter?"**
   Recommended: size to the decision's tolerance, not to a spurious-precision number.
   Canon: market-model conventions (Gartner/Forrester); decision-driven analysis.

3. **"What's your target margin of error and confidence — and does your sample clear it per segment, not just overall?"**
   Recommended: power each reported segment, not only the total.
   Canon: Cochran *Sampling Techniques*; AAPOR standards.

4. **"Are your survey questions free of leading and double-barreled wording?"**
   Recommended: pre-test the wording; cite the bias source.
   Canon: Schuman & Presser; Dillman *Tailored Design Method*.

5. **"Do your segments pass measurable / substantial / accessible / actionable — or are they just demographic slices?"**
   Recommended: drop segments that fail substantiality or accessibility.
   Canon: Kotler segmentation criteria.

Walk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke `market_sizer.py` → `sample_size_planner.py` → `segmentation_scorer.py`.

{
  "format": "aiskill/v1",
  "skill": {
    "name": "market-research",
    "slug": "market-research",
    "version": "2.9.0",
    "description": "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 is the evidence-building methodology, not live-campaign optimization.",
    "category": "Business",
    "tags": [
      "product-business",
      "research"
    ],
    "platforms": [
      "chatgpt",
      "claude-code",
      "cursor"
    ],
    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT"
  },
  "prompts": {
    "system": "# market-research\r\n\r\nUpstream 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.\r\n\r\n## Purpose\r\n\r\nMarket-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:\r\n\r\nThree deterministic tools:\r\n\r\n1. `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.\r\n2. `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).\r\n3. `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.\r\n\r\n## When to use\r\n\r\nInvoke this skill when:\r\n\r\n- A board or exec asks \"how big is this market?\" and you need a defensible, triangulated answer.\r\n- You are fielding a survey and need a sample size that holds up per segment, not just overall.\r\n- You have a list of candidate segments and need to know which are real markets vs demographic slices.\r\n- You are synthesizing competitive intelligence and need a methodological backbone.\r\n\r\n**Do NOT use this skill to**: measure a live campaign (attribution, ROAS, CPA → `marketing-skill/campaign-analytics`), build demand-gen / paid-media plans (`marketing-skill/marketing-demand-acquisition`), set positioning / GTM strategy (`marketing-skill/marketing-strategy-pmm`), or set pricing (`commercial/pricing-strategist`).\r\n\r\n## Workflow\r\n\r\n1. **Write the brief** — Fill `assets/market_research_brief_template.md` (objective, the decision this informs, sizing approach, sampling plan, assumptions register).\r\n2. **Size the market** — Run `market_sizer.py --input market.json --method both --profile {b2b-saas|consumer|enterprise|marketplace|hardware|services}`. Reconcile the top-down/bottoms-up delta before quoting anything.\r\n3. **Plan the survey** — Run `sample_size_planner.py --input survey.json`. Fund the per-segment floors, not just the overall n.\r\n4. **Score the segments** — Run `segmentation_scorer.py --input segments.json --profile <same>`. Drop segments failing the substantiality/accessibility gate.\r\n5. **Assemble the evidence pack** — Combine into a brief. Every number carries its method + assumptions + confidence.\r\n\r\n## Scripts\r\n\r\n| Script | Purpose | Profiles |\r\n|---|---|---|\r\n| `scripts/market_sizer.py` | TAM/SAM/SOM top-down AND bottoms-up + triangulation flag | b2b-saas, consumer, enterprise, marketplace, hardware, services |\r\n| `scripts/sample_size_planner.py` | Survey n + FPC + per-segment minima | n/a (parameter-driven) |\r\n| `scripts/segmentation_scorer.py` | Kotler 5-criteria scoring + gate | b2b-saas, consumer, enterprise, marketplace, hardware, services |\r\n\r\nAll three: stdlib-only, `--help`, `--sample`, `--output {human,json}`.\r\n\r\n## Onboarding & customization\r\n\r\nRun the onboarding questionnaire **once before you start** — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior.\r\n\r\n```bash\r\npython3 scripts/onboard.py            # interactive (also: --defaults, --set key=value, --reset)\r\npython3 scripts/onboard.py --show     # see the questions + current effective config\r\n```\r\n\r\nAnswers are saved to `~/.config/research-ops/market-research.json` (global) or `./.research-ops/market-research.json` (`--scope project`) and are read automatically by `config_loader.py`. They set the default market **profile**, the default survey **confidence** and **margin of error**, and the default **sizing method**. CLI flags always override saved config; `RESEARCH_OPS_NO_CONFIG=1` ignores it.\r\n\r\n**The four questions:** market profile · survey confidence · margin of error · sizing method.\r\n\r\n## Optimize with autoresearch (opt-in)\r\n\r\nThis skill ships an **isolated, opt-in** bridge to `engineering/autoresearch-agent`. Only when you ask to \"optimize\" / \"reconcile the sizing\" / \"run a loop\" does an autoresearch experiment iteratively reconcile your market model so top-down and bottoms-up triangulate. `scripts/ar_evaluator.py` is the ground-truth evaluator; it prints `tam_divergence: <fraction>` (**lower** is better).\r\n\r\n```bash\r\n/ar:setup --domain custom --name tam-triangulation \\\r\n  --target market.json \\\r\n  --eval \"python3 ar_evaluator.py --target market.json\" \\\r\n  --metric tam_divergence --direction lower\r\n/ar:loop custom/tam-triangulation\r\n```\r\n\r\nIsolated: no hard dependency — autoresearch runs only on demand, and the loop edits `market.json`, never the evaluator.\r\n\r\n## References\r\n\r\n- `references/market_sizing_canon.md` — TAM/SAM/SOM frameworks (Bessemer, a16z); top-down vs bottoms-up; Fermi estimation; market-model conventions; common sizing fallacies.\r\n- `references/survey_methodology.md` — Cochran *Sampling Techniques*; Dillman *Tailored Design Method*; Groves *Survey Methodology*; question-wording bias (Schuman & Presser); AAPOR standards.\r\n- `references/segmentation_and_ci.md` — Kotler segmentation criteria; needs-based vs firmographic; Porter Five Forces; SCIP ethics; Christensen JTBD; conjoint/MaxDiff primer.\r\n\r\n## Assumptions\r\n\r\n- The sizer reports both methods but cannot validate your inputs — a top-down \"1% of a $40B market\" is only as good as the cited source and the serviceable fraction.\r\n- Sample-size uses the conservative p=0.5 (maximum variance) unless you supply an expected proportion.\r\n- Segment scores are inputs you provide; the tool enforces the gates and the weighting, it does not gather the underlying evidence.\r\n- Competitive intelligence must follow the SCIP code of ethics — no misrepresentation, no protected information.\r\n\r\n## Anti-patterns\r\n\r\n- **A single TAM number with no method.** Always triangulate top-down against bottoms-up.\r\n- **Spurious precision.** Size to the decision's tolerance; \"$3.7142B\" implies a confidence you do not have.\r\n- **Powering only the total.** Each reported segment needs its own sample floor.\r\n- **Leading or double-barreled survey questions.** Pre-test wording against the bias literature.\r\n- **Calling a demographic slice a segment.** It must be substantial AND accessible.\r\n\r\n## Distinct from\r\n\r\n| Neighbor | Scope | Difference |\r\n|---|---|---|\r\n| `marketing-skill/campaign-analytics` | Attribution, ROAS, CPA, funnel of a live campaign | That **measures spend deployed**; this is **upstream methodology** |\r\n| `marketing-skill/marketing-demand-acquisition` | Demand-gen, paid media, channel mix | That **runs acquisition**; this **builds the evidence** |\r\n| `marketing-skill/marketing-strategy-pmm` | Positioning, GTM, category | That **sets strategy**; this **sizes and segments the market** |\r\n| `commercial/pricing-strategist` | Pricing model + WTP + packaging | That **sets price**; this **sizes the market** |\r\n| `product-research` (sibling) | User/product discovery methods | That studies **users**; this studies **the market** |\r\n\r\n## Quick examples\r\n\r\n```bash\r\npython3 scripts/market_sizer.py --sample\r\npython3 scripts/sample_size_planner.py --population 62000 --confidence 0.95 --moe 0.05\r\npython3 scripts/segmentation_scorer.py --sample --output json\r\n```\r\n\r\nThe sample market triangulates a ~$1.47B top-down SAM against the bottoms-up figure and flags the divergence; the segmentation sample drops the \"solopreneurs who might want analytics\" slice for failing the substantiality and accessibility gates.\r\n\r\n## Forcing-question library (Matt Pocock grill discipline)\r\n\r\nWalked one at a time by `/cs-grill-research-ops` or the orchestrator. Recommended answer + canon citation per question. Never bundled.\r\n\r\n1. **\"Is your TAM top-down or bottoms-up — and have you computed it both ways to triangulate?\"**\r\n   Recommended: both; reconcile the delta before quoting a number.\r\n   Canon: Bessemer / a16z market-sizing; Fermi estimation.\r\n\r\n2. **\"What decision will this market size actually drive — and at what precision does it matter?\"**\r\n   Recommended: size to the decision's tolerance, not to a spurious-precision number.\r\n   Canon: market-model conventions (Gartner/Forrester); decision-driven analysis.\r\n\r\n3. **\"What's your target margin of error and confidence — and does your sample clear it per segment, not just overall?\"**\r\n   Recommended: power each reported segment, not only the total.\r\n   Canon: Cochran *Sampling Techniques*; AAPOR standards.\r\n\r\n4. **\"Are your survey questions free of leading and double-barreled wording?\"**\r\n   Recommended: pre-test the wording; cite the bias source.\r\n   Canon: Schuman & Presser; Dillman *Tailored Design Method*.\r\n\r\n5. **\"Do your segments pass measurable / substantial / accessible / actionable — or are they just demographic slices?\"**\r\n   Recommended: drop segments that fail substantiality or accessibility.\r\n   Canon: Kotler segmentation criteria.\r\n\r\nWalk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke `market_sizer.py` → `sample_size_planner.py` → `segmentation_scorer.py`.",
    "user_template": ""
  },
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  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The market-research/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
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        "title": "Use it",
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  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/market-research",
    "exported_at": "2026-11-03T07:44:25.669Z"
  }
}

format: aiskill/v1
skill:
  name: market-research
  slug: market-research
  version: 2.9.0
  description: >-
    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 is the evidence-building methodology, not live-campaign
    optimization.
  category: Business
  tags:
    - product-business
    - research
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT
prompts:
  system: "# market-research\r\n\r\nUpstream 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.\r\n\r\n## Purpose\r\n\r\nMarket-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:\r\n\r\nThree deterministic tools:\r\n\r\n1. `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.\r\n2. `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).\r\n3. `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.\r\n\r\n## When to use\r\n\r\nInvoke this skill when:\r\n\r\n- A board or exec asks \"how big is this market?\" and you need a defensible, triangulated answer.\r\n- You are fielding a survey and need a sample size that holds up per segment, not just overall.\r\n- You have a list of candidate segments and need to know which are real markets vs demographic slices.\r\n- You are synthesizing competitive intelligence and need a methodological backbone.\r\n\r\n**Do NOT use this skill to**: measure a live campaign (attribution, ROAS, CPA → `marketing-skill/campaign-analytics`), build demand-gen / paid-media plans (`marketing-skill/marketing-demand-acquisition`), set positioning / GTM strategy (`marketing-skill/marketing-strategy-pmm`), or set pricing (`commercial/pricing-strategist`).\r\n\r\n## Workflow\r\n\r\n1. **Write the brief** — Fill `assets/market_research_brief_template.md` (objective, the decision this informs, sizing approach, sampling plan, assumptions register).\r\n2. **Size the market** — Run `market_sizer.py --input market.json --method both --profile {b2b-saas|consumer|enterprise|marketplace|hardware|services}`. Reconcile the top-down/bottoms-up delta before quoting anything.\r\n3. **Plan the survey** — Run `sample_size_planner.py --input survey.json`. Fund the per-segment floors, not just the overall n.\r\n4. **Score the segments** — Run `segmentation_scorer.py --input segments.json --profile <same>`. Drop segments failing the substantiality/accessibility gate.\r\n5. **Assemble the evidence pack** — Combine into a brief. Every number carries its method + assumptions + confidence.\r\n\r\n## Scripts\r\n\r\n| Script | Purpose | Profiles |\r\n|---|---|---|\r\n| `scripts/market_sizer.py` | TAM/SAM/SOM top-down AND bottoms-up + triangulation flag | b2b-saas, consumer, enterprise, marketplace, hardware, services |\r\n| `scripts/sample_size_planner.py` | Survey n + FPC + per-segment minima | n/a (parameter-driven) |\r\n| `scripts/segmentation_scorer.py` | Kotler 5-criteria scoring + gate | b2b-saas, consumer, enterprise, marketplace, hardware, services |\r\n\r\nAll three: stdlib-only, `--help`, `--sample`, `--output {human,json}`.\r\n\r\n## Onboarding & customization\r\n\r\nRun the onboarding questionnaire **once before you start** — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior.\r\n\r\n```bash\r\npython3 scripts/onboard.py            # interactive (also: --defaults, --set key=value, --reset)\r\npython3 scripts/onboard.py --show     # see the questions + current effective config\r\n```\r\n\r\nAnswers are saved to `~/.config/research-ops/market-research.json` (global) or `./.research-ops/market-research.json` (`--scope project`) and are read automatically by `config_loader.py`. They set the default market **profile**, the default survey **confidence** and **margin of error**, and the default **sizing method**. CLI flags always override saved config; `RESEARCH_OPS_NO_CONFIG=1` ignores it.\r\n\r\n**The four questions:** market profile · survey confidence · margin of error · sizing method.\r\n\r\n## Optimize with autoresearch (opt-in)\r\n\r\nThis skill ships an **isolated, opt-in** bridge to `engineering/autoresearch-agent`. Only when you ask to \"optimize\" / \"reconcile the sizing\" / \"run a loop\" does an autoresearch experiment iteratively reconcile your market model so top-down and bottoms-up triangulate. `scripts/ar_evaluator.py` is the ground-truth evaluator; it prints `tam_divergence: <fraction>` (**lower** is better).\r\n\r\n```bash\r\n/ar:setup --domain custom --name tam-triangulation \\\r\n  --target market.json \\\r\n  --eval \"python3 ar_evaluator.py --target market.json\" \\\r\n  --metric tam_divergence --direction lower\r\n/ar:loop custom/tam-triangulation\r\n```\r\n\r\nIsolated: no hard dependency — autoresearch runs only on demand, and the loop edits `market.json`, never the evaluator.\r\n\r\n## References\r\n\r\n- `references/market_sizing_canon.md` — TAM/SAM/SOM frameworks (Bessemer, a16z); top-down vs bottoms-up; Fermi estimation; market-model conventions; common sizing fallacies.\r\n- `references/survey_methodology.md` — Cochran *Sampling Techniques*; Dillman *Tailored Design Method*; Groves *Survey Methodology*; question-wording bias (Schuman & Presser); AAPOR standards.\r\n- `references/segmentation_and_ci.md` — Kotler segmentation criteria; needs-based vs firmographic; Porter Five Forces; SCIP ethics; Christensen JTBD; conjoint/MaxDiff primer.\r\n\r\n## Assumptions\r\n\r\n- The sizer reports both methods but cannot validate your inputs — a top-down \"1% of a $40B market\" is only as good as the cited source and the serviceable fraction.\r\n- Sample-size uses the conservative p=0.5 (maximum variance) unless you supply an expected proportion.\r\n- Segment scores are inputs you provide; the tool enforces the gates and the weighting, it does not gather the underlying evidence.\r\n- Competitive intelligence must follow the SCIP code of ethics — no misrepresentation, no protected information.\r\n\r\n## Anti-patterns\r\n\r\n- **A single TAM number with no method.** Always triangulate top-down against bottoms-up.\r\n- **Spurious precision.** Size to the decision's tolerance; \"$3.7142B\" implies a confidence you do not have.\r\n- **Powering only the total.** Each reported segment needs its own sample floor.\r\n- **Leading or double-barreled survey questions.** Pre-test wording against the bias literature.\r\n- **Calling a demographic slice a segment.** It must be substantial AND accessible.\r\n\r\n## Distinct from\r\n\r\n| Neighbor | Scope | Difference |\r\n|---|---|---|\r\n| `marketing-skill/campaign-analytics` | Attribution, ROAS, CPA, funnel of a live campaign | That **measures spend deployed**; this is **upstream methodology** |\r\n| `marketing-skill/marketing-demand-acquisition` | Demand-gen, paid media, channel mix | That **runs acquisition**; this **builds the evidence** |\r\n| `marketing-skill/marketing-strategy-pmm` | Positioning, GTM, category | That **sets strategy**; this **sizes and segments the market** |\r\n| `commercial/pricing-strategist` | Pricing model + WTP + packaging | That **sets price**; this **sizes the market** |\r\n| `product-research` (sibling) | User/product discovery methods | That studies **users**; this studies **the market** |\r\n\r\n## Quick examples\r\n\r\n```bash\r\npython3 scripts/market_sizer.py --sample\r\npython3 scripts/sample_size_planner.py --population 62000 --confidence 0.95 --moe 0.05\r\npython3 scripts/segmentation_scorer.py --sample --output json\r\n```\r\n\r\nThe sample market triangulates a ~$1.47B top-down SAM against the bottoms-up figure and flags the divergence; the segmentation sample drops the \"solopreneurs who might want analytics\" slice for failing the substantiality and accessibility gates.\r\n\r\n## Forcing-question library (Matt Pocock grill discipline)\r\n\r\nWalked one at a time by `/cs-grill-research-ops` or the orchestrator. Recommended answer + canon citation per question. Never bundled.\r\n\r\n1. **\"Is your TAM top-down or bottoms-up — and have you computed it both ways to triangulate?\"**\r\n   Recommended: both; reconcile the delta before quoting a number.\r\n   Canon: Bessemer / a16z market-sizing; Fermi estimation.\r\n\r\n2. **\"What decision will this market size actually drive — and at what precision does it matter?\"**\r\n   Recommended: size to the decision's tolerance, not to a spurious-precision number.\r\n   Canon: market-model conventions (Gartner/Forrester); decision-driven analysis.\r\n\r\n3. **\"What's your target margin of error and confidence — and does your sample clear it per segment, not just overall?\"**\r\n   Recommended: power each reported segment, not only the total.\r\n   Canon: Cochran *Sampling Techniques*; AAPOR standards.\r\n\r\n4. **\"Are your survey questions free of leading and double-barreled wording?\"**\r\n   Recommended: pre-test the wording; cite the bias source.\r\n   Canon: Schuman & Presser; Dillman *Tailored Design Method*.\r\n\r\n5. **\"Do your segments pass measurable / substantial / accessible / actionable — or are they just demographic slices?\"**\r\n   Recommended: drop segments that fail substantiality or accessibility.\r\n   Canon: Kotler segmentation criteria.\r\n\r\nWalk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke `market_sizer.py` → `sample_size_planner.py` → `segmentation_scorer.py`."
  user_template: ""
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The market-research/ folder contains
        SKILL.md plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/market-research
  exported_at: "2026-11-03T07:44:25.669Z"