Market Sizing Skill

Help startup founders build credible, defensible TAM/SAM/SOM analysis — the kind that earns investor trust rather than raising eyebrows. Produce a structured, validated market sizing with external sources, sensitivity testing, and a self-check against common pitfalls. The tone is founder-first: a rigorous but supportive coaching session.

Input Formats

Accept any format: pitch deck (PDF, PPTX, markdown), financial model, market data, text descriptions, or verbal description of the business.

Available Scripts

All scripts are at

${CLAUDE_PLUGIN_ROOT}/skills/market-sizing/scripts/

• market_sizing.py

  — TAM/SAM/SOM calculator (top-down, bottom-up, or both)

• sensitivity.py

  — Stress-test assumptions with low/base/high ranges and confidence-based auto-widening

• checklist.py

  — Validates 22-item self-check with pass/fail per item

• compose_report.py

  — Assembles report with cross-artifact validation;

  --strict

  exits 1 on high/medium warnings

• visualize.py

  — Generates self-contained HTML with SVG charts (not JSON)

Run with:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/market-sizing/scripts/<script>.py --pretty [args]

Available References

Read as needed from

${CLAUDE_PLUGIN_ROOT}/skills/market-sizing/references/

• tam-sam-som-methodology.md

  — Definitions, calculation methods, industry examples, best practices

• pitfalls-checklist.md

  — Self-review checklist for common mistakes

• artifact-schemas.md

  — JSON schemas for all analysis artifacts

Artifact Pipeline

Every analysis deposits structured JSON artifacts into a working directory. The final step assembles all artifacts into a report and validates consistency. This is not optional.

inputs.json

methodology.json

validation.json

sizing.json

market_sizing.py -o

sensitivity.json

sensitivity.py

checklist.json

checklist.py

compose_report.py

Rules:

• Deposit each artifact before proceeding to the next step
• For agent-written artifacts (Steps 1-3), consult

  references/artifact-schemas.md

  for the JSON schema
• If a step is not applicable, deposit a stub:

  {"skipped": true, "reason": "..."}

• Do NOT use

  isolation: "worktree"

  for sub-agents — files written in a worktree won't appear in the main

  $ANALYSIS_DIR

Keep the founder informed with brief, plain-language updates at each step. Never mention file names, scripts, or JSON. After each analytical step (4–6), share a one-sentence finding before moving on.

Workflow

Path Setup

SCRIPTS="$CLAUDE_PLUGIN_ROOT/skills/market-sizing/scripts"
REFS="$CLAUDE_PLUGIN_ROOT/skills/market-sizing/references"
SHARED_REFS="$CLAUDE_PLUGIN_ROOT/references"
if ls "$(pwd)"/mnt/*/ >/dev/null 2>&1; then
  ARTIFACTS_ROOT="$(ls -d "$(pwd)"/mnt/*/ | head -1)artifacts"
elif ls "$(pwd)"/sessions/*/mnt/*/ >/dev/null 2>&1; then
  ARTIFACTS_ROOT="$(ls -d "$(pwd)"/sessions/*/mnt/*/ | head -1)artifacts"
else
  ARTIFACTS_ROOT="$(pwd)/artifacts"
fi

If

CLAUDE_PLUGIN_ROOT

Glob

**/founder-skills/skills/market-sizing/scripts/market_sizing.py

SCRIPTS

REFS

If

ARTIFACTS_ROOT

$(pwd)/artifacts

artifacts/

$(pwd)

Glob

**/artifacts/founder_context.json

ARTIFACTS_ROOT

mkdir -p "$ARTIFACTS_ROOT"

ANALYSIS_DIR="$ARTIFACTS_ROOT/market-sizing-{company-slug}"
mkdir -p "$ANALYSIS_DIR"

Steps 1-2: Extract Inputs & Choose Methodology

When files are provided (deck, model, market data), spawn a

general-purpose

SCRIPTS

REFS

SHARED_REFS

ANALYSIS_DIR

isolation: "worktree"

The sub-agent:

1. Reads the provided file(s)
2. Reads

   $REFS/tam-sam-som-methodology.md

3. Reads

   $REFS/artifact-schemas.md

   for

   inputs.json

   and

   methodology.json

   schemas
4. Extracts all market-relevant data
5. Writes

   inputs.json

   to

   $ANALYSIS_DIR/inputs.json

6. Writes

   methodology.json

   to

   $ANALYSIS_DIR/methodology.json

If the deck includes explicit TAM/SAM/SOM claims, record them in

inputs.json

existing_claims

Instruct the sub-agent to return ONLY:

1. File paths written
2. Company brief: name, product description, geography, target segments, pricing model, revenue model type
3. Quantitative data found: revenue, customer count, ARPU, growth rates, headcount (actual values, not the full inputs.json)
4. Existing claims: actual TAM/SAM/SOM numbers from the deck (if found), with slide/section reference
5. Methodology: chosen approach + rationale
6. Data availability: which key fields are populated vs missing
7. List of missing/unclear fields that the founder should clarify

Do not echo the full artifacts or raw document content. The company brief must be rich enough for the main agent to direct web research, construct sizing calculations, perform reality checks, and provide contextual coaching — without needing to re-read the source materials.

After the sub-agent returns, review the summary. If missing fields are flagged, ask the founder and patch

inputs.json

When conversational input (no files): Handle directly in the main agent — the data is already in the conversation. Read

references/tam-sam-som-methodology.md

Graceful degradation: If Task tool is unavailable, extract directly in the main agent.

Step 3: External Validation ->

validation.json

When methodology is "both", spawn 2

general-purpose

isolation: "worktree"

inputs.json

• Sub-agent A (Top-down research): WebSearch for industry reports, government statistics, analyst estimates for total market size, segment percentages, market growth rates.
• Sub-agent B (Bottom-up research): WebSearch for customer counts, pricing/ARPU benchmarks, competitor data, serviceable segment data. Also receives: pricing model, customer profile.

Each returns ONLY: (1) structured assumptions array

[{name, value, source_url, source_title, quality_tier, confidence, category}]

Main agent consolidates both sets of findings into

validation.json

When methodology is single (top-down or bottom-up), spawn 1 sub-agent for the chosen approach. Same return contract. Main agent writes

validation.json

When pure calculation (user provides all numbers, no validation needed): Handle directly — no sub-agent.

Source quality hierarchy: Government/regulatory > Established analysts > Industry associations > Academic > Business press > Company blogs (product facts only).

Triangulate key numbers with 2+ independent sources. Track every source with quality tier and segment match. Every assumption must appear in the

assumptions

name

category

sourced

derived

agent_estimate

Graceful degradation: If Task tool is unavailable, research directly in the main agent.

Step 4: Calculate TAM/SAM/SOM ->

sizing.json

cat <<'SIZING_EOF' | python3 "$SCRIPTS/market_sizing.py" --stdin --pretty -o "$ANALYSIS_DIR/sizing.json"
{...sizing input JSON — see artifact-schemas.md for format...}
SIZING_EOF

For "both" mode, check the comparison section — a >30% TAM discrepancy means investigating which assumptions are flawed. TAM must match the product's actual target universe (not inflated industry totals).

Multi-vertical / platform companies: If

inputs.json

1. Identify verticals — classify as

   commercial

   (revenue/pilots),

   r_and_d

   (demonstrated feasibility, 2-3yr commercialization path), or

   future

   (conceptual/early).
2. Include

   commercial

   and

   r_and_d

   in TAM. If top-down only covers one vertical, use bottom-up as primary. When verticals have different ARPUs, compute weighted blended ARPU.

   Future

   verticals go in coaching commentary as upside, not in the calculated TAM.
3. Narrow SAM and SOM — SAM = traction + active R&D segments. SOM = beachhead only.
4. Document scope in

   methodology.json

   rationale

   .

Default to full-scope TAM. Only narrow to beachhead if the user explicitly requests it.

Step 4.5: Reality Check

Before proceeding, answer:

1. Laugh test: Would an experienced VC nod or raise an eyebrow? Seed + <5 pilots + >$1B TAM = explain yourself.
2. Scope match: Does TAM cover all

   commercial

   and

   r_and_d

   verticals from

   inputs.json

   ?
3. Customer count sanity: Can you name a representative sample of the customers in your count?
4. Convergence integrity: Were top-down and bottom-up parameters set independently? If you adjusted one after seeing the other, revert and accept the delta.

This step produces no artifact. If it reveals problems, fix them before proceeding.

Steps 5 & 6: Parallel Analysis (Sensitivity + Checklist)

Spawn 2

general-purpose

isolation: "worktree"

SCRIPTS

REFS

ANALYSIS_DIR

Sub-agent A — Sensitivity:

Reads

$ANALYSIS_DIR/validation.json

$ANALYSIS_DIR/sizing.json

sensitivity.py

Tag each parameter with confidence from validation:

sourced

derived

agent_estimate

agent_estimate

UNSOURCED_ASSUMPTIONS

cat <<'SENS_EOF' | python3 "$SCRIPTS/sensitivity.py" --pretty -o "$ANALYSIS_DIR/sensitivity.json"
{...sensitivity input JSON — see artifact-schemas.md for format...}
SENS_EOF

Instruct Sub-agent A to return ONLY: (1) file path written, (2)

most_sensitive

Sub-agent B — Checklist:

Reads

$REFS/pitfalls-checklist.md

inputs.json

methodology.json

validation.json

sizing.json

$REFS/artifact-schemas.md

cat <<'CHECK_EOF' | python3 "$SCRIPTS/checklist.py" --pretty -o "$ANALYSIS_DIR/checklist.json"
{"items": [
  {"id": "structural_tam_gt_sam_gt_som", "status": "pass", "notes": null},
  ...all 22 items...
]}
CHECK_EOF

Instruct Sub-agent B to return ONLY: (1) file path written, (2)

score_pct

overall_status

Graceful degradation: If Task tool is unavailable, run Steps 5-6 sequentially in the main agent.

After both sub-agents return, share a coaching update with the founder before proceeding to Step 7.

Step 7: Compose and Validate Report

python3 "$SCRIPTS/compose_report.py" --dir "$ANALYSIS_DIR" --pretty -o "$ANALYSIS_DIR/report.json"

Fix high-severity warnings and re-run. Use

--strict

Primary deliverable: Read

report_markdown

$ANALYSIS_DIR/report.md

Step 8 (Optional): Generate Visual Report

python3 "$SCRIPTS/visualize.py" --dir "$ANALYSIS_DIR" -o "$ANALYSIS_DIR/report.html"

Present the HTML file path to the user so they can open the visual report.

Step 9: Deliver Artifacts

Copy final deliverables to workspace root:

{Company}_Market_Sizing.md

.html

.json

Scoring

• Each of 22 items: pass / fail / not_applicable

• score_pct

  = pass / (total - not_applicable) x 100
• compose_report.py validates cross-artifact consistency (assumption coverage, sensitivity ranges)