Market sizing

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.

Skill Metadata

• Author: lool-ventures
• Version: managed in founder-skills/.claude-plugin/plugin.json
• Compatibility: Python 3.10+ and uv for script execution.
• Exports:
  • sizing.json → financial-model-review, ic-sim, fundraise-readiness
  • sensitivity.json → financial-model-review
  • checklist.json → ic-sim

Skill Execution Model (READ FIRST)

This skill runs inline in the main thread (not as a sub-agent). The main thread has full tool access including Bash and WebFetch, and is responsible for orchestrating the full pipeline: running producer scripts, persisting artifacts, performing web research, and dispatching the market-sizing sub-agent at specific moments.

Two dispatch contexts for the sub-agent:

• Context A — Per-step analytical dispatch (Mitigation 1): Steps 5 and 6 dispatch the market-sizing agent via the Task tool. The key element here is parallel dispatch: Step 5 (methodology calculation) dispatches the agent twice simultaneously — one for TOP_DOWN_METHODOLOGY and one for BOTTOM_UP_METHODOLOGY — in a single assistant turn when the methodology is "both". The sub-agent does deep analysis and returns structured JSON. The main thread captures the JSON and pipes it through the producer script (market_sizing.py --stdin). The sub-agent does NOT write artifacts directly.
• Context B — Post-compose coaching dispatch: The final step dispatches the sub-agent after compose_report.py --write-md has written report.md. The sub-agent reads report.md, appends ## Coaching Commentary, verifies all canonical artifacts on disk, and returns a structured success payload.

Why this model: In Cowork, sub-agents have a restricted tool allowlist (no Bash). By keeping orchestration in the main thread and dispatching sub-agents only for analytical or post-compose tasks that use only Read/Edit/Glob/Grep, the pipeline works correctly in both Claude Code (CLI) and Cowork.

WebFetch-before-dispatch pattern: The main thread performs WebFetch/WebSearch research calls BEFORE dispatching sub-agents. Research data is passed inline in the sub-agent prompts. Sub-agents in Cowork cannot reach the network from inside the dispatch fork (no WebFetch/WebSearch in sub-agent allowlist).

Tolerant JSON extraction protocol (Context A): After dispatching the sub-agent, capture its final assistant message. The sub-agent should return raw JSON, but may wrap it in ```json ... ``` fences or add a prose preamble. Extract JSON tolerantly:

1. If the message is wrapped in a ```json ... ``` (or plain ``` ... ```) fence, strip the fence first.
2. Try to parse the stripped text directly as JSON.
3. If that fails, walk through the text looking for the first { character and try json.JSONDecoder().raw_decode(text[i:]) — this is brace-aware and handles nested objects correctly (unlike regex, which truncates on the first }).
4. If extraction fails entirely, re-prompt the sub-agent with: "Your previous reply could not be parsed as JSON. Return ONLY the JSON object — no markdown fences, no prose preamble."

See founder-skills/references/skill-execution-model.md for the full inline-skill execution model (3 dispatch contexts, Mitigation 1+2, producer contract, Cowork quirks, per-symptom triage).

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); accepts --stdin for JSON piping
• 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; --write-md writes report.md; --strict exits 1 on high/medium warnings
• visualize.py — Generates self-contained HTML with SVG charts (not JSON)

Also available from ${CLAUDE_PLUGIN_ROOT}/scripts/ (shared):

• founder_context.py — Per-company context management (init/read/merge/validate)

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.

Step	Artifact	Producer
1	founder context	founder_context.py read/init
2	inputs.json	Agent (heredoc)
3	methodology.json	Agent (heredoc)
4	validation.json	Main thread (WebFetch/WebSearch research)
5	sizing.json	Context A dispatch: TOP_DOWN_METHODOLOGY + BOTTOM_UP_METHODOLOGY in parallel → market_sizing.py --stdin
6a	sensitivity.json	Context A dispatch: SENSITIVITY_TEST → sensitivity.py
6b	checklist.json	Context A dispatch: CHECKLIST → checklist.py
7	Report	compose_report.py --write-md (writes both report.json and report.md)
8	Coaching	Context B dispatch: POST_COMPOSE_COACHING

Rules:

• Deposit each artifact before proceeding to the next step
• For agent-written artifacts (Steps 2-4), 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 (5–6), share a one-sentence finding before moving on.

Workflow

Step 0: Path Setup

Every Bash tool call runs in a fresh shell — variables do not persist. Prefix every Bash call that uses these paths with the variable block below, or substitute absolute paths directly:

SCRIPTS="${CLAUDE_PLUGIN_ROOT}/skills/market-sizing/scripts"
REFS="${CLAUDE_PLUGIN_ROOT}/skills/market-sizing/references"
SHARED_SCRIPTS="${CLAUDE_PLUGIN_ROOT}/scripts"
SHARED_REFS="${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 is empty OR the path it resolves to does not exist in your environment (in Claude Cowork it substitutes to a host-side path that is not present inside the session VM — test with ls), fall back: Glob for **/skills/market-sizing/scripts/market_sizing.py, strip to get SCRIPTS, derive REFS and SHARED_SCRIPTS. In Claude Cowork this is always the case — don't retry the substituted path; go straight to the Glob fallback. If Glob returns multiple matches, prefer the one under a plugin mount (.remote-plugins/ or the plugins cache) over any workspace copy. If Glob returns nothing, locate it with Bash: find / -path '*/skills/market-sizing/scripts/market_sizing.py' 2>/dev/null | head -5.

If ARTIFACTS_ROOT resolves to $(pwd)/artifacts but no artifacts/ directory exists at $(pwd): Use Glob with pattern **/artifacts/founder_context.json to locate existing artifacts, and derive ARTIFACTS_ROOT from the result. If nothing is found, mkdir -p "$ARTIFACTS_ROOT" and proceed.

After Step 1 (when the slug is known):

ANALYSIS_DIR="$ARTIFACTS_ROOT/market-sizing-${SLUG}"
mkdir -p "$ANALYSIS_DIR"
mkdir -p "$ANALYSIS_DIR/.staging"   # for ad-hoc sub-agent JSON staging
RUN_ID="$(date -u +%Y%m%dT%H%M%SZ)"

Pass RUN_ID to all sub-agents. Every artifact written to $ANALYSIS_DIR must include "metadata": {"run_id": "$RUN_ID"} at the top level. compose_report.py checks that all artifact run IDs match — a mismatch triggers a STALE_ARTIFACT high-severity warning, blocking under --strict.

If ANALYSIS_DIR already contains artifacts from a previous run, remove them before starting:

rm -f "$ANALYSIS_DIR"/{inputs,methodology,validation,sizing,sensitivity,checklist,report}.json "$ANALYSIS_DIR"/report.{html,md}

In Cowork, file deletion may require explicit permission. If cleanup fails with "Operation not permitted", request delete permission and retry before proceeding.

Step 1: Read or Create Founder Context

python3 "$SHARED_SCRIPTS/founder_context.py" read --artifacts-root "$ARTIFACTS_ROOT" --pretty

Exit 0 (found): Use the company slug and pre-filled fields. Proceed to Step 2.

Exit 1 (not found): This is normal for a first run — do not treat it as an error. Use AskUserQuestion (NOT plain chat) to ask for company name, stage, sector, and geography. Provide at least 2 options. Then create:

python3 "$SHARED_SCRIPTS/founder_context.py" init \
  --company-name "Acme Corp" --stage seed --sector "B2B SaaS" \
  --geography "US" --artifacts-root "$ARTIFACTS_ROOT"

Exit 2 (multiple): Present the list, ask which company, re-read with --slug.

Steps 2-3: Extract Inputs & Choose Methodology

When files are provided (deck, model, market data), read the provided file(s) directly and extract market-relevant data. Read $REFS/tam-sam-som-methodology.md and $REFS/artifact-schemas.md.

Extract all market-relevant data. If the deck includes explicit TAM/SAM/SOM claims, record them in inputs.json under existing_claims.

existing_claims must be a flat object with lowercase keys tam, sam, som. Use null for any figure the deck does not state. Custom keys (e.g., SAM_Israel_only) are silently ignored by reconciliation and will trigger an EXISTING_CLAIMS_SHAPE warning.

If the deck states figures that don't fit the flat shape — regional sub-SAMs, time-anchored SOM projections, alternative TAM frames — put them in the optional existing_claims_detail field (any structure). This field does NOT participate in deck-vs-computed reconciliation, but it is rendered as a "Deck Claims (Narrative)" sub-section in the report.

Write inputs.json:

cat <<'INPUTS_EOF' > "$ANALYSIS_DIR/inputs.json"
{
  "company_name": "...",
  "analysis_date": "YYYY-MM-DD",
  "stage": "seed",
  "sector": "...",
  "geography": "...",
  "product_description": "...",
  "target_segments": ["..."],
  "pricing_model": "...",
  "revenue_model": "...",
  "existing_claims": {"tam": null, "sam": null, "som": null},
  "existing_claims_detail": null,
  "materials_provided": ["..."],
  "metadata": {"run_id": "<RUN_ID>"}
}
INPUTS_EOF

Write methodology.json:

cat <<'METH_EOF' > "$ANALYSIS_DIR/methodology.json"
{
  "approach_chosen": "both",
  "rationale": "...",
  "metadata": {"run_id": "<RUN_ID>"}
}
METH_EOF

When conversational input (no files): Extract directly from the conversation. Read references/tam-sam-som-methodology.md, choose the approach, and write both artifacts directly.

After writing, verify that $ANALYSIS_DIR contains both inputs.json and methodology.json.

Gate: Confirm Methodology and Inputs

MANDATORY STOP — TWO SEPARATE STEPS. DO NOT COMBINE THEM.

Step A: Output a chat message with the methodology choice and key inputs. Use a formatted summary. This is a normal assistant message — NOT an AskUserQuestion call. Example:

Here's what I've extracted and how I plan to approach the sizing:

**Company:** Acme Corp — AI-powered compliance for fintechs
**Geography:** US
**Target segments:** Mid-market fintechs ($10M-$500M revenue)

**Methodology:** Both top-down and bottom-up
- Top-down: Global RegTech market → US share → fintech compliance segment
- Bottom-up: ~2,400 target fintechs × $48K ARPU

**Key inputs found:**
| Input | Value | Source |
|-------|-------|--------|
| Current ARR | $850K | Deck slide 7 |
| Customers | 12 | Deck slide 8 |
| ARPU (monthly) | $4,000 | Derived from ARR/customers |
| Growth rate | 15% MoM | Deck slide 9 |

**Missing / needs clarification:**
- Geographic expansion plans (US only or international?)
- Enterprise vs SMB customer split

If existing_claims were found in the deck, include them: "Your deck claims TAM of $X — I'll validate this against external sources."

Step B: AFTER the chat message, call AskUserQuestion with ONLY a short question. The question field is plain text — NO markdown, NO tables, NO bullet points.

Question: Does this approach and these inputs look right? Options: Looks good / Change methodology / Correct or add data

CRITICAL: The AskUserQuestion question must be ONE SHORT SENTENCE. Put ALL details in the chat message (Step A), not in the question.

This two-step pattern (chat message then AskUserQuestion) is required because AskUserQuestion renders as plain text. Detailed content goes in the chat message; only the gate question goes in AskUserQuestion.

If the founder selects "Looks good": Proceed to Step 4 (External Validation).

If "Change methodology": Ask which approach they prefer (top-down / bottom-up / both) and why. Update methodology.json and repeat Steps A+B.

If "Correct or add data": Ask which values are wrong or missing, correct/patch inputs.json, and check whether the updated inputs change what methodology is viable. If so, update methodology.json too. Repeat Steps A+B.

Step 4: External Validation -> validation.json

The main thread performs WebFetch/WebSearch research. Do NOT dispatch a sub-agent for this step — the main thread has WebFetch/WebSearch capability, and sub-agents in Cowork do not. Perform all web research calls yourself.

When methodology is "both": Research both approaches in parallel (two WebSearch calls in one assistant turn — one for top-down market data, one for bottom-up customer/ARPU data).

• Top-down research: WebSearch for industry reports, government statistics, analyst estimates for total market size, segment percentages, market growth rates.
• Bottom-up research: WebSearch for customer counts, pricing/ARPU benchmarks, competitor data, serviceable segment data.

When methodology is single: Perform one research pass for the chosen approach.

When pure calculation (user provides all numbers): Skip research. Write a stub validation.json with {"skipped": true, "reason": "User-provided inputs, no external validation required"}.

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

Triangulate key numbers with 2+ independent sources. Every assumption must appear in the assumptions array with a name matching script parameter names and a category of sourced, derived, or agent_estimate.

Write validation.json directly:

cat <<'VAL_EOF' > "$ANALYSIS_DIR/validation.json"
{
  "assumptions": [
    {"name": "industry_total", "value": 50000000000, "category": "sourced", "label": "Global RegTech market", "source_url": "...", "source_title": "...", "confidence": "high"},
    ...
  ],
  "figure_validations": [
    {"figure": "TAM", "label": "Global RegTech TAM", "status": "validated", "source_count": 2}
  ],
  "sources": [
    {"title": "...", "url": "...", "publisher": "...", "date_accessed": "YYYY-MM-DD", "supported": "industry_total"}
  ],
  "metadata": {"run_id": "<RUN_ID>"}
}
VAL_EOF

Sub-agent JSON staging

When a sub-agent returns JSON too large for bash heredoc, write it to $ANALYSIS_DIR/.staging/<step>_input.json first, then pipe via:

cat "$ANALYSIS_DIR/.staging/<step>_input.json" | python3 "$SCRIPTS/<producer>.py" ...

The .staging/ directory is created at setup and removed at cleanup. This avoids Operation not permitted errors that occur when writing to the session outputs mount (Cowork marks it read-only post-write).

Step 5: Calculate TAM/SAM/SOM -> sizing.json (Context A dispatch)

The dispatch pattern depends on methodology:

Parallel dispatch recipe (methodology = "both")

Dispatch the market-sizing agent TWICE in parallel via the Task tool — one for top-down, one for bottom-up. Use a SINGLE assistant turn with 2 Task tool calls (NOT two sequential turns). The Claude Code harness runs both Task calls in parallel when they appear in the same assistant response.

Pseudocode for the dispatch (executed as 2 parallel Task tool_use blocks):

[
  Task(description="Market sizing: top-down methodology",
       prompt="CONTEXT: TOP_DOWN_METHODOLOGY\nANALYSIS_DIR: <path>\nRUN_ID: <id>\n<research data from validation.json>"),
  Task(description="Market sizing: bottom-up methodology",
       prompt="CONTEXT: BOTTOM_UP_METHODOLOGY\nANALYSIS_DIR: <path>\nRUN_ID: <id>\n<research data from validation.json>"),
]

Full dispatch prompt template (TOP_DOWN_METHODOLOGY):

CONTEXT: TOP_DOWN_METHODOLOGY
ANALYSIS_DIR: <absolute path to ANALYSIS_DIR>
RUN_ID: <RUN_ID>

You are the market-sizing agent dispatched in Context A (TOP_DOWN_METHODOLOGY).
Read inputs.json at <ANALYSIS_DIR>/inputs.json and validation.json at
<ANALYSIS_DIR>/validation.json.

Using the top-down approach, compute TAM/SAM/SOM. Pre-fetched research data:
<inline the relevant assumptions from validation.json — industry_total, segment_pct, share_pct>

Return JSON only — exactly the shape expected by market_sizing.py --stdin
for approach "top_down":
{
  "approach": "top_down",
  "industry_total": <number>,
  "segment_pct": <number>,
  "share_pct": <number>
}

Full dispatch prompt template (BOTTOM_UP_METHODOLOGY):

CONTEXT: BOTTOM_UP_METHODOLOGY
ANALYSIS_DIR: <absolute path to ANALYSIS_DIR>
RUN_ID: <RUN_ID>

You are the market-sizing agent dispatched in Context A (BOTTOM_UP_METHODOLOGY).
Read inputs.json at <ANALYSIS_DIR>/inputs.json and validation.json at
<ANALYSIS_DIR>/validation.json.

Using the bottom-up approach, compute TAM/SAM/SOM. Pre-fetched research data:
<inline the relevant assumptions from validation.json — customer_count, arpu, serviceable_pct, target_pct>

Return JSON only — exactly the shape expected by market_sizing.py --stdin
for approach "bottom_up":
{
  "approach": "bottom_up",
  "customer_count": <integer>,
  "arpu": <number>,
  "serviceable_pct": <number>,
  "target_pct": <number>
}

After both sub-agents return: apply the tolerant JSON extraction protocol (see "Skill Execution Model" preamble) to each returned message. Then pipe through the producer script for each, combining results:

# Combine top-down and bottom-up into a single "both" payload
cat <<'SIZING_EOF' | python3 "$SCRIPTS/market_sizing.py" --stdin --pretty -o "$ANALYSIS_DIR/sizing.json"
{
  "approach": "both",
  "industry_total": <from top-down sub-agent>,
  "segment_pct": <from top-down sub-agent>,
  "share_pct": <from top-down sub-agent>,
  "customer_count": <from bottom-up sub-agent>,
  "arpu": <from bottom-up sub-agent>,
  "serviceable_pct": <from bottom-up sub-agent>,
  "target_pct": <from bottom-up sub-agent>
}
SIZING_EOF

Single methodology dispatch

When methodology is "top_down" only: dispatch one TOP_DOWN_METHODOLOGY task and pipe through market_sizing.py --stdin. When methodology is "bottom_up" only: dispatch one BOTTOM_UP_METHODOLOGY task and pipe through market_sizing.py --stdin.

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 lists applications in 2+ distinct industries:

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 5.5: 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 6a & 6b: Parallel Analysis (Sensitivity + Checklist) (Context A dispatch)

Dispatch the market-sizing agent TWICE in parallel via the Task tool — one for SENSITIVITY_TEST, one for CHECKLIST. Use a SINGLE assistant turn with 2 Task tool calls. The Claude Code harness runs both Task calls in parallel.

SENSITIVITY_TEST dispatch prompt template

CONTEXT: SENSITIVITY_TEST
ANALYSIS_DIR: <absolute path to ANALYSIS_DIR>
RUN_ID: <RUN_ID>

You are the market-sizing agent dispatched in Context A (SENSITIVITY_TEST).
Read:
- <ANALYSIS_DIR>/validation.json — for confidence tiers
- <ANALYSIS_DIR>/sizing.json — for base values and approach

Construct sensitivity input with confidence-based ranges. Tag each parameter
with confidence from validation: `sourced` (range stands), `derived`
(min +/-30%), `agent_estimate` (min +/-50%). Include EVERY `agent_estimate`
parameter — compose_report.py flags missing ones as UNSOURCED_ASSUMPTIONS.

Return JSON only — exactly the shape expected by sensitivity.py:
{
  "approach": "bottom_up|top_down|both",
  "base": {<parameter: value pairs from sizing.json>},
  "ranges": {
    "<parameter>": {"low_pct": <negative number>, "high_pct": <positive number>}
  }
}

After the sub-agent returns: apply the tolerant JSON extraction protocol. Then pipe through the producer script:

cat <<'SENS_EOF' | python3 "$SCRIPTS/sensitivity.py" --pretty -o "$ANALYSIS_DIR/sensitivity.json"
<JSON extracted from sub-agent reply>
SENS_EOF

CHECKLIST dispatch prompt template

CONTEXT: CHECKLIST
ANALYSIS_DIR: <absolute path to ANALYSIS_DIR>
RUN_ID: <RUN_ID>

You are the market-sizing agent dispatched in Context A (CHECKLIST). Read:
- ${CLAUDE_PLUGIN_ROOT}/skills/market-sizing/references/pitfalls-checklist.md
- ${CLAUDE_PLUGIN_ROOT}/skills/market-sizing/references/artifact-schemas.md
  (read the "Canonical 22 checklist IDs" section)
- <ANALYSIS_DIR>/inputs.json
- <ANALYSIS_DIR>/methodology.json
- <ANALYSIS_DIR>/validation.json
- <ANALYSIS_DIR>/sizing.json

Assess all 22 items with status (pass/fail/not_applicable) and notes.

Return JSON only — the items array without a summary (the producer script
computes the summary):
{
  "items": [
    {"id": "structural_tam_gt_sam_gt_som", "status": "pass", "notes": null},
    ...all 22 items...
  ]
}

After the sub-agent returns: apply the tolerant JSON extraction protocol. Then pipe through the producer script:

cat <<'CHECK_EOF' | python3 "$SCRIPTS/checklist.py" --pretty -o "$ANALYSIS_DIR/checklist.json"
<JSON extracted from sub-agent reply>
CHECK_EOF

Verify after both sub-agents return: check that $ANALYSIS_DIR contains fresh sensitivity.json and checklist.json. If either is missing, re-run the failed dispatch before proceeding. Share a coaching update with the founder.

Step 7: Compose and Validate Report

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

Fix high-severity warnings and re-run. Use --strict to enforce a clean report.

Post-write verification: compose_report.py exits non-zero (code 2) if the declared output files don't exist or are empty after writing. If compose exits non-zero, stop and report the exact stderr — do not proceed to Step 8.

Step 8: Post-Compose Coaching Commentary (Context B dispatch, POST_COMPOSE_COACHING)

Dispatch the market-sizing sub-agent in Context B (Mitigation 2). Dispatch via the Task tool after compose_report.py has successfully written report.md.

Before dispatching, construct the coaching_payload from the compose output. Read report.json to extract the checklist summary, failed items, and high-severity warnings. Read sizing.json for tam, sam, som. Read methodology.json for methodology. Read checklist.json for confidence (or derive from the checklist score_pct). Extract the insertion_marker from report.json (the compose script emits it alongside report_markdown). Do NOT pass warned_items from a warn status — market-sizing checklist has no warn status, so warned_items is always [].

The compose script also emits coaching_payload.deck_coverage directly in report.json — copy this field verbatim into the dispatch prompt (it is null when the founder's existing_claims had no canonical figures stated; otherwise {"deck_reviewed": true, "stated": [...], "missing": [...]}). Coaching framing for deck_coverage: when missing is non-empty, frame as "deck stated {stated} but should also show {missing}" — NOT understatement. If the warnings list contains EXISTING_CLAIMS_SHAPE, do not trust deck_coverage = null as "deck wasn't reviewed"; frame the coaching around the warning and the "Deck Claims (Narrative)" section instead.

Dispatch prompt template:

CONTEXT: POST_COMPOSE_COACHING
ANALYSIS_DIR: <absolute path to ANALYSIS_DIR>
RUN_ID: <RUN_ID>

You are the market-sizing agent dispatched in Context B (POST_COMPOSE_COACHING).
The main thread has already run all producer scripts and composed the final
report using the Mitigation 2 protocol. Do NOT read the full report.md.

coaching_payload:
{
  "schema_version": "v0.4.2-market-sizing",
  "summary": {
    "score_pct": <number from checklist.json summary>,
    "total": <number>,
    "pass": <number>,
    "fail": <number>,
    "not_applicable": <number>
  },
  "failed_items": [<array of failed checklist item objects with id and notes>],
  "warned_items": [],
  "high_severity_warnings": [<codes from report.json validation.warnings where severity=="high">],
  "methodology": "<top_down|bottom_up|both from methodology.json>",
  "confidence": "<high|medium|low from sizing.json or checklist>",
  "tam": <tam value from sizing.json>,
  "sam": <sam value from sizing.json>,
  "som": <som value from sizing.json>,
  "company_name": "<from inputs.json>",
  "deck_coverage": <null OR {"deck_reviewed": true, "stated": [<canonical keys with values>], "missing": [<canonical keys with null>]} — copy verbatim from coaching_payload emitted by compose_report.py>,
  "review_dir": "<ANALYSIS_DIR absolute path>",
  "report_path": "<ANALYSIS_DIR>/report.md",
  "insertion_marker": "<EXACT marker string from report.json, e.g. <!-- COACHING_INSERTION_POINT_a1b2c3d4 -->"
}

Follow the POST_COMPOSE_COACHING procedure in your agent body exactly:
1. grep_idempotency_check (6-state matrix)
2. Compose commentary from coaching_payload (failed_items only; warned_items is always [])
3. edit_via_marker (single Edit call replacing insertion_marker with ## Coaching Commentary\n\n<commentary>)
4. self_verify_artifacts_via_grep_run_id (Grep run_id in all 6 producer artifacts)
5. Return success payload

Return:
{"status": "complete", "review_dir": "<path>", "report_path": "<path>",
 "tam": <number>, "sam": <number>, "som": <number>,
 "methodology": "<top_down|bottom_up|both>",
 "confidence": "<high|medium|low>",
 "high_severity_warnings": [<list>]}
OR if any step fails:
{"status": "blocked", "reason": "<specific gap>"}

After the sub-agent returns: apply the tolerant JSON extraction protocol to obtain the success/blocked payload. If status == "blocked", stop and report the reason to the founder. If status == "complete", present report_path to the founder.

Step 9 (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 10: Deliver Artifacts

Copy final deliverables to workspace root: {Company}_Market_Sizing.md, .html (if generated), .json (optional).

rm -rf "$ANALYSIS_DIR/.staging" 2>/dev/null || true

Edge Cases

• Founder provided text, not a file: When the founder describes their market in conversation rather than uploading a file, adapt: write inputs.json from the conversation and note reduced confidence in data-backed assertions. Set materials_provided: ["text"].
• Cross-skill context: If founder_context.py returned prior deck-review or financial-model-review runs, mention relevant findings in coaching commentary (e.g., "Your deck claims $X TAM — our analysis calculates $Y"). Do not hard-fail on discrepancies; flag them for the founder.

Main-Thread Return

This skill runs inline in the main thread (not as a sub-agent). The final outcome the main thread delivers to the founder is:

• The path to $ANALYSIS_DIR/report.md — the primary deliverable.
• The structured success payload from the Context B sub-agent (Step 8): {status, review_dir, report_path, tam, sam, som, methodology, confidence, high_severity_warnings}.
• Optionally: the HTML report path from Step 9.

Do NOT inline report_markdown in the assistant message. The founder reads the file via the path. (Closes issue #13: ~25 KB of markdown was previously round-tripped through the parent context.)

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)