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How Claude actually judges your startup idea (a reasoning chain dump)

An audit reasoning chain is the sequence of prompts, tool calls, and intermediate verdicts a large language model runs through before producing its final score — visible to anyone who instruments their MCP server and inspects the trace.

inite2 min read

Claude judges a startup idea by extracting structured fields from your prompt, running three parallel research lenses (market, competition, problem-solution), then a sequential pipeline (team, traction, business model, GTM, finance, mission), surfacing cross-lens contradictions, and finally computing a stage-aware weighted score across 6 dimensions. A `GO` requires score ≥ 80, zero red flags, avg confidence ≥ 0.6, and no major contradiction.

Key facts

  • Methodology v2 weights shift by stage: idea-stage puts 35% on Problem-Solution; Series A+ puts 30% on Traction.
  • Avg audit fires 8-12 MCP tool calls; complex audits with cross-lens contradictions fire 18-22.
  • Confidence < 0.6 caps the maximum score at 65 regardless of lens scores.
  • A single major contradiction (severity ≥ 0.7) automatically downgrades a `GO` to `REFINE`.

Open the dashboard, click any audit, and you can read the reasoning trace top to bottom. Here is what one looks like for the running example "audit this idea: an AI note-taking app for lawyers".

Step 1 — extract

Claude calls extract_idea. Returns {stage: "mvp", sector: "legal_tech", problem: "billable-hour reconstruction is manual and error-prone", solution: "voice + screen capture → automatic time entry"}. Confidence on each field is logged. Ambiguity ≥ 0.5 halts the pipeline and asks you to clarify — silent assumptions are how generic LLMs hallucinate.

Step 2 — three parallel lenses

Three skills run concurrently, each calling its own MCP tools:

  • audit-market → calls web_search for "legal tech market size 2026", dataforseo_trends for "billable hours software", returns TAM bounds with named comparables.
  • audit-competition → calls web_search for "AI billable hours lawyer", lookup_entity for the named players (Clio, MyCase, Smokeball), returns 5 segments + market gaps.
  • audit-problem-solution-fit → calls web_search on Reddit threads in r/Lawyertalk, returns 5 problem headlines with frequency counts + 5 solution variants.

Step 3 — sequential pipeline

Each subsequent skill consumes outputs from the parallel lenses:

  1. audit-team weights founder credibility against the legal-tech sector lens. (Skipped if no team data.)
  2. audit-traction classifies idea-stage vs established by counting metrics signals.
  3. audit-business-model generates 3 variants (verbatim user model + 2 alternatives). You pick one before the pipeline continues.
  4. audit-gtm-scale maps a 4-stage evolution (MVP launch → Y1 pilot → Y2 scale → Y3 growth).
  5. audit-finance builds 4-year projections using stage-aware multiples.
  6. audit-mission crafts an investor-ready Mission slide (80-150 char headline, 4 supporting bullets).

Step 4 — consensus check

Every lens output gets diffed for contradictions. Real example from a recent run:

Lens A (market): "TAM ≈ $4B, CAGR 18%." Lens B (competition): "Saturated by Clio + 4 large incumbents with 80% market share."

Severity 0.65. Logged via consensus_check MCP tool. Feeds into dealbreakers.

Step 5 — dealbreakers v2

Final pass: stage-aware weighted score, confidence-weighted aggregation, risk-asymmetric verdict. The math is:

score = sum(lens_score[i] * stage_weight[i] * confidence[i]) / sum(weight * confidence)
verdict =
  GO     if score ≥ 80 AND no red flags AND avg confidence ≥ 0.6 AND no major contradiction
  REFINE if 60 ≤ score < 80 OR (score ≥ 80 with cap-triggering condition)
  KILL   otherwise

The cap-triggering conditions are intentional — a score of 92 with one severity-0.8 contradiction is not GO. The system errs toward false negatives so the founder hears bad news before the investor does.

Why this beats one-shot LLM verdicts

A generic LLM that you ask "is this idea good?" produces a plausible-sounding answer because it pattern-matches. The methodology v2 verdict is reproducible — same prompt, same data sources, same weights, same score. That makes it useful as a tool, not just a conversation.

Run your own:

curl -fsSL https://inite.studio/install.sh | sh

FAQ

Frequently asked questions

  1. What does 'stage-aware' actually mean?

    The 6-lens weights re-distribute based on whether the idea is at idea / mvp / seed / series_a_plus stage. Idea-stage demands rigorous Problem-Solution evidence; Series A+ demands traction. The same audit run twice with different stages produces different verdicts.

  2. How does Claude detect contradictions across lenses?

    After every lens runs, the consensus_check MCP tool diffs claim pairs across lenses (market vs competition, traction vs finance) and flags pairs where the same entity carries opposite signals. Each contradiction lands with a 0-1 severity score and feeds into the dealbreakers pass.

  3. Can I see the raw reasoning trace?

    Yes, partially. The dashboard surfaces every tool call, every intermediate verdict, and every contradiction — the prompt body itself stays on your machine. The MCP server only sees the structured tool inputs, not the conversation.

  4. Is this better than asking ChatGPT 'is my idea good'?

    Concretely yes — generic LLM verdicts have no grounding (no SERP fetch, no Trends, no Reddit), no methodology (one-shot output, no contradiction check), and no stage awareness. The methodology v2 verdict is reproducible: same prompt, same data sources, same weights = same score.

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