studio.inite/ideaudit-tools

Tools

• compute_barrier

  Compute barrier_score (0-24) + label (PRISTINE/OPEN/COMPETITIVE/CROWDED) from competitor counts + SERP noise fraction.

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• compute_budget_proof

  Compute budget_proof_score (0-10) + label (STRONG/CONFIRMED/WEAK/ABSENT) + purchase_intent_pct from pricing hits + review-site hits + intent mentions.

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• compute_build_complexity

  Compute build_complexity_penalty (0-10, higher = worse) + per-factor breakdown. Hard tags: ml/realtime/blockchain/hardware/compliance/custom-ai/regulated/on-device-ai/iot.

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• compute_collection_scores

  Compute 12 deterministic collection scores (0-100) + badges + death reason for an enriched idea. Pure math. No external calls.

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• compute_crossed_matrix

  Crossed-product audit explorer. Same input as compute_dealbreakers_v2 — returns substrate verdict (no-observer baseline) + crossed verdict (when observer supplied) + a 5-row matrix of {solo, cofounded_technical, cofounded_business, domain_expert, serial} archetype verdicts. Never persists; meant for the dashboard "view as [archetype]" dropdown and for previewing a verdict before committing to it.

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• compute_dealbreakers_v2

  Methodology v2 dealbreakers — stage-aware weights + confidence-weighted lens scoring + risk-asymmetric verdict (GO requires score≥80 AND zero red flags AND avg confidence≥0.6). Optional `observer` triggers the crossed-product pipeline: substrate verdict (no-observer baseline) PLUS crossed verdict (observer-perturbed weights, risk-tolerance shifted thresholds) PLUS 5-row archetype matrix. The KILL gate (≥2 blockers / score<50) is observer-invariant — fatal stays fatal.

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• compute_funding_momentum

  Compute funding_momentum_score (0-10) + badge (HOT/WARM/COOL/COLD) from tier-weighted funding-article hit counts.

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• compute_hiring_demand

  Compute hiring_demand_score (0-10) from priority-weighted ATS site hit counts (use registries/hiring-sources for priorities).

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• compute_lrs_composite

  Compose lrs_final_100 (0-100) + label (WEAK/EMERGING/GOOD/STRONG/ELITE) + leaderboard_eligible flag + sub-percent breakdown. Weights: sv 0.25, sp 0.30, barrier 0.25, monetization 0.20.

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• compute_lrs_composite_v2

  LRS composite v2 — 6 components (SV, Pain, Barrier, Monet, X-Signal, Budget-Proof). Default Python weights 0.18/0.22/0.18/0.14/0.18/0.10 sum=1.0. Returns BOTH weighted score and equal-weight baseline (per OECD Handbook + Greco 2018 — equal-weight is defensible default when no outcome calibration exists). buildComplexityPenalty 0-10 subtracted from score. sectorProfile (ai_native/creator/crypto) opt-in reshuffles SV→0.16, X→0.20. Labels: THE_ROAR (≥80) / PROMISING (≥60) / EXPERIMENTAL (≥40) / WEAK_SIGNAL (<40).

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• compute_monetization

  Compute monetization_score (0-21) + label + has_pricing_anchors from pricing anchors + model tags + deal cycle hint.

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• compute_multi_source_tam

  Multi-source TAM consensus. Pass 2-3 sources of market-size text. Optional `estimateYear` per source — when supplied, the result includes yearRange and a hasStaleData flag (true if the span exceeds 5 years). Outliers are dropped by modified Z-score over the median absolute deviation when n≥4. Returns the extracted dollar amounts + consensus median + an agreement score 0..1, where 1 means every source lands within 20% of the median.

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• compute_ppc_spend_signal

  Wave 5 N.4 — compute ppc_spend_score (0-10) + label (STRONG/CONFIRMED/WEAK/ABSENT) + market_saturation from PPC traffic projection (avgCpcUsd, totalMonthlySpendUsd, optional competitorBidders + competition). Feed numbers from dataforseo_ad_traffic.

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• compute_search_velocity

  Compute search_velocity_score (0-25) from Trends timeline values + rising queries count + geo region count.

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• compute_search_velocity_v2

  Search velocity (0-25) v2 — canonical 0.40*volume + 0.30*trend + 0.20*intent + 0.10*geo. CRITICAL: externalVolumeNorm MUST come from external sources (Amazon BSR / app store installs / job-board postings) — NOT the Trends timeline (would double-count, since Trends is itself normalized 0-100 within window). trendNorm is derived internally from trendsTimelineValues. Trends peak<50 zeroes the trend component (Yotpo SEO floor). Optional daysSinceLastSignal applies exponential freshness decay (search half-life 90d).

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• compute_social_pain

  Compute social_pain_score (0-30) + total mentions + dominant perspective (business/consumer/trend/mixed).

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• compute_urgency_composite

  Compose composite_urgency_score (0-10) + badge (LOW/MEDIUM/HIGH/VERY_HIGH/EXTREME) from 3 sub-scores: news, pain, hiring.

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• compute_x_signal

  Compute x_signal_score (0-20) + recency share + positivity rate from X/Twitter mention counts.

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• derive_kill_criteria

  Derive a falsifiable, data-driven list of kill criteria from upstream signals — the outputs of validate_unit_economics and compute_dealbreakers_v2, plus an ICP drift count. Returns one row per rule with {rule, threshold, status, evidence?}, where status is tripped_now / monitor / cleared. Replaces prose kill criteria, which are tautologies that can never fire.

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• get_started

  What this server is, what it will do for you right now without an account, and what an account adds. Call this first if you have no API key — it answers in one round trip instead of sending you to a website.

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• validate_unit_economics

  Sanity-check a unit-economics row before publishing it in a business-model slide. Catches the math-drift class of failures (customers × ARPU ≠ revenue), enforces the LTV/CAC ≥ 1.5 floor, the cohort-positivity check, and CAC payback bounds. Returns {ok, errors[{rule, severity, detail}], derived{ratios}}. Skills MUST regenerate the row when ok=false (block-severity errors); warn-severity errors should be surfaced in the final report but do not gate publication. No LLM calls.

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Tool count and median score across every tool in this server’s corpus — honest in a way a single cherry-picked tool’s badge wouldn’t be.

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