ideaudit

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.

{
  "type": "object",
  "properties": {},
  "required": []
}

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

{
  "type": "object",
  "properties": {
    "directCompetitorCount": {
      "type": "integer",
      "minimum": 0
    },
    "adjacentCompetitorCount": {
      "type": "integer",
      "minimum": 0,
      "default": 0
    },
    "serpNoise": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "default": 0
    }
  },
  "required": [
    "directCompetitorCount"
  ]
}

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

{
  "type": "object",
  "properties": {
    "pricingHitsCount": {
      "type": "integer",
      "minimum": 0
    },
    "reviewSiteHitsCount": {
      "type": "integer",
      "minimum": 0
    },
    "purchaseIntentMentions": {
      "type": "integer",
      "minimum": 0
    },
    "hasNamedPricing": {
      "type": "boolean"
    }
  },
  "required": [
    "pricingHitsCount"
  ]
}

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.

{
  "type": "object",
  "properties": {
    "externalApisCount": {
      "type": "integer",
      "minimum": 0
    },
    "stackComplexityTags": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "integrationsCount": {
      "type": "integer",
      "minimum": 0
    }
  },
  "required": [
    "externalApisCount"
  ]
}

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

{
  "type": "object",
  "properties": {
    "analysisId": {
      "type": "string"
    },
    "enrichedData": {
      "type": "object",
      "description": "EnrichedData with canonical_idea signals."
    }
  },
  "required": [
    "analysisId",
    "enrichedData"
  ]
}

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.

{
  "type": "object",
  "properties": {
    "stage": {
      "type": "string",
      "enum": [
        "idea",
        "mvp",
        "seed",
        "series_a_plus"
      ]
    },
    "sector": {
      "type": "string"
    },
    "lensScores": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "lens",
          "score",
          "confidence"
        ],
        "properties": {
          "lens": {
            "type": "string",
            "enum": [
              "team",
              "problem_solution",
              "traction",
              "competition",
              "gtm",
              "finance"
            ]
          },
          "score": {
            "type": "number",
            "minimum": 0,
            "maximum": 100
          },
          "confidence": {
            "type": "number",
            "minimum": 0,
            "maximum": 1
          },
          "redFlag": {
            "type": "boolean"
          }
        }
      }
    },
    "stageProbabilities": {
      "type": "object",
      "properties": {
        "idea": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "mvp": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "seed": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "series_a_plus": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        }
      }
    },
    "unresolvedContradictions": {
      "type": "integer",
      "minimum": 0,
      "default": 0
    },
    "hasMajorContradiction": {
      "type": "boolean",
      "default": false
    },
    "observer": {
      "type": "object",
      "description": "Founder profile that crosses with the substrate idea to produce an observer-relative verdict. When omitted, only the substrate verdict is returned.",
      "required": [
        "founder_type"
      ],
      "properties": {
        "founder_type": {
          "type": "string",
          "enum": [
            "solo",
            "cofounded_technical",
            "cofounded_business",
            "domain_expert",
            "serial"
          ]
        },
        "runway_months": {
          "type": "integer",
          "minimum": 0,
          "maximum": 60
        },
        "capital_usd_band": {
          "type": "string",
          "enum": [
            "under_50k",
            "50k_500k",
            "500k_5m",
            "over_5m"
          ]
        },
        "risk_tolerance": {
          "type": "string",
          "enum": [
            "conservative",
            "moderate",
            "aggressive"
          ]
        },
        "expertise_sectors": {
          "type": "array",
          "maxItems": 8,
          "items": {
            "type": "string",
            "minLength": 2,
            "maxLength": 80
          }
        },
        "time_horizon_years": {
          "type": "integer",
          "minimum": 1,
          "maximum": 15
        },
        "exit_goal": {
          "type": "string",
          "enum": [
            "lifestyle",
            "acquisition",
            "ipo",
            "unicorn"
          ]
        }
      }
    }
  },
  "required": [
    "stage",
    "lensScores"
  ]
}

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.

{
  "type": "object",
  "properties": {
    "stage": {
      "type": "string",
      "enum": [
        "idea",
        "mvp",
        "seed",
        "series_a_plus"
      ]
    },
    "sector": {
      "type": "string"
    },
    "lensScores": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "lens",
          "score",
          "confidence"
        ],
        "properties": {
          "lens": {
            "type": "string",
            "enum": [
              "team",
              "problem_solution",
              "traction",
              "competition",
              "gtm",
              "finance"
            ]
          },
          "score": {
            "type": "number",
            "minimum": 0,
            "maximum": 100
          },
          "confidence": {
            "type": "number",
            "minimum": 0,
            "maximum": 1
          },
          "redFlag": {
            "type": "boolean"
          }
        }
      }
    },
    "stageProbabilities": {
      "type": "object",
      "properties": {
        "idea": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "mvp": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "seed": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        },
        "series_a_plus": {
          "type": "number",
          "minimum": 0,
          "maximum": 1
        }
      }
    },
    "unresolvedContradictions": {
      "type": "integer",
      "minimum": 0,
      "default": 0
    },
    "hasMajorContradiction": {
      "type": "boolean",
      "default": false
    },
    "observer": {
      "type": "object",
      "description": "Founder profile that crosses with the substrate idea to produce an observer-relative verdict. When omitted, only the substrate verdict is returned.",
      "required": [
        "founder_type"
      ],
      "properties": {
        "founder_type": {
          "type": "string",
          "enum": [
            "solo",
            "cofounded_technical",
            "cofounded_business",
            "domain_expert",
            "serial"
          ]
        },
        "runway_months": {
          "type": "integer",
          "minimum": 0,
          "maximum": 60
        },
        "capital_usd_band": {
          "type": "string",
          "enum": [
            "under_50k",
            "50k_500k",
            "500k_5m",
            "over_5m"
          ]
        },
        "risk_tolerance": {
          "type": "string",
          "enum": [
            "conservative",
            "moderate",
            "aggressive"
          ]
        },
        "expertise_sectors": {
          "type": "array",
          "maxItems": 8,
          "items": {
            "type": "string",
            "minLength": 2,
            "maxLength": 80
          }
        },
        "time_horizon_years": {
          "type": "integer",
          "minimum": 1,
          "maximum": 15
        },
        "exit_goal": {
          "type": "string",
          "enum": [
            "lifestyle",
            "acquisition",
            "ipo",
            "unicorn"
          ]
        }
      }
    }
  },
  "required": [
    "stage",
    "lensScores"
  ]
}

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

{
  "type": "object",
  "properties": {
    "hitsByTier": {
      "type": "object",
      "properties": {
        "tier_1": {
          "type": "integer",
          "minimum": 0
        },
        "presswire": {
          "type": "integer",
          "minimum": 0
        },
        "regional": {
          "type": "integer",
          "minimum": 0
        },
        "vertical": {
          "type": "integer",
          "minimum": 0
        }
      }
    },
    "recent30dHits": {
      "type": "integer",
      "minimum": 0
    }
  },
  "required": [
    "hitsByTier"
  ]
}

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

{
  "type": "object",
  "properties": {
    "sites": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "domain": {
            "type": "string"
          },
          "hits": {
            "type": "integer",
            "minimum": 0
          },
          "priority": {
            "type": "integer",
            "enum": [
              1,
              2,
              3
            ]
          }
        },
        "required": [
          "domain",
          "hits",
          "priority"
        ]
      }
    }
  },
  "required": [
    "sites"
  ]
}

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).

{
  "type": "object",
  "properties": {
    "searchVelocityScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 25
    },
    "socialPainScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 30
    },
    "barrierScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 24
    },
    "monetizationScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 21
    },
    "xSignalScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 20
    },
    "budgetProofScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 10
    },
    "buildComplexityPenalty": {
      "type": "number",
      "minimum": 0,
      "maximum": 10
    },
    "sectorProfile": {
      "type": "string",
      "enum": [
        "default",
        "ai_native",
        "creator",
        "crypto"
      ],
      "description": "Opt-in sector weight override. Default uses Python canonical weights."
    }
  },
  "required": [
    "searchVelocityScore",
    "socialPainScore",
    "barrierScore",
    "monetizationScore",
    "xSignalScore",
    "budgetProofScore"
  ]
}

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.

{
  "type": "object",
  "properties": {
    "searchVelocityScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 25
    },
    "socialPainScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 30
    },
    "barrierScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 24
    },
    "monetizationScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 21
    }
  },
  "required": [
    "searchVelocityScore",
    "socialPainScore",
    "barrierScore",
    "monetizationScore"
  ]
}

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

{
  "type": "object",
  "properties": {
    "pricingAnchorsCount": {
      "type": "integer",
      "minimum": 0
    },
    "modelTags": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "e.g. [\"subscription\",\"usage\",\"marketplace\"]"
    },
    "dealCycle": {
      "type": "string",
      "description": "instant/days/weeks/months/quarters"
    }
  },
  "required": [
    "pricingAnchorsCount"
  ]
}

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.

{
  "type": "object",
  "properties": {
    "inputs": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "source",
          "text"
        ],
        "properties": {
          "source": {
            "type": "string"
          },
          "text": {
            "type": "string"
          },
          "estimateYear": {
            "type": "integer",
            "minimum": 1990,
            "maximum": 2100,
            "description": "Optional: year the estimate was published."
          }
        }
      }
    }
  },
  "required": [
    "inputs"
  ]
}

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.

{
  "type": "object",
  "properties": {
    "avgCpcUsd": {
      "type": "number",
      "minimum": 0
    },
    "totalMonthlySpendUsd": {
      "type": "number",
      "minimum": 0
    },
    "competitorBidders": {
      "type": "integer",
      "minimum": 0
    },
    "competition": {
      "type": "number",
      "minimum": 0,
      "maximum": 1
    }
  },
  "required": [
    "avgCpcUsd",
    "totalMonthlySpendUsd"
  ]
}

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).

{
  "type": "object",
  "properties": {
    "trendsTimelineValues": {
      "type": "array",
      "items": {
        "type": "number",
        "minimum": 0,
        "maximum": 100
      },
      "description": "Monthly Trends values 0-100. Used ONLY to derive trendNorm — never as raw volume."
    },
    "externalVolumeNorm": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Normalized 0-1 demand volume from EXTERNAL sources (Amazon, app stores, jobs). Caller normalizes before passing."
    },
    "intentNorm": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "0-1 commercial/transactional intent ratio."
    },
    "geoSpreadNorm": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "0-1 geographic spread (regions with interest > threshold)."
    },
    "daysSinceLastSignal": {
      "type": "number",
      "minimum": 0,
      "description": "Optional: days since most recent confirming signal. Triggers exponential freshness decay (half-life 90d)."
    }
  },
  "required": [
    "trendsTimelineValues",
    "externalVolumeNorm",
    "intentNorm",
    "geoSpreadNorm"
  ]
}

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

{
  "type": "object",
  "properties": {
    "timelineValues": {
      "type": "array",
      "items": {
        "type": "number",
        "minimum": 0,
        "maximum": 100
      },
      "description": "Monthly Trends values 0-100 (e.g. last 10-12 months)."
    },
    "risingQueriesCount": {
      "type": "integer",
      "minimum": 0
    },
    "geoRegionCount": {
      "type": "integer",
      "minimum": 0
    }
  },
  "required": [
    "timelineValues"
  ]
}

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

{
  "type": "object",
  "properties": {
    "painMentions": {
      "type": "integer",
      "minimum": 0
    },
    "intentMentions": {
      "type": "integer",
      "minimum": 0,
      "default": 0
    },
    "urgencyMentions": {
      "type": "integer",
      "minimum": 0,
      "default": 0
    },
    "categoryCounts": {
      "type": "object",
      "properties": {
        "business": {
          "type": "integer",
          "minimum": 0
        },
        "consumer": {
          "type": "integer",
          "minimum": 0
        },
        "trend": {
          "type": "integer",
          "minimum": 0
        }
      }
    }
  },
  "required": [
    "painMentions"
  ]
}

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

{
  "type": "object",
  "properties": {
    "newsSignalScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 10
    },
    "painSignalScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 10
    },
    "hiringSignalScore": {
      "type": "number",
      "minimum": 0,
      "maximum": 10
    }
  },
  "required": [
    "newsSignalScore",
    "painSignalScore",
    "hiringSignalScore"
  ]
}

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

{
  "type": "object",
  "properties": {
    "mentionsCount": {
      "type": "integer",
      "minimum": 0
    },
    "recent7dCount": {
      "type": "integer",
      "minimum": 0
    },
    "sentimentPositive": {
      "type": "integer",
      "minimum": 0
    },
    "sentimentNegative": {
      "type": "integer",
      "minimum": 0
    },
    "founderMentions": {
      "type": "integer",
      "minimum": 0
    }
  },
  "required": [
    "mentionsCount"
  ]
}

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.

{
  "type": "object",
  "properties": {
    "unitEcon": {
      "type": "object",
      "description": "The result of validate_unit_economics."
    },
    "dealbreakers": {
      "type": "object",
      "description": "The result of compute_dealbreakers_v2."
    },
    "icpDriftCount": {
      "type": "integer",
      "minimum": 0
    }
  },
  "required": []
}

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.

{
  "type": "object",
  "properties": {
    "customers": {
      "type": "number"
    },
    "arpu": {
      "type": "number"
    },
    "annualRevenue": {
      "type": "number"
    },
    "monthlyChurn": {
      "type": "number"
    },
    "cac": {
      "type": "number"
    },
    "ltv": {
      "type": "number"
    },
    "grossMargin": {
      "type": "number"
    }
  },
  "required": [
    "customers",
    "arpu",
    "annualRevenue"
  ]
}