ideaudit

studio.inite由 inite.studio 发布官方发布者v1.1.0更新于 Oct 24, 2026

The scoring behind an audit allowed to say no. Twenty deterministic tools, offline, no account.

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概览

AI 生成的概览

面向创业点子审计的确定性评分工具：21 个纯数学函数，把市场、资金与信号输入换算成分数与 GO/KILL 结论。

可以做什么
  • 一次调用了解服务器用途以及账号能带来什么
  • 计算结论：dealbreakers v2、交叉原型矩阵、推导出的终止标准
  • 组合评分：LRS 综合分（v1 与 v2）、紧迫度综合分、12 项集合评分
  • 评估市场形态：进入壁垒、多来源 TAM 共识、搜索热度（v1 与 v2）、社交痛点
  • 评估资金面：单位经济校验、变现能力、预算证明、PPC 投放信号
  • 评估信号：融资势头、招聘需求、X/Twitter 信号、构建复杂度
适用场景
适合希望用透明、可复现的算术而非模型判断来给创业或产品点子打分，或想用自己的数据核对审计数值的场景。若只想验证算法本身，仅安装本地包即可。
运行要求
可通过 npx 从 npm 包 @inite/ideaudit-tools 以 stdio 在本地运行，也可使用 api.inite.studio 的托管 HTTP 端点。本地服务器无需账号、密钥、网络或模型调用。托管服务器需在请求头 Authorization 中提供来自服务商设置页的 Bearer 令牌；不带该头时端点以访客通道提供确定性评分工具。
安装前请注意
本地服务器不存储任何内容：传入 auditId 会被拒绝，结论只存在于对话中，之后无法重新打开。持久化、第三方市场数据与知识图谱需要托管服务器和账号。托管端点接受 Authorization Bearer 令牌，属于需妥善保管的凭据。评分质量取决于你提供的输入。

安装

在 SourceWeft 中

  1. 打开 控制台中的 ideaudit，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/studio.inite%2Fideaudit-tools/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "ideaudit": {
      "type": "http",
      "url": "https://api.inite.studio/mcp"
    }
  }
}

README

ideaudit-tools

The deterministic scoring behind ideaudit, as a local MCP server. No account, no key, no network, no model calls — just the arithmetic.

bash
claude mcp add ideaudit-tools -- npx -y @inite/ideaudit-tools

Listed in the MCP registry as studio.inite/ideaudit-tools, which carries both ways in: this package over stdio, and the hosted server over HTTP. Installing straight from this repository — npx -y github:inite-ai/ideaudit-mcp — also works and runs the same code.

Why this is published

The product's claim is that it is an audit allowed to say no. A verdict nobody can inspect is a verdict nobody has to believe, so the part that produces the number is open — including the number that says no. You can read how three lens scores become KILL, disagree with the thresholds, and argue about the arithmetic rather than about our word.

It is also the honest shape of the system. The server makes zero model calls; judgement lives in the skills, determinism lives in the tools. These are the tools.

What runs locally

Twenty tools, every one a pure function of its input:

The verdictcompute_dealbreakers_v2, compute_crossed_matrix, derive_kill_criteria
Composite scoringcompute_lrs_composite, compute_lrs_composite_v2, compute_urgency_composite, compute_collection_scores
Market shapecompute_barrier, compute_multi_source_tam, compute_search_velocity, compute_search_velocity_v2, compute_social_pain
Moneyvalidate_unit_economics, compute_monetization, compute_budget_proof, compute_ppc_spend_signal
Signalscompute_funding_momentum, compute_hiring_demand, compute_x_signal, compute_build_complexity

compute_dealbreakers_v2 is the one that decides. Stage-aware weights, confidence-weighted lens scores, and a deliberately risk-asymmetric verdict: GO needs a score ≥ 80 and zero red flags and average confidence ≥ 0.6, while the KILL gate is observer-invariant — a founder profile shifts every threshold above the gate, and none of them below it. You cannot buy a better answer by describing yourself more favourably. There is a test that says so.

Same input, same output as the hosted server — because it is the same specification object, imported rather than reimplemented. A test asserts that identity rather than comparing two copies, so the two cannot drift.

Where to look

Be told where the numbers are rather than hunt for them:

  • src/specs/*.ts — one file per tool: what it accepts, what it means, what it returns. compute-dealbreakers-v2.ts is the verdict.
  • src/server.ts — the whole server, including what it refuses.
  • src/__tests__/parity.test.ts — the claims above, as assertions. The observer-invariant KILL gate is checked against all five archetypes.
  • dist/bundle.js — the arithmetic, un-minified. computeBarrier is a dozen lines and every constant is visible: 0.6 off the score per direct competitor, 0.2 per adjacent, six points of range spent on SERP noise, and labels at 18 / 12 / 6. Disagree with those numbers, not with our word.

That last one is a build artefact and reads like one — the commented source of the maths lives in the monorepo this repository is mirrored from, and is not here. If you want the reasoning behind a threshold rather than the threshold itself, open an issue and ask; that is a cheaper conversation than either of us pretending.

What needs the hosted server

Not withheld to make a point — these genuinely need somewhere to keep things and someone to pay for them:

  • Persistence. Saving an audit, finalising it, reading it back, comparing two of them, the dashboard. A verdict that exists only in a chat transcript is one nobody can re-open or defend later.
  • Data. SERP, keyword volume, funding rounds, hiring signals, social mentions. Those are metered third-party APIs.
  • The knowledge graph. Entities, evidence, contradictions across audits.

That boundary is enforced rather than documented. The hosted compute_dealbreakers_v2 takes an auditId; this one refuses it, by name, instead of letting zod strip it silently and handing you a correct verdict plus the false belief that it had been written down.

$ compute_dealbreakers_v2 {"auditId": "…", …}{ "error": "unknown_arguments", "unknown": ["auditId"],  "message": "This server computes; it stores nothing. …" }

The hosted server

bash
claude mcp add ideaudit --transport http https://api.inite.studio/mcp \  --scope user --header "Authorization: Bearer $IDEAUDIT_API_KEY"

It serves these twenty tools and everything above them. Sign up at inite.studio; --scope user matters, or the registration is bound to one directory.

Using both

They compose. Register the local server for the arithmetic and the hosted one for everything else; tool names do not collide because the local set is a strict subset served under the same names, so whichever you register wins for those twenty and the rest resolve to the platform.

If you only want to check our maths against your own numbers, the local server alone is enough and always will be.

Contributing, and a warning about it

This repository is a projection. It is rebuilt and force-pushed from the monorepo that develops it every time the package changes, which means a pull request opened here would be overwritten by the next sync — through no fault of yours, and with no notification. Saying so is cheaper than letting someone find out by losing an afternoon.

Issues are the right channel and they are read: a threshold you think is wrong, a number you cannot reproduce, a tool that accepts something it should refuse. Bring the input you used — every tool here is deterministic, so a disagreement about an answer is always settleable.

Contribute with an AI agent

[Open in GitHub Codespaces]

You don't need to be an engineer. Open the repo in your coding agent and say /contribute (Claude Code) — or "read AGENTS.md and help me report …" in any other agent. It runs the tool on your input, finds the constant involved, and files an issue a maintainer can settle — an issue, not a pull request, for the reason above. AGENTS.md also explains why dist/ is committed and how node scripts/verify-dist.mjs checks it against src/.

Licence

Apache-2.0. See LICENSE.

来源：README.md，提交 822ac38

工具

21

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.

无需输入参数。

compute_barrier

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

输入参数serpNoisedirectCompetitorCount必填adjacentCompetitorCount

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.

输入参数hasNamedPricingpricingHitsCount必填reviewSiteHitsCountpurchaseIntentMentions

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.

输入参数externalApisCount必填integrationsCountstackComplexityTags

compute_collection_scores

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

输入参数analysisId必填enrichedData必填

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.

输入参数stage必填sectorobserverlensScores必填stageProbabilitieshasMajorContradictionunresolvedContradictions

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.

输入参数stage必填sectorobserverlensScores必填stageProbabilitieshasMajorContradictionunresolvedContradictions

compute_funding_momentum

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

输入参数hitsByTier必填recent30dHits

compute_hiring_demand

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

输入参数sites必填

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).

输入参数barrierScore必填xSignalScore必填sectorProfilesocialPainScore必填budgetProofScore必填monetizationScore必填searchVelocityScore必填buildComplexityPenalty

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.

输入参数barrierScore必填socialPainScore必填monetizationScore必填searchVelocityScore必填

compute_monetization

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

输入参数dealCyclemodelTagspricingAnchorsCount必填

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.

输入参数inputs必填

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.

输入参数avgCpcUsd必填competitioncompetitorBidderstotalMonthlySpendUsd必填

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).

输入参数intentNorm必填geoSpreadNorm必填externalVolumeNorm必填daysSinceLastSignaltrendsTimelineValues必填

compute_search_velocity

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

输入参数geoRegionCounttimelineValues必填risingQueriesCount

compute_social_pain

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

输入参数painMentions必填categoryCountsintentMentionsurgencyMentions

compute_urgency_composite

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

输入参数newsSignalScore必填painSignalScore必填hiringSignalScore必填

compute_x_signal

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

输入参数mentionsCount必填recent7dCountfounderMentionssentimentNegativesentimentPositive

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.

输入参数unitEcondealbreakersicpDriftCount

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.

输入参数cacltvarpu必填customers必填grossMarginmonthlyChurnannualRevenue必填

版本历史

1
  1. v1.1.0最新Sep 16, 2026

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