IdeaGauntlet

IdeaGauntlet

Stress-test product ideas before you build them.

IdeaGauntlet is an open-source CLI and library that turns a raw product idea into adversarial critique, multi-role debate, synthetic user objections, validation plans, and idea comparison.

Built for founders, indie hackers, and product engineers who want sharper pre-validation before spending weeks building the wrong thing.

Most AI tools help you generate more ideas. IdeaGauntlet helps you survive the one you already have.

One command gives you a verdict, an evidence-backed scorecard, the risks that actually kill the idea, and the cheapest test that could disprove it this week.

npx idea-gauntlet quick "an AI agent that attends your meetings for you"

Install & Update

# Install
npm install -g idea-gauntlet

# Update to latest release
npm install -g idea-gauntlet@latest

On global install, IdeaGauntlet performs best-effort integration setup for detected Claude Code, Codex, Cursor, and MCP-compatible clients. All writes are non-destructive (never overwriting your files) and fully reversible via idea-gauntlet uninstall. The install downloads no browser and runs no network install; if postinstall was skipped (e.g. --ignore-scripts), run idea-gauntlet install yourself.

Try it

Inside Claude Code / Codex / Cursor

After installing, open your AI coding tool and ask:

Use IdeaGauntlet court mode to stress-test this idea:

A focus-room app for remote workers that pairs people into silent 50-minute work sessions.

No IdeaGauntlet API key is needed. The AI coding tool supplies the model and context. If postinstall did not detect your tool, run idea-gauntlet install later.

Important: Agent-native integrations execute workflows natively. They do not run the idea-gauntlet CLI first. If you type idea-gauntlet court "..." in chat, the assistant treats it as analysis intent, not a shell command.

In the terminal — 30 seconds, no install

One command, nothing to install globally — just an API key:

ANTHROPIC_API_KEY=sk-ant-... npx idea-gauntlet quick "A focus-room app for remote workers"

Groq (GROQ_API_KEY=gsk_...), any OpenAI-compatible endpoint, or local Ollama (--ollama) work too — see Provider setup.

Get a card you can actually post

Add --format card to any command to render the verdict as a self-contained 1200×630 image (OG / Twitter preview size) — verdict badge, overall score, score radar, top risks, and the one-line brutal takeaway. Built to screenshot and share:

idea-gauntlet quick "Your idea" --format card -o idea.html   # writes idea.card.html

Full details in Shareable Report Card.

See a real report

Two example reports for examples/IDEA.md, generated by IdeaGauntlet itself (agent-native mode — the AI is the model):

• examples/sample-quick-report.md — fast critique
• examples/sample-court-report.md — full multi-role debate (steelman → skeptics → verdict)

A taste of the Quick verdict on that idea:

🔪 Focusmate has done exactly this since 2016 — "FocusRoom" as described is a feature, not a company; without a niche it can't defend, you're volunteering to fight a funded incumbent with a worse version of their product.

Core features

Synthetic users are fictional — not research evidence. Scores are diagnostic signals, not predictions.

Two things IdeaGauntlet refuses to fake

1. It tells you which scores are evidence, and which are guesses

Most AI analysis tools present a retrieved fact and a confident guess in the same font. IdeaGauntlet separates them. Every scored dimension is labelled:

A model cannot mark its own work as sourced. It is asked to cite the sources it used, and those citations are then checked against the research that was actually retrieved. A citation that isn't there is stripped, flagged as unverified in the report, and the dimension drops to judgment. Verification can only ever downgrade a score's basis, never promote it.

Each report then states the ratio plainly:

Evidence-backed: 1/3 scored dimensions ●○○

1 of 3 scored dimensions cite a source found in the research brief; 2 rest on model judgment alone. 1 claimed citation was not found in the brief and was not counted as evidence.

When no research is available at all, every dimension is judgment — the report says so instead of implying grounding it doesn't have.

This applies to both quick and court. Court reaches its scores through a seven-role debate rather than a single rubric call, but the judge's citations go through exactly the same verification before any dimension is allowed to read as evidence-backed.

2. It tells you what would prove the idea wrong, and what that costs

A verdict you can't act on is entertainment. assumptions turns the idea into a falsification plan — what must be true, the number that declares it dead, and the cheapest way to find out:

idea-gauntlet assumptions "A focus-room app for remote workers"

Resolving every fatal assumption: 10 days, $50.

Two design decisions make this useful rather than decorative:

• Kill thresholds must name a number. "Low signup rate" is not a threshold; "fewer than 8 of 40 visitors leave an email" is. Deciding what counts as failure before running the test is the whole point — it is how you avoid talking yourself past a bad result.
• The test order is computed, not asked for. Ordering is criticality × uncertainty × cheapness, calculated in code. Models are agreeable about ranking and tend to echo whatever they listed first; arithmetic at least stays consistent.

If the model returns something unusable, the command fails instead of inventing thresholds — a ledger of made-up numbers is worse than no ledger.

Quick start examples

# 1. Quick critique
idea-gauntlet quick "A focus-room app for remote workers"

# 2. Go-to-Market strategy
idea-gauntlet gtm "A focus-room app" --budget "$5000" --timeline "90 days"

# 3. Deep competitive intelligence
idea-gauntlet compete "A focus-room app" --depth deep

# 4. Revenue model exploration & pricing tiers
idea-gauntlet revenue "A focus-room app"

# 5. Founder-market fit evaluation
idea-gauntlet founder "A focus-room app" --background "8 years distributed engineer, worked remotely for 3 years"

# 6. AI-simulated customer discovery interview
idea-gauntlet interview "A focus-room app" --persona "Senior Remote Engineer"

# 7. Investor pitch deck draft (YC, Sequoia, a16z)
idea-gauntlet pitch "A focus-room app" --template yc --company "FocusGauntlet"

# 8. Idea evolution & pivot coaching
idea-gauntlet evolve "A focus-room app" --focus monetization

# 9. Regulatory & compliance scanner
idea-gauntlet compliance "A focus-room app" --regions us,eu

# 10. Multi-model ensemble consensus
idea-gauntlet ensemble "A focus-room app" --models "gpt-4o,claude-sonnet-5"

# 11. Trend monitoring (re-research saved ideas)
idea-gauntlet trend <saved-idea-id>

# 12. Court mode (save to file)
idea-gauntlet court "Your idea" --output report.md

# 13. Compare multiple ideas
idea-gauntlet compare "Idea A" "Idea B"

# 14. Export HTML report
idea-gauntlet quick "Your idea" --format html --output report.html

# 15. Batch mode (one idea per line in file)
idea-gauntlet batch ideas.txt --mode quick --output reports/

# 16. Interactive REPL
idea-gauntlet interactive "Your idea"

Agent-native integrations

IdeaGauntlet installs instructions for supported coding tools:

# Rerun integration setup if postinstall skipped a tool
idea-gauntlet install

Use natural language:

Use IdeaGauntlet court mode and focus on distribution risk:
...

Evidence-aware analysis

Inside tools that provide web/search access, IdeaGauntlet agent-native court mode may perform a brief market evidence scan before the debate. It uses this research to build a research brief, competitor landscape, evidence gaps, and source notes before the judge verdict.

Terminal CLI mode does not guarantee live web browsing. It uses the configured provider and any context you provide.

Provider setup

Direct CLI and MCP generation require a provider. Agent-native workflows do not.

Anthropic Claude (Native):

Simply set your Anthropic API key, or provide a key with the prefix sk-ant- as IDEAGAUNTLET_API_KEY:

export ANTHROPIC_API_KEY="your-anthropic-key"
# Optional model override (default: claude-sonnet-5)
export IDEAGAUNTLET_MODEL="claude-sonnet-5"

Groq (Native):

Set your Groq API key, or provide a key with the prefix gsk_ as IDEAGAUNTLET_API_KEY:

export GROQ_API_KEY="your-groq-key"
# Optional model override (default: llama-3.3-70b-versatile)
export IDEAGAUNTLET_MODEL="llama-3.3-70b-versatile"

OpenAI-compatible:

export IDEAGAUNTLET_API_KEY="your-key"
export IDEAGAUNTLET_BASE_URL="https://api.openai.com/v1"
export IDEAGAUNTLET_MODEL="<your-model>"

Local Ollama:

ollama serve
idea-gauntlet quick "Your idea" --ollama --model llama3

Supports OpenAI, OpenRouter, Groq, Anthropic Claude, Together, Fireworks, LM Studio, LocalAI.

Command reference

Common options

TypeScript API

import {
  runGauntlet,
  runGTMEngine,
  runCompetitiveIntelligenceEngine,
  runRevenueModelEngine,
  runFounderMarketFitEngine,
  runUserInterviewEngine,
  runPitchGenerator,
  runEvolutionCoach,
  runComplianceScanner,
  runMarketSizingEngine,
  runUnitEconomicsEngine,
  runAntiPatternDetector,
  ClaudeProvider,
  OpenAICompatibleProvider,
} from "idea-gauntlet";

const provider = new ClaudeProvider({
  apiKey: process.env.ANTHROPIC_API_KEY!,
  model: "claude-sonnet-5",
});

// Run standard gauntlet critique
const report = await runGauntlet({
  idea: "A focus-room app for remote workers",
  mode: "quick",
  provider,
});

// Run GTM and Strategy engines
const gtm = await runGTMEngine({ idea: "A focus-room app" }, provider);
const compIntel = await runCompetitiveIntelligenceEngine({ idea: "A focus-room app" }, provider);
const revenue = await runRevenueModelEngine({ idea: "A focus-room app" }, provider);
const founderFit = await runFounderMarketFitEngine({ idea: "A focus-room app" }, provider, "8 yrs full-stack engineer");

// Run Discovery & Venture engines
const interview = await runUserInterviewEngine({ idea: "A focus-room app" }, provider);
const pitch = await runPitchGenerator({ idea: "A focus-room app" }, provider, { template: "yc" });
const evolution = await runEvolutionCoach({ idea: "A focus-room app" }, provider);
const compliance = await runComplianceScanner({ idea: "A focus-room app" }, provider);

console.log(report.markdown);

Custom providers implement the LLMProvider interface.

Scoring philosophy

Scores are diagnostic signals, not predictions.

Evidence scores stay low unless you provide real validation evidence.

Visualization and HTML export

Generate a styled, self-contained HTML report with radar chart and Mermaid diagrams:

idea-gauntlet quick "Your idea" --format html -o report.html
idea-gauntlet court "Your idea" --format html -o report.html

The HTML report includes:

• Radar chart — 7-dimension score visualization (pure SVG, no dependencies)
• Mermaid diagrams — MVP flowchart, timeline Gantt, court mindmap (rendered via CDN)
• Dark-mode design — styled CSS, glassmorphism header, responsive layout

Shareable Report Card

Generate a single, self-contained 1200×630 card (OG / Twitter preview size) built to screenshot and share — verdict badge, overall score, score radar, top risks, and the one-line brutal takeaway:

idea-gauntlet quick "Your idea" --format card -o idea.html   # writes idea.card.html
idea-gauntlet court "Your idea" --format card -o idea.html

Open the file, screenshot it, and post it. The card carries the tool name and install line, so every share is a link back.

Use in CI (GitHub Action)

Run IdeaGauntlet automatically on every pull request that touches IDEA.md and post the verdict as a single, auto-updating PR comment — idea review as part of your workflow, like code review.

# .github/workflows/idea-gauntlet.yml
name: IdeaGauntlet
on:
  pull_request:
    paths: ["IDEA.md"]
permissions:
  contents: read
  pull-requests: write
jobs:
  critique:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - id: gauntlet
        uses: Thuong180702/IdeaGauntlet@v1   # pin to a released tag
        with:
          idea-file: IDEA.md
          mode: court                         # or 'quick' to save tokens
          api-key: ${{ secrets.IDEAGAUNTLET_API_KEY }}
      - uses: actions/github-script@v7
        if: steps.gauntlet.outputs.skipped != 'true'
        env:
          REPORT_FILE: ${{ steps.gauntlet.outputs.report-file }}
        with:
          script: |
            const fs = require('fs');
            const MARKER = '';
            const body = MARKER + '\n🤖 **IdeaGauntlet** stress-tested this idea:\n\n' + fs.readFileSync(process.env.REPORT_FILE, 'utf8');
            const { owner, repo } = context.repo;
            const issue_number = context.issue.number;
            const { data: comments } = await github.rest.issues.listComments({ owner, repo, issue_number });
            const existing = comments.find((c) => c.body && c.body.includes(MARKER));
            if (existing) await github.rest.issues.updateComment({ owner, repo, comment_id: existing.id, body });
            else await github.rest.issues.createComment({ owner, repo, issue_number, body });

A copy-pasteable version lives at .github/workflows/example-idea-gauntlet.yml. Add your key as a repo secret named IDEAGAUNTLET_API_KEY (Anthropic sk-ant-… or Groq gsk_…) — it is passed via env and never logged.

Interactive mode

Refine ideas iteratively in a REPL:

idea-gauntlet interactive "Your idea"

Commands:

Score benchmarking

Compare your scores against a synthetic reference set of 50 idea archetypes with illustrative (hand-authored, not measured) outcomes and scores. This gives rough distributional context — it is not real-company data and must not be read as a prediction.

In interactive mode, run /benchmark after analysis to see:

• Per-dimension percentile ranking
• Overall percentile
• Similar ideas from the benchmark dataset
• Outcome distribution of similar ideas

Benchmark is a directional guide, not a prediction. Dataset is small and retrospective.

Batch mode

Run critique on multiple ideas from a text file (one idea per line):

idea-gauntlet batch ideas.txt --mode quick --output reports/

Outputs individual reports to the specified directory, or prints all to stdout.

History and evolution tracking

Save reports and track how scores change as you iterate:

# Save a report (use --save flag on any command)
idea-gauntlet quick "Your idea" --save

# List all saved reports
idea-gauntlet history

# View a specific report
idea-gauntlet history <id>

# Compare score deltas between two saved reports
idea-gauntlet history <new-id> --evolve <old-id>

Interactive Court Defense

When running in Interactive mode, you can defend your idea against skeptics in Court mode:

1. Start interactive mode: idea-gauntlet interactive "Your idea"
2. Set mode to court: /mode court
3. Add a defense argument: /defend "We bypass this distribution risk by partnering with key industry platforms directly."
4. Run court analysis: /run
5. The Judge and Skeptics will dynamically process your defense, debate it, and re-calibrate the scorebars in the report.
6. Clear defenses at any time: /clear-defenses

Custom court roles

Load custom roles from a JSON file for court mode:

idea-gauntlet court "Your idea" --roles my-roles.json

Role file format:

[
  {
    "roleName": " distribution skeptic",
    "perspective": "Question how this reaches users without paid acquisition."
  }
]

Optional project-local setup

Global install is the normal path. To commit IdeaGauntlet instructions into a specific repo:

idea-gauntlet setup --all

Dry run: idea-gauntlet setup --dry-run --all

Maintenance

# Check global install status
idea-gauntlet status

# Rerun integration setup
idea-gauntlet install

# Remove integrations before uninstalling
idea-gauntlet uninstall
npm uninstall -g idea-gauntlet

Development

npm install
npm run typecheck
npm run test
npm run build
npm pack --dry-run

License

MIT