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falsification-gauntlet

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Falsification Gauntlet

Adversarial stress-testing framework for product ideas. Goal: Kill weak ideas fast, refine promising ones, proceed only with conviction.

Core Principles

  1. Dialog, not checklist — Multi-turn conversation by design. The user must arrive at conclusions themselves.
  2. Socratic method — Surface questions, let user reason through. Never declare "this won't work"—guide them to see it.
  3. User expertise is material — Explicitly prompt for domain knowledge. User knows things Claude doesn't.
  4. Three outcomes — Kill (clean exit), Proceed (build with conviction), or Refine (thesis needs adjustment)
  5. Soft exit ramps — If a phase clearly invalidates the thesis, offer to skip ahead. User can override.

Workflow

Before Starting

Create a state file in the working directory. See references/templates.md for the state file template.

[idea-name]-gauntlet-state.md

Update this file after each phase.

Phase 1: Thesis Articulation

Goal: Crystallize the core bet and surface load-bearing assumptions.

Questions to explore:

  • What's the core bet in one sentence?
  • Who is the customer? What's the acute pain?
  • What has to be TRUE for this to work? (List 3-5 load-bearing assumptions)

User expertise prompt:

"Before we dig in—what's your unique insight here? Why do you believe this when others might not?"

Output: Create [idea-name]-phase1-thesis.md using template in references.


Phase 2: Moat Interrogation

Goal: Determine if there's a defensible advantage.

Questions to explore:

  • Why you? Why now?
  • What's the defensible advantage? (Network effects, data, switching costs, brand, regulatory, proprietary tech)
  • Can incumbents do this? Will they?
  • Can platforms (Google, Meta, Apple, Amazon) commoditize this?

User expertise prompt:

"Who do you already know is playing in this space or adjacent? What's your read on their trajectory?"

Soft exit ramp (if moat is clearly absent):

"Based on what we've found, there doesn't appear to be a defensible moat here. We can skip to Phase 5 (decision) unless you think there's value in continuing through technical feasibility and competitive landscape."

Output: Create [idea-name]-phase2-moat.md using template in references.


Phase 3: Technical Feasibility

Goal: Determine if we can actually build this.

Questions to explore:

  • Can we build this with current technology?
  • What's in our control vs. dependent on platforms/APIs/partners?
  • Are there hard technical blockers? (API limitations, data access, regulatory)
  • What's the build complexity? (Solo founder viable? Requires team?)

Research approach: Use web search to investigate API capabilities, platform constraints, technical precedents.

Soft exit ramp (if technically blocked):

"This appears to be technically blocked by [specific constraint]. We can skip to Phase 5 unless you see a path around this."

Output: Create [idea-name]-phase3-technical.md using template in references.


Phase 4: Competitive Landscape

Goal: Map who else is here and assess commoditization risk.

Questions to explore:

  • Who's already here? (Direct competitors)
  • Who's adjacent and could enter? (Platform risk, big tech, well-funded startups)
  • What's the trajectory? (Growing, consolidating, commoditizing)
  • Is there a timing window? (Why now vs. 2 years ago or 2 years from now)

User expertise prompt:

"Any adjacent players I should look at that might not show up in obvious searches?"

Research approach: Web search for competitors, funding news, platform announcements, market analysis.

Output: Create [idea-name]-phase4-competitive.md using template in references.


Phase 5: Decision

Goal: Synthesize findings and reach a decision with conviction.

Process:

  1. Summarize each load-bearing assumption and its status (Validated / Invalidated / Uncertain)
  2. Present the three options: Kill, Proceed, Refine
  3. Ask the user for their gut read
  4. Let them make the call

User expertise prompt:

"Given everything we've surfaced—what's your gut saying?"

Do NOT make the decision for the user. Present the evidence. Let them conclude.

Output: Create [idea-name]-phase5-decision.md using template in references.


State Management

After each phase:

  1. Update the state file with phase completion status
  2. Update load-bearing assumptions with current status
  3. Capture any key user expertise that surfaced

If context runs out mid-process, the state file + phase outputs enable resumption.

Phase Output Templates

See references/templates.md for all templates:

  • State file template
  • Phase 1-5 output templates

Source

git clone https://github.com/narainio/context-daemon/blob/main/skills/falsification-gauntlet/SKILL.mdView on GitHub

Overview

Falsification Gauntlet is an adversarial framework designed to rigorously test a product thesis before building. It helps you surface load-bearing assumptions, interrogate potential moats, assess technical feasibility, and map the competitive landscape. The process aims to kill weak ideas fast or refine them into stronger theses, guided by a Socratic, multi-turn approach rather than a checklist.

How This Skill Works

Work through defined phases with a state file in your working directory. The framework uses a dialog-driven, Socratic method to surface questions and avoid declaring that something won't work, instead guiding you toward conclusions. After each phase you get one of three outcomes: Kill, Proceed, or Refine, with soft exit ramps to skip ahead if a phase clearly invalidates the thesis.

When to Use It

  • When a user asks to stress-test an idea or validate a thesis before committing resources.
  • When you need to surface core bets, customer pains, and 3–5 load-bearing assumptions.
  • When you want to interrogate defensible moats and the competitive landscape.
  • When evaluating technical feasibility and potential build blockers.
  • When choosing to kill, refine, or proceed based on rigorous evaluation.

Quick Start

  1. Step 1: Create the state file named [idea-name]-gauntlet-state.md and articulate the core thesis in Phase 1.
  2. Step 2: Proceed through Phases 2 and 3 to interrogate moats and technical feasibility, documenting outputs in phase files.
  3. Step 3: Decide the outcome (Kill, Proceed, or Refine) and plan next steps, using soft exit ramps if needed.

Best Practices

  • Create a dedicated state file named [idea-name]-gauntlet-state.md and update after each phase.
  • Use the Socratic prompts to surface reasoning rather than delivering verdicts.
  • Explicitly prompt the user for domain expertise to ground the assessment.
  • Document phase outputs as [idea-name]-phaseX-....md templates for traceability.
  • Use soft exit ramps to skip phases when a thesis is clearly invalid or can be refined later.

Example Use Cases

  • An AI analytics startup evaluates data access and latency assumptions before building.
  • A hardware concept tests manufacturing feasibility and supplier risk for a new device.
  • A marketplace idea probes switching costs and network effects to assess moat.
  • A privacy tool maps regulatory constraints and data-access risks affecting feasibility.
  • A consumer app compares competing features to gauge commoditization risk.

Frequently Asked Questions

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