falsification-gauntlet
Scannednpx machina-cli add skill narainio/context-daemon/falsification-gauntlet --openclawFalsification Gauntlet
Adversarial stress-testing framework for product ideas. Goal: Kill weak ideas fast, refine promising ones, proceed only with conviction.
Core Principles
- Dialog, not checklist — Multi-turn conversation by design. The user must arrive at conclusions themselves.
- Socratic method — Surface questions, let user reason through. Never declare "this won't work"—guide them to see it.
- User expertise is material — Explicitly prompt for domain knowledge. User knows things Claude doesn't.
- Three outcomes — Kill (clean exit), Proceed (build with conviction), or Refine (thesis needs adjustment)
- 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:
- Summarize each load-bearing assumption and its status (Validated / Invalidated / Uncertain)
- Present the three options: Kill, Proceed, Refine
- Ask the user for their gut read
- 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:
- Update the state file with phase completion status
- Update load-bearing assumptions with current status
- 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
- Step 1: Create the state file named [idea-name]-gauntlet-state.md and articulate the core thesis in Phase 1.
- Step 2: Proceed through Phases 2 and 3 to interrogate moats and technical feasibility, documenting outputs in phase files.
- 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.