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Falsification gauntlet

Skill narainio/context-daemon/skills/falsification-gauntlet

Adversarial stress-testing framework for product ideas. Use when the user wants to rigorously evaluate a product thesis before building—identifying load-bearing assumptions, interrogating moats, testing technical feasibility, and mapping competitive landscape. Designed to KILL weak ideas early or REFINE them into stronger theses. Triggers on phrases like "stress-test this idea", "should I build this", "kill list", "validate my thesis", "is this worth building", or when a user presents a product concept and wants rigorous evaluation before committing resources.From its SKILL.md

Install
npx -y skills add narainio/context-daemon --skill falsification-gauntlet

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SKILL.md

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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

What ships with it: 1 file

5.3 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most test skills give in ~1.1k tokens

Counted across 964 of the 1,571 authors here whose files we hold, read 2026-08-07

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Said here and by no other author read

  • maintain a state file across phases
  • prompt the user for domain expertise
  • guide the user using socratic questioning
  • crystallize the core bet and assumptions in phase one
  • interrogate defensible advantage in phase two
  • test technical feasibility in phase three

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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