design-falsification-test
SkillMediaLets your agent design a concrete test that could disprove its own claim.
Use design-falsification-test in Claude, ChatGPT or Ahel Desktop
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Then ask your AI: use the design-falsification-test skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
About this skill
Specify a concrete observation, computation, intervention, counterexample, or measurement that would count against the sharpened claim, prioritizing cheap high-information tests.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/design-falsification-test/SKILL.md and read by Ahel’s review.
Purpose
Specify a concrete observation, computation, intervention, counterexample, or measurement that could count against a claim.
Input contract
required: [sharp_claim, scope, candidate_observables]
optional: [cost, available_tools, competing_predictions]
constraints: [test must identify a failure condition and measurement rule]
Procedure
- Enumerate claim commitments and possible failure observations.
- Rank tests by information value and cost.
- Specify the cheapest decisive test with pass/fail interpretation.
If the falsification test has an executable intervention and verdict rule, consider execute-probe as the next tactic.
Output contract
produces: [falsification_test, failure_condition, measurement_plan, priority_rationale]
delta_fields: [findings, evidence_updates, uncertainties, decisions]
Quality gates
- Test must be capable of producing evidence against the claim, not only confirming examples.
Failure and counterexamples
Do not design a test whose acceptance criterion is copied from the claim.
Provenance map
- resolved: red-team-truthseeking
Signals
- GitHub stars
- 503
- Forks
- 42
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
design-falsification-test- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine