design-falsification-test

Specify a concrete observation, computation, intervention, counterexample, or measurement that would count against the sharpened claim, prioritizing cheap…

Skill by yogsoth-ai

IndexedApache-2.0★ 505

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About

Specify a concrete observation, computation, intervention, counterexample, or measurement that would count against the sharpened claim, prioritizing cheap high-information tests.

Use cases: design, testing

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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.

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codeload.github.com · found in install command

Files it touches

~/.claude/skills/design-falsification-test

What's inside

Skill name: design-falsification-test
Description: Specify a concrete observation, computation, intervention, counterexample, or measurement that would count against the sharpened claim, prioritizing cheap high-information tests.
License: Apache-2.0
Instructions: 29 lines in SKILL.md · view on GitHub

Files (1)

SKILL.md

SKILL.md (first lines)

# design-falsification-test
## Purpose
Specify a concrete observation, computation, intervention, counterexample, or measurement that could count against a claim.
## Input contract
```yaml
required: [sharp_claim, scope, candidate_observables]
optional: [cost, available_tools, competing_predictions]
constraints: [test must identify a failure condition and measurement rule]
```
## Procedure
1. Enumerate claim commitments and possible failure observations.
2. Rank tests by information value and cost.
3. 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
```yaml
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

From yogsoth-ai/de-anthropocentric-research-engine, Apache-2.0.

Signals

Stars: 505
Forks: 43
Open issues: 1
Last commit (this folder): Sep 23, 2026 (1 month ago)
Last release: Oct 23, 2026 (5 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License: Apache-2.0
Repository: github.com/yogsoth-ai/de-anthropocentric-research-engine
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

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design-falsification-test skill. Agent Mart. https://agentmart.dev/skills/yogsoth-ai-design-falsification-test (last updated 2026-10-24)