abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Specify a concrete observation, computation, intervention, counterexample, or measurement that would count against the sharpened claim, prioritizing cheap high-information tests.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-falsification-test --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-falsification-test -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-falsification-test -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-falsification-test -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-falsification-test -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-falsification-test -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-falsification-test -a windsurf -y
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."
Specify a concrete observation, computation, intervention, counterexample, or measurement that could count against a claim.
required: [sharp_claim, scope, candidate_observables] optional: [cost, available_tools, competing_predictions] constraints: [test must identify a failure condition and measurement rule]
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.
produces: [falsification_test, failure_condition, measurement_plan, priority_rationale] delta_fields: [findings, evidence_updates, uncertainties, decisions]
Do not design a test whose acceptance criterion is copied from the claim.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Evaluate competing arguments against stated criteria and produce a reasoned verdict with uncertainty.
Move a scientific object up/down in abstraction or narrow/broaden selected scope dimensions (population, mechanism, context,…
Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner. Perspective, target,…
Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.
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