abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Gate a hypothesis on falsifiability, operational definition, and boundary conditions; can be entered from any hypothesis-generation path.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill falsifiability-audit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/falsifiability-auditContext preview
The summary Claude sees to decide when to auto-load this skill.
Gate a hypothesis on falsifiability, operational definition, and boundary conditions; can be entered from any hypothesis-generation path.
name: falsifiability-audit description: "Gate a hypothesis on falsifiability, operational definition, and boundary conditions; can be entered from any hypothesis-generation path."
Gate a hypothesis on falsifiability, operational definition, and boundary conditions; can be entered from any hypothesis-generation path.
required: [hypothesis, operational_definition, boundary_conditions] optional: [assumptions, prior_findings, evidence_updates] constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
1. You MUST load skill `evaluate-falsifiability` to evaluate the claim's reachable falsifiers. 2. You MUST load skill `operationalize-construct` to operationalize every construct used by the claim. 3. You MUST load skill `specify-boundaries` to state the scope and boundary conditions. If the audit exposes an ill-formed question, consider `formulate-research-question`. If the claim is ready for an empirical test, consider `design-experiment`. If a broader falsification program is required, `falsification-first-audit` may be the better next tactic.
Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.
produces: [falsifiability_verdict, operational_definition, boundary_conditions] delta_fields: [findings, decisions]
Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.
| source | criterion | treatment | |---|---|---| | resolved v3 entries above | node-specific criteria | retained and specialized to the v4 object contract | | experiment-execution/statistical-testing | $\alpha$ = 0.05 | fixed value retained where applicable | | experiment-execution/sample-size-estimation | power = 0.8 | fixed value retained where applicable |
Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
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, outcome, timeframe, system…
Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner. Perspective, target, escalation depth,…
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.