abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Trace supposedly independent evidence/reasoning paths to shared priors, datasets, models, prompts, framings, assumptions, or upstream sources that induce correlated errors.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill identify-shared-priors --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/identify-shared-priorsContext preview
The summary Claude sees to decide when to auto-load this skill.
Trace supposedly independent evidence/reasoning paths to shared priors, datasets, models, prompts, framings, assumptions, or upstream sources that induce correlated errors.
name: identify-shared-priors description: "Trace supposedly independent evidence/reasoning paths to shared priors, datasets, models, prompts, framings, assumptions, or upstream sources that induce correlated errors."
Trace supposedly independent paths to shared priors, datasets, models, prompts, framings, assumptions, or upstream sources.
required: [evidence_paths, provenance_records] optional: [prompt_records, model_records, upstream_sources] constraints: [shared dependency identity and path membership must be explicit]
1. Inventory each path's inputs and assumptions. 2. Match shared priors, data, models, prompts, framing, and upstream evidence. 3. Produce dependency clusters and unresolved provenance.
If shared priors and dependence groups are explicit, consider `estimate-effective-evidence-count` as the next tactic.
produces: [shared_prior_map, dependency_clusters, unresolved_links] delta_fields: [findings, evidence_updates, uncertainties, open_questions]
Mark provenance unknown rather than inferring common origin from naming similarity.
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.