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Skill

/identify-load-bearing-factors

Identify factors/assumptions/uncertainties whose change most strongly controls the conclusion.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill identify-load-bearing-factors --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/identify-load-bearing-factors

Context preview

The summary Claude sees to decide when to auto-load this skill.

Identify factors/assumptions/uncertainties whose change most strongly controls the conclusion.

SKILL.md

identify-load-bearing-factors.SKILL.md
name: identify-load-bearing-factors
description: "Identify factors/assumptions/uncertainties whose change most strongly controls the conclusion."

identify-load-bearing-factors

Purpose

Identify factors or assumptions whose change most strongly controls the conclusion.

Input contract

required: [conclusion, factor_or_assumption_set, perturbation_evidence]
optional: [necessity_sufficiency_results, uncertainty_contributions, critical_path]
constraints: [load-bearing status is tied to observed or reasoned conclusion change; preserve factor identity]

Procedure

1. Assemble ablation, necessity/sufficiency, fragility, and uncertainty evidence. 2. Compare conclusion changes attributable to each factor. 3. Classify factors as necessary, sufficient, jointly necessary, or decorative where supported. 4. Rank load-bearing factors and state the evidence gap for each uncertain ranking.

Output contract

produces: [load_bearing_register, necessity_sufficiency_map, fragility_ranking, evidence_gaps]
delta_fields: [findings, evidence_updates, decisions, uncertainties]

Quality gates

  • Every factor receives a role classification and evidence-backed importance assessment.
  • Fragility severity and alternative credibility are reported separately.
  • EVPI or uncertainty-contribution results retain the caller's units and are not treated as causal proof.

Parameterization

The caller must provide conclusion schema, factor/assumption list, perturbation results, classification ontology, importance metric, and uncertainty representation.

Failure and counterexamples

Reject a load-bearing claim based only on correlation, an untested factor, or a perturbation that changes multiple undeclared inputs.

Provenance map

  • resolved: stress-test/load-bearing-identification
  • resolved: deep-insight/fragility-flagging
  • resolved: deep-insight/critical-path-identification

Preserved source criteria ledger

No numeric source gate was present in the resolved source nodes; the role and evidence requirements above preserve their qualitative constraints.

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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.

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Repo: yogsoth-ai/de-anthropocentric-research-engine