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/causal-necessity-testing

Tactic: Extract causal claims, evaluate probability of necessity (PN)

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill causal-necessity-testing --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/causal-necessity-testing

Context preview

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

Tactic: Extract causal claims, evaluate probability of necessity (PN)

SKILL.md

causal-necessity-testing.SKILL.md
name: causal-necessity-testing
description: 'Tactic: Extract causal claims, evaluate probability of necessity (PN)
  and sufficiency (PS) for each, classify into necessity-sufficiency quadrants.'
type: tactic
strategies:
- necessity-sufficiency
- structural-counterfactual
- thought-experiment
dependencies:
  sops:
  - causal-claim-extraction
  - load-bearing-identification
  - necessity-evaluation
  - sufficiency-evaluation

Causal Necessity Testing Tactic

PNS evaluation: for each causal claim, determine whether the cause is necessary, sufficient, both, or neither.

Orchestration

1. **causal-claim-extraction** extracts all X→Y causal claims from the artifact 2. **necessity-evaluation** asks: if X had NOT occurred, would Y still hold? (PN) 3. **sufficiency-evaluation** asks: if X occurred in isolation, would Y follow? (PS) 4. Classify each claim into quadrant:

  • PN high + PS high → INUS condition (load-bearing)
  • PN high + PS low → necessary but not sufficient
  • PN low + PS high → sufficient but redundant
  • PN low + PS low → spurious or decorative

5. **load-bearing-identification** synthesizes quadrant assignments

Scoring

  • PN and PS scored 0.0–1.0 (probability estimates)
  • Threshold for "high": >= 0.7
  • Threshold for "low": < 0.3
  • Middle range (0.3–0.7): uncertain, flag for deeper investigation

Subagents Dispatched

  • causal-claim-extraction (claim identification)
  • necessity-evaluation (PN scoring per claim)
  • sufficiency-evaluation (PS scoring per claim)
  • load-bearing-identification (quadrant synthesis)

Termination Conditions

  • All extracted claims evaluated within budget
  • Early termination if INUS condition found and budget is S
  • All claims score PN < 0.3 (no necessary factors found — conclusion may be overdetermined)

<!-- BEGIN available-tables (generated) -->

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

| SOP | When to use | | --- | --- | | causal-claim-extraction | Extract all causal claims (X causes Y, X leads to Y, X enables Y) from an artifact, producing a structured list of cause-effect pairs. | | load-bearing-identification | Identify which factors are "load-bearing walls" — factors whose removal would collapse the conclusion. | | necessity-evaluation | Evaluate the probability of necessity (PN) for a causal factor — would the conclusion fail if this factor were absent? | | sufficiency-evaluation | Evaluate the probability of sufficiency (PS) for a causal factor — would this factor alone be enough to produce the conclusion? |

<!-- END available-tables (generated) -->

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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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