Causal Necessity Testing Tactic

SkillDev tools

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

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Causal Necessity Testing Tactic skill

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/causal-necessity-testing/SKILL.md and read by ahel’s review.

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)

Available SOPs

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

SOPWhen to use
causal-claim-extractionExtract 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-identificationIdentify which factors are "load-bearing walls" — factors whose removal would collapse the conclusion.
necessity-evaluationEvaluate the probability of necessity (PN) for a causal factor — would the conclusion fail if this factor were absent?
sufficiency-evaluationEvaluate the probability of sufficiency (PS) for a causal factor — would this factor alone be enough to produce the conclusion?

Signals

GitHub stars
469
Forks
37
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
causal-necessity-testing
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine