Closest Worlds Strategy
SkillDev tools'Strategy: Lewis Possible Worlds — find the minimal change to reality
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Closest Worlds Strategy skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/closest-worlds/SKILL.md and read by ahel’s review.
Lewis semantics: evaluate counterfactuals by finding the nearest possible world where the antecedent holds and checking whether the consequent follows.
Method
- causal-claim-extraction identifies the conclusion and its supporting factors
- factor-enumeration maps the space of possible changes
- flip-point-detection searches for minimal changes that flip the conclusion
- counterfactual-scenario-construction builds the nearest world where conclusion fails
- fragility-measurement computes distance from actuality to flip-point
- load-bearing-identification ranks factors by proximity to flip
Budget Table
| Parameter | S | M | L |
|---|---|---|---|
| Change candidates explored | 5 | 12 | 25 |
| Flip-point searches | 3 | 8 | 15 |
| World-distance comparisons | 3 | 6 | 12 |
Orchestration
causal-claim-extraction → factor-enumeration
→ [generate change candidates]:
flip-point-detection (binary search for minimal flip)
→ counterfactual-scenario-construction (build nearest world)
→ fragility-measurement (compute distance)
→ load-bearing-identification (rank by proximity)
Subagents
- causal-claim-extraction (conclusion identification)
- factor-enumeration (change space mapping)
- flip-point-detection (minimal flip search)
- counterfactual-scenario-construction (world building)
- fragility-measurement (distance computation)
- load-bearing-identification (proximity ranking)
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| minimal-change-search | Tactic: Generate candidate changes, detect flip-points where conclusion reverses, measure fragility as distance to nearest flip. |
| systematic-factor-ablation | Tactic: List all factors, remove one at a time, assess conclusion stability, rank factors by load-bearing importance. |
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. |
| counterfactual-scenario-construction | Construct precise, internally consistent counterfactual scenarios where specified factors are altered, then reason about the resulting conclusion. |
| factor-enumeration | List all key factors, conditions, and assumptions that support or enable the artifact's conclusion. |
| flip-point-detection | Find the minimal change magnitude along a dimension that causes the conclusion to flip from true to false. |
| fragility-measurement | Compute a fragility index from flip-point distances and degradation scores, summarizing how robust the conclusion is. |
| load-bearing-identification | Identify which factors are "load-bearing walls" — factors whose removal would collapse the conclusion. |
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
closest-worlds- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine