# Thought Experiment

> Strategy: Williamson-style precise thought experiments — construct carefully specified counterfactual scenarios to test whether conclusions depend on contingent features.

- Skill: `yogsoth-ai/thought-experiment` (Agent Skill)
- Install (CLI): `npx skillmds@latest add yogsoth-ai/thought-experiment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yogsoth-ai/thought-experiment/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: yogsoth-ai (https://skillmd.com/u/yogsoth-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/yogsoth-ai/thought-experiment

---


# Thought Experiment Strategy

Williamson methodology: construct precise, well-specified counterfactual scenarios that isolate individual variables.

## Method

1. **causal-claim-extraction** identifies the conclusion and its dependencies
2. **factor-enumeration** identifies contingent vs. essential features
3. **counterfactual-scenario-construction** builds precise thought experiments
4. **flip-point-detection** identifies which scenario variations flip the conclusion
5. **necessity-evaluation** determines if flipped factors are genuinely necessary
6. **load-bearing-identification** distinguishes essential from contingent support

## Design Principles

- Scenarios must be internally consistent (no impossible worlds)
- Changes must be minimal and precisely specified
- Background conditions must be held fixed except the target variable
- Conclusions must follow from the scenario, not from intuition pumps

## Budget Table

| Parameter | S | M | L |
|---|---|---|---|
| Thought experiments | 3 | 8 | 15 |
| Variables isolated | 3 | 6 | 12 |
| Scenario precision checks | 1 | 3 | 6 |

## Orchestration

```
causal-claim-extraction → factor-enumeration
→ [for each contingent feature]:
    counterfactual-scenario-construction (precise scenario)
    → flip-point-detection (does conclusion hold?)
    → necessity-evaluation (is this genuinely necessary?)
→ load-bearing-identification (essential vs contingent)
```

## Subagents

- causal-claim-extraction (dependency identification)
- factor-enumeration (contingent feature detection)
- counterfactual-scenario-construction (scenario design)
- flip-point-detection (conclusion testing)
- necessity-evaluation (necessity judgment)
- load-bearing-identification (classification)

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## Available Tactics

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

| Tactic | When to use |
| --- | --- |
| causal-necessity-testing | Tactic: Extract causal claims, evaluate probability of necessity (PN) and sufficiency (PS) for each, classify into necessity-sufficiency quadrants. |
| minimal-change-search | Tactic: Generate candidate changes, detect flip-points where conclusion reverses, measure fragility as distance to nearest flip. |

## 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. |
| 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? |

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