causation-consequence
Causal reasoning in historical analysis. Covers distinguishing immediate from underlying causes, tracing chains of causation across time scales, identifying unintended consequences, and applying counterfactual reasoning to assess causal significance. Use when analyzing why historical events occurred, evaluating the relative weight of causes, or assessing whether outcomes were inevitable or contingent.
npx skills add majiayu000/claude-skill-registry --skill causation-consequence --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill guides an agent to perform causal reasoning in historical analysis, focusing on distinguishing immediate from underlying causes, tracing chains of causation across time scales, identifying unintended consequences, and applying counterfactual reasoning to assess causal significance. It is intended for analyzing why historical events occurred, weighing the relative importance of causes, and evaluating whether outcomes were inevitable or contingent.
How it works
The agent is instructed to:
- Distinguish between immediate (triggers) and underlying (structural) causes, and recognize deep causes (longue duree) in Braudel’s framework.
- Build causal chains by listing links from a cause to its effects, ensuring each link is empirically supported, mechanistically plausible, and temporally ordered.
- Identify unintended consequences by categorizing perverse effects, unintended beneficiaries, cascade effects, and technological consequences, with examples and analytical caveats.
- Apply counterfactual reasoning using explicit rules: minimal rewrite, plausibility, specify mechanism, and acknowledge uncertainty; present reasoning as likely or plausible rather than certain.
- Use worked examples to illustrate immediate vs underlying causes, chains of causation, unintended consequences, and counterfactual scenarios, and to highlight weighting and contingency considerations.
When to use it
Use when analyzing why historical events occurred, evaluating the relative weight of causes, or assessing whether outcomes were inevitable or contingent. This includes weighing multiple causal layers, tracing long chains across time, and examining how counterfactuals could alter interpretations of causation.
What it can touch
The skill references concrete data points, named events, and historical periods as part of its worked examples (e.g., 1914 assassination, Berlin Wall 1989, Versailles Treaty 1919, Lincoln assassination). It does not specify executable tools or code actions beyond general causal reasoning tasks and counterfactual analysis.
Caveats
The skill emphasizes that historical causation involves multiple layers with contingent outcomes and warns against teleological narratives. It notes that there is no single formula for weighting causes and that counterfactuals are thought experiments, not predictions. The examples illustrate methodological cautions rather than guaranteeing historical certainty.
# Causation and Consequence Causation is the central problem of historical explanation. Historians do not merely record what happened — they explain why it happened. But historical causation is not like causation in physics, where controlled experiments can isolate variables. Historical events are singular, unrepeatable, and produced by the intersection of multiple causal threads operating at different time scales. This skill catalogs four practices for reasoning about causes and consequences in history: distinguishing immediate from underlying causes, tracing chains of causation, identifying unintended consequences, and applying counterfactual reasoning. **Agent affinity:** ibn-khaldun (social and economic causation, cyclical patterns), braudel (longue duree structural causation) **Concept IDs:** hist-immediate-underlying-causes, hist-chains-of-causation, hist-unintended-consequences, hist-counterfactual-reasoning ## The Causal Reasoning Framework at a Glance | # | Practice | Core question | Key signal | |---|---|---|---| | 1 | Immediate vs. underlying causes | What triggered the event vs. what made it possible? | Time scale separation between trigger and conditions | | 2 | Chains
- The Causal Reasoning Framework at a Glance
- Practice 1 — Immediate vs. Underlying Causes
- The Three Layers of Causation
- Worked Example — The Fall of the Berlin Wall (1989)
- The Weighting Problem
- Practice 2 — Chains of Causation
- Constructing a Causal Chain
- Worked Example — From Cotton Gin to Civil War
- Branching Chains and Feedback Loops
- Practice 3 — Unintended Consequences
- Categories of Unintended Consequences
- Worked Example — The Unintended Consequences of the Versailles Treaty (1919)
- Why Unintended Consequences Occur
- Practice 4 — Counterfactual Reasoning
What does the causation-consequence skill do?
Causal reasoning in historical analysis. Covers distinguishing immediate from underlying causes, tracing chains of causation across time scales, identifying unintended consequences, and applying counterfactual reasoning to assess causal significance. Use when analyzing why historical events occurred, evaluating the relative weight of causes, or assessing whether outcomes were inevitable or contingent.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill causation-consequence --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.