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Skill

/analyze-intervention

Apply an intervention/do-style graph modification and propagate predicted directional effects while recording assumptions.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-intervention --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/analyze-intervention

Context preview

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

Apply an intervention/do-style graph modification and propagate predicted directional effects while recording assumptions.

SKILL.md

analyze-intervention.SKILL.md
name: analyze-intervention
description: "Apply an intervention/do-style graph modification and propagate predicted directional effects while recording assumptions."

analyze-intervention

Purpose

Analyze an intervention's target, mechanism, implementation conditions, and observed effects.

Input contract

required: [intervention, target_system, outcome_records]
optional: [implementation_records, comparator, mechanism_hypotheses]
constraints: [effect interpretation must retain implementation and comparison conditions]

Procedure

1. Define intervention components, dose, timing, and target mechanism. 2. Map implementation fidelity and deviations to observed conditions. 3. Compare outcomes with the declared comparator and plausible alternatives. 4. Summarize mechanism evidence, effect heterogeneity, and unresolved attribution.

If the intervention analysis yields a causal attribution that needs an explicit validity check, consider `validate-causal-link` as the next tactic.

Output contract

produces: [intervention_map, implementation_profile, outcome_comparison, mechanism_assessment, attribution_limits]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]

Quality gates

  • Intervention components and implementation are separated.
  • Comparator and outcome conditions are explicit.

Failure and counterexamples

Do not attribute an effect to the intervention when co-interventions or implementation changes are untracked.

Provenance map

  • `resolved: analyze-intervention`
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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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Python
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Apache-2.0
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Repo: yogsoth-ai/de-anthropocentric-research-engine