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/apply-perturbation

Apply a controlled change to an assumption, factor, component, parameter, or condition and record response.

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill apply-perturbation --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/apply-perturbation

Context preview

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

Apply a controlled change to an assumption, factor, component, parameter, or condition and record response.

SKILL.md

apply-perturbation.SKILL.md
name: apply-perturbation
description: "Apply a controlled change to an assumption, factor, component, parameter, or condition and record response."

apply-perturbation

Purpose

Apply a controlled change to an assumption, factor, component, parameter, or condition and record the response.

Input contract

required: [baseline_artifact, perturbation_target, perturbation_axis, response_metric]
optional: [variation_range, removal_mode, factor_list, uncertainty_model]
constraints: [change one declared target or axis at a time unless the caller explicitly supplies an interaction design; preserve baseline comparability]

Procedure

1. Record the baseline artifact, target, axis, response metric, and comparison direction. 2. Generate the caller-specified variation, removal, negation, or ablation conditions. 3. Re-evaluate the response at each condition and attach evidence and uncertainty. 4. Identify degradation, flip points, or threshold regions and summarize the effect.

Output contract

produces: [perturbation_series, response_comparison, degradation_or_flip_points, interpretation]
delta_fields: [findings, evidence_updates, decisions, uncertainties]

Quality gates

  • Controlled perturbation records performance at each point along one defined axis and identifies degradation thresholds where applicable.
  • Single-factor removal retains a degradation score from 0.0 (no effect) to 1.0 (collapse) and states conclusion before/after.
  • Ablation removes components one by one; do not combine removals while labeling the result single-factor.

Parameterization

The caller must provide the baseline artifact or system, perturbation target and axis, range or removal mode, response metric, factor list when needed, and degradation/flip classification rule.

Failure and counterexamples

Reject when the baseline is missing, the perturbation is not attributable, response measurements are incomparable, or a degradation score is reported without before/after reasoning.

Provenance map

  • resolved: deep-insight/controlled-perturbation
  • concept: creative-ideation/assumption-perturbation
  • resolved: creative-ideation/ablation-execution
  • resolved: stress-test/single-factor-removal

Verbatim source criteria excerpts

  • `single-factor-removal` line 35: degradation_score: 0.0 (no effect) to 1.0 (collapse)

Preserved source criteria ledger

| source | physical line | kind | source criterion | |---|---:|---|---| | stress-test/single-factor-removal | 24 | numeric | Degradation score ranges from 0.0 (no effect) to 1.0 (collapse), with before/after conclusion and reasoning. |

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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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Repo: yogsoth-ai/de-anthropocentric-research-engine