apply-perturbation
SkillDev toolsLets your agent test how a system reacts by changing one assumption or component at a time and recording the results.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the apply-perturbation skill
About this skill
Apply a controlled change to an assumption, factor, component, parameter, or condition and record response.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/apply-perturbation/SKILL.md and read by ahel’s review.
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
- Record the baseline artifact, target, axis, response metric, and comparison direction.
- Generate the caller-specified variation, removal, negation, or ablation conditions.
- Re-evaluate the response at each condition and attach evidence and uncertainty.
- 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-removalline 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. |
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
apply-perturbation- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine