/ablation-design
Design ablation studies to isolate component contributions in ML systems
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill ablation-design --agent claude-codeHow it fires
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/ablation-design
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Design ablation studies to isolate component contributions in ML systems
SKILL.md
ablation-design.SKILL.mdname: ablation-design
description: Design ablation studies to isolate component contributions in ML systems
version: 1.0.0
category: experiment-execution
type: strategy
sops:
- ablation-component-mapping
- baseline-selection
- metric-specification
- sample-size-estimation
tactics:
- statistical-method-selection
dependencies:
sops:
- ablation-component-mapping
- baseline-selection
- metric-specification
- sample-size-estimation
tactics:
- statistical-method-selection
Strategy: Ablation Design
**Question**: What does each component contribute?
Methodology
- **Systematic Ablation** (Newell 1974): Remove one component at a time, measure degradation.
- **Replacement Ablation**: Replace component with simpler alternative to isolate contribution.
- **Combinatorial Ablation** (ABLATOR): Test component subsets to detect interaction effects.
- **Conditional Ablation**: Ablate components under specific data conditions to find context-dependent contributions.
Execution Flow
1. **ablation-component-mapping** → Map system architecture to ablatable units 2. **baseline-selection** → Select full-system and minimal-system anchors 3. **metric-specification** → Define metrics that capture component contribution 4. **sample-size-estimation** → Determine runs needed for reliable delta estimation 5. **statistical-method-selection** (tactic) → Choose appropriate significance tests for deltas
Budget Gate
| Ablation Type | Components (N) | Min Runs | When to Use | |---------------|---------------|----------|-------------| | Systematic (leave-one-out) | 3-8 | N + 2 | Standard component analysis | | Replacement | 3-8 | 2N + 2 | Need to distinguish "removal" vs "simplification" | | Combinatorial (selected) | 4-6 | ~2N | Suspected interactions between components | | Combinatorial (full) | 3-4 | 2^N | Small systems, need complete picture | | Conditional | 3-6 | N * conditions | Context-dependent contributions |
<!-- BEGIN available-tables (generated) -->
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | statistical-method-selection | Select appropriate statistical methods for experiment analysis |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | ablation-component-mapping | Map system architecture to ablatable units for ablation studies | | baseline-selection | Select appropriate baselines for experimental comparison | | metric-specification | Define experiment metrics and significance standards | | sample-size-estimation | SOP: power analysis and required experiment count estimation |
<!-- END available-tables (generated) -->
Read more
name: ablation-design description: Design ablation studies to isolate component contributions in ML systems version: 1.0.0 category: experiment-execution type: strategy sops: - ablation-component-mapping - baseline-selection - metric-specification - sample-size-estimation tactics: - statistical-method-selection dependencies: sops: - ablation-component-mapping - baseline-selection - metric-specification - sample-size-estimation tactics: - statistical-method-selection
Strategy: Ablation Design
**Question**: What does each component contribute?
Methodology
- **Systematic Ablation** (Newell 1974): Remove one component at a time, measure degradation.
- **Replacement Ablation**: Replace component with simpler alternative to isolate contribution.
- **Combinatorial Ablation** (ABLATOR): Test component subsets to detect interaction effects.
- **Conditional Ablation**: Ablate components under specific data conditions to find context-dependent contributions.
Execution Flow
1. **ablation-component-mapping** → Map system architecture to ablatable units 2. **baseline-selection** → Select full-system and minimal-system anchors 3. **metric-specification** → Define metrics that capture component contribution 4. **sample-size-estimation** → Determine runs needed for reliable delta estimation 5. **statistical-method-selection** (tactic) → Choose appropriate significance tests for deltas
Budget Gate
| Ablation Type | Components (N) | Min Runs | When to Use | |---------------|---------------|----------|-------------| | Systematic (leave-one-out) | 3-8 | N + 2 | Standard component analysis | | Replacement | 3-8 | 2N + 2 | Need to distinguish "removal" vs "simplification" | | Combinatorial (selected) | 4-6 | ~2N | Suspected interactions between components | | Combinatorial (full) | 3-4 | 2^N | Small systems, need complete picture | | Conditional | 3-6 | N * conditions | Context-dependent contributions |
<!-- BEGIN available-tables (generated) -->
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | statistical-method-selection | Select appropriate statistical methods for experiment analysis |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | ablation-component-mapping | Map system architecture to ablatable units for ablation studies | | baseline-selection | Select appropriate baselines for experimental comparison | | metric-specification | Define experiment metrics and significance standards | | sample-size-estimation | SOP: power analysis and required experiment count estimation |
<!-- END available-tables (generated) -->
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

