ablation-component-mapping
Ablation-component-mapping identifies and catalogs removable system components for ablation studies by analyzing architecture dependencies, defining removal procedures, and predicting performance impacts. Use this skill when designing experiments to measure individual component contributions to a system's overall performance or when decomposing complex architectures into testable units.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/ablation-component-mapping && cp -r /tmp/ablation-component-mapping/skills/ablation-component-mapping ~/.claude/skills/ablation-component-mappingSKILL.md
# SOP: Ablation Component Mapping Map the system architecture to a set of ablatable units, identifying dependencies, removal strategies, and expected impact for each component. Subagent — spawned via subagent-spawning/spawn-agent skill.
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: 面对异常的最佳解释推理
Remove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
Design ablation studies to isolate component contributions in ML systems
Remove components one by one from a system, record the response/impact of each removal.