abstraction-laddering
Abstraction-laddering moves between concrete and abstract framings by escalating three levels up through "Why?" questions and descending three levels down through "How?" questions. Use this skill when research framings feel stuck or misaligned to identify the most productive level of analysis for your investigation. The subagent spawning mechanism constructs complete abstraction ladders within defined token budgets.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/abstraction-laddering && cp -r /tmp/abstraction-laddering/skills/abstraction-laddering ~/.claude/skills/abstraction-ladderingSKILL.md
# Abstraction Laddering Navigate abstraction levels to find productive framings. ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Budget One unit = one abstraction ladder construction.
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.
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems