analogy-chain
The analogy-chain skill systematically constructs extended metaphorical comparisons across three to five progressive layers, with each layer uncovering previously hidden connections and conceptual dimensions. Use this skill when exploring complex topics that benefit from structured, multi-level abstraction, as the layered approach reveals non-obvious insights by building each analogy upon its predecessor rather than treating comparisons as isolated statements.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/analogy-chain && cp -r /tmp/analogy-chain/skills/analogy-chain ~/.claude/skills/analogy-chainSKILL.md
# Analogy Chain Chain analogies to deeper levels (3-5 layers). ## Execution Subagent — spawned via subagent-spawning/spawn-agent skill. ## Why Subagent Analogy chaining requires sustained deepening of a single thread without distraction. Each layer builds on the previous, requiring unbroken focus on progressive abstraction and re-concretization.
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