analogy-extraction
The analogy-extraction skill systematically discovers transferable structural principles by scanning multiple source domains, abstracting their core mechanisms, mapping structural correspondences to a target domain, and validating that analogies operate at deep relational rather than surface levels. Use this skill when seeking novel solutions or frameworks by learning from how different fields solve analogous problems, requiring at least five scanned domains and two validated deep analogies to meet minimum standards.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/analogy-extraction && cp -r /tmp/analogy-extraction/skills/analogy-extraction ~/.claude/skills/analogy-extractionSKILL.md
# Analogy Extraction Extract transferable structural principles from source domains. ## Stages ### Stage 1: Source Identification Identify candidate source domains using domain-scanning SOP. Evaluate each for structural similarity depth (surface/structural/systemic). ### Stage 2: Abstraction For each promising source, extract the abstract principle using abstraction-extraction or biological-strategy-extraction SOP. Strip domain-specific details to reveal the transferable mechanism. ### Stage 3: Structural Mapping Map source structure to target domain. Identify: corresponding elements, missing elements (gaps), extra elements (opportunities). Use structural-mapping SOP. ### Stage 4: Transfer Validation Assess mapping quality: Is the analogy surface-level (shared labels) or deep (shared relational structure)? Use analogy-quality-assessment SOP. Only deep analogies warrant transfer. ## Minimum Yield | Metric | Floor | |--------|-------| | Source domains scanned | ≥5 | | Abstractions extracted | ≥3 | | Structural mappings completed | ≥3 | | Validated deep analogies | ≥2 | ## Available SOPs | SOP | Role | |-----|------| | domain-scanning | Stage 1 — find candidate source domains | | web-search | Stage 1 — supplement domain search | | paper-overview | Stage 1 — find academic analogies | | abstraction-extraction | Stage 2 — extract abstract principles | | structural-mapping | Stage 3 — map source→target structure | | analogy-quality-assessment | Stage 4 — validate mapping depth | | novelty-scoring | Post — score resulting ideas | | idea-synthesis | Post — synthesize into coherent concepts |
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