ahrq-reason-classification
This Claude Code skill classifies research gaps according to the AHRQ 4-reason framework, categorizing gaps as arising from insufficient information, biased information, inconsistent information, or information not yet integrated into practice. Use it when analyzing healthcare research literature to systematically identify the root causes preventing evidence from addressing identified gaps, enabling more targeted research prioritization and evidence synthesis efforts.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/ahrq-reason-classification && cp -r /tmp/ahrq-reason-classification/skills/ahrq-reason-classification ~/.claude/skills/ahrq-reason-classificationSKILL.md
# AHRQ Reason Classification Classify why a research gap exists using the AHRQ framework. ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Budget One unit = one AHRQ reason classification pass.
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