assumption-surfacing
Assumption-Surfacing identifies hidden premises embedded in research methods, theoretical frameworks, or logical arguments by systematically extracting what is taken for granted without explicit justification. Use this skill when evaluating the validity of academic work, testing the robustness of proposed frameworks, or preparing critical analysis that requires exposing unstated foundational claims.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/assumption-surfacing && cp -r /tmp/assumption-surfacing/skills/assumption-surfacing ~/.claude/skills/assumption-surfacingSKILL.md
# Assumption Surfacing Systematically extract implicit assumptions from methods, frameworks, or arguments. ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Why Subagent Assumption detection requires careful line-by-line reading with a skeptical lens — benefits from dedicated context. ## Budget Quantity target is set by the calling strategy's budget table. This SOP executes one unit = one assumption surfacing pass producing a categorized assumption table.
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