alternatives-generation
The alternatives-generation skill systematically produces distinct alternatives for every known approach to a problem, ensuring no methodology remains unquestioned. It spawns a dedicated subagent to maintain focus on generating genuinely different options rather than incremental variations, enabling comprehensive exploration of alternative methodologies within structured research workflows.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/alternatives-generation && cp -r /tmp/alternatives-generation/skills/alternatives-generation ~/.claude/skills/alternatives-generationSKILL.md
# Alternatives Generation Generate alternatives for every known approach. ## Execution Subagent — spawned via subagent-spawning/spawn-agent skill. ## Why Subagent Alternatives generation requires systematically considering each known approach and producing genuinely different alternatives (not variations). Benefits from dedicated context that can maintain the "different, not better" mindset.
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