alternative-model-generation
The alternative-model-generation skill creates variant formulations of existing models by systematically modifying their foundational assumptions. Use this when exploring how relaxing, replacing, or generalizing specific constraints affects model behavior, applicability, or theoretical implications. It spawns subagents to execute generation passes, with each pass consuming one budget unit.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/alternative-model-generation && cp -r /tmp/alternative-model-generation/skills/alternative-model-generation ~/.claude/skills/alternative-model-generationSKILL.md
# Alternative Model Generation Generate model variants by relaxing assumptions. ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Budget One unit = one alternative model generation 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