alternative-futures
Alternative Futures generates 2-4 competing scenarios that reinterpret the same evidence base to challenge an artifact's dominant narrative. Use this skill when exploring how different analytical frameworks or assumptions applied to identical evidence could produce fundamentally different conclusions, helping identify which observable indicators would distinguish between plausible alternatives.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/alternative-futures && cp -r /tmp/alternative-futures/skills/alternative-futures ~/.claude/skills/alternative-futuresSKILL.md
# Alternative Futures Generates multiple competing scenarios to challenge the dominant narrative. ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Why Subagent Alternative generation requires creative divergent thinking without anchoring on the artifact's conclusions. Isolated context prevents confirmation bias. ## Input - **artifact**: The artifact whose conclusions are being challenged - **evidence_base**: The evidence available (same evidence, different interpretations) ## Output - **alternatives**: 2-4 divergent scenarios, each internally consistent - **discriminators**: Observable indicators that would distinguish between alternatives - **plausibility_ranking**: Relative plausibility of each alternative vs. the artifact's conclusion
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