argument-extraction
This Claude Code skill extracts the strongest possible versions of arguments supporting a given opinion cluster by spawning a subagent to synthesize reasoning across multiple perspectives. Use it when you need to represent a group's position fairly in its most compelling form, particularly as input for comparing disagreements between different opinion clusters.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/argument-extraction && cp -r /tmp/argument-extraction/skills/argument-extraction ~/.claude/skills/argument-extractionSKILL.md
# Argument Extraction Extract the core arguments supporting a given opinion cluster and present them in their strongest possible form (steel-manned). Synthesizes reasoning from multiple perspectives within the cluster into coherent, well-structured arguments. ## Execution Spawn a subagent that takes a cluster characterization and the relevant judgments, then produces a set of steel-manned arguments representing the cluster's position. ## Why Subagent - Argument extraction requires careful synthesis across multiple inputs - Steel-manning requires dedicated analytical attention - Output feeds directly into disagreement-visualization ## HARD-GATE Output MUST contain: at least 1 argument per cluster, each with `claim`, `evidence`, `reasoning`, and `strength` fields. Arguments must be steel-manned (strongest possible version). <!-- BEGIN available-tables (generated) --> ## Available SOPs Optional, no fixed order; the final leaf is always a sop. | SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. | <!-- END available-tables (generated) -->
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: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems