abp-vulnerability-classification
This Claude Code skill classifies assumptions along two dimensions, load-bearing importance and vulnerability to falsification, to identify which assumptions most critically need testing. It prioritizes assumptions that carry high explanatory weight while being likely to be false, directing analytical effort toward the highest-risk gaps in reasoning.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/abp-vulnerability-classification && cp -r /tmp/abp-vulnerability-classification/skills/abp-vulnerability-classification ~/.claude/skills/abp-vulnerability-classificationSKILL.md
# ABP Vulnerability Classification Classify assumption vulnerability to prioritize testing. ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Budget One unit = one vulnerability classification pass. <!-- 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