abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Execute one pairwise comparison under a specified criterion/model and return winner, confidence, and reasons.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill compare-pair --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill compare-pair -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill compare-pair -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill compare-pair -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill compare-pair -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill compare-pair -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill compare-pair -a windsurf -y
name: compare-pair description: "Execute one pairwise comparison under a specified criterion/model and return winner, confidence, and reasons."
Execute one pairwise comparison under a declared criterion and return a winner, confidence, tie status, and reasons.
required: [item_a, item_b, comparison_criterion, comparison_model, evidence_register] optional: [tie_rule, prior_ratings] constraints: [criterion direction and evidence scope are fixed before comparison]
1. Verify that both items are comparable under the same criterion and evidence scope. 2. Assess item A and item B symmetrically, recording criterion-level evidence and limitations. 3. Select A, B, or tie; assign confidence on the declared scale and explain the decisive contrast. 4. Return the comparison record without updating global ratings.
produces: [pairwise_result, confidence, rationale, evidence_gaps] delta_fields: [findings, evidence_updates, uncertainties, decisions]
Do not compare items with different scopes or criteria. If evidence is asymmetric or incomparable, return unresolved rather than forcing a winner.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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