abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill aggregate-ranking -a windsurf -y
name: aggregate-ranking description: "Aggregate criterion or comparison results into an ordered recommendation under an explicit rule."
Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
required: [candidate_set, criterion_results, aggregation_rule] optional: [tie_break_rule, missing_value_policy, uncertainty_annotations] constraints: [criterion directions and scales must be declared; no silent imputation]
1. Align candidate identifiers, criterion directions, units, and validity flags. 2. Apply the supplied aggregation rule without changing weights or directions. 3. Propagate missingness and uncertainty; apply the declared tie-break only after aggregation. 4. Return ordered candidates with component contributions and recommendation status.
produces: [ordered_recommendation, aggregate_scores, contribution_table, unresolved_comparisons] delta_fields: [findings, evidence_updates, decisions, uncertainties]
Caller supplies candidate schema, criterion scales/directions, weights or aggregation formula, tie-break rule, and missing/uncertainty policy.
Reject mixed units without normalization; reject a recommendation when a hard criterion is unresolved.
| source | criterion | |---|---| | priority-synthesis | All scoring dimensions are present for every gap. | | priority-synthesis | Weight vector sums to 1.0 within +/-0.001. | | priority-synthesis | Priority list is sorted descending; ties use feasibility sub-score. | | priority-synthesis | Top N is N=min(3,total gaps) and includes attack-path suggestions. | | scoring-synthesis | Final recommendation includes recommended alternative, confidence, key assumptions, and risk warnings. |
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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