--- name: yogsoth-ai/convergence-multi-criteria-scoring source: https://app.decimal.ai/s/yogsoth-ai-convergence-multi-criteria-scoring@2/SKILL.md source_sha256: 397e98098d9b --- # Multi-Criteria Scoring Evaluate and rank candidate alternatives using multi-criteria weighted assessment. Supports mainstream MCDA methods including AHP, BWM, TOPSIS, VIKOR, ELECTRE, PROMETHEE, and MAUT, covering the full workflow from criteria definition, weighting, scoring, to sensitivity analysis. ## Strategy Routing | Signal | Strategy | |--------|----------| | "What criteria? How to score?" / select best option / rank by criteria | best-option-selection | | "Full ranking" / full ranking / league table / priority list | full-ranking | | "Categorize" / categorize / A/B/C grading / compliance sorting | category-sorting | | "Eliminate non-qualifying" / go/no-go / safety screening / veto | non-compensatory-screening | | "Determine weights" / weight negotiation / criteria importance | weight-elicitation | ## Manifest ### Strategies | Strategy | Purpose | Budget | |----------|---------|--------| | best-option-selection | Select best option from candidates | M | | full-ranking | Produce complete priority ranking | M | | category-sorting | Classify candidates into predefined categories | M | | non-compensatory-screening | Eliminate non-qualifying candidates | S | | weight-elicitation | Determine criteria weights | S | ### Tactics | Tactic | Purpose | |--------|---------| | scoring-matrix-construction | Define criteria → assign weights → score → aggregate → sensitivity check | | screening-then-scoring | Non-compensatory elimination first → then fine-grained scoring of survivors | | multi-method-triangulation | Multi-method comparison → identify method-sensitive options | ### Subagent SOPs | SOP | Purpose | |-----|---------| | criterion-definition | Extract evaluation criteria from research goals and candidates | | weight-elicitation-sop | Compute criteria weights using specified method | | alternative-scoring | Score candidates against each criterion | | normalization | Normalize the score matrix | | dominance-check | Identify dominated and non-dominated alternatives | | threshold-setting | Set minimum thresholds for each criterion | | conjunctive-filter | Eliminate candidates failing thresholds | | rank-comparison | Compare consistency of multiple ranking results | | method-sensitivity-report | Analyze impact of method choice on rankings | | scoring-synthesis | Synthesize scores, rankings, and sensitivity into final recommendation | ## Budget Table | Metric | Target | |--------|--------| | Criteria count | 5-8 | | Candidate count | 8-15 | | Weight method comparison | >=2 methods | | Sensitivity analysis | >=3 parameter perturbations | ## MCP Tools | Tool | Usage | |------|-------| | vault_search | Retrieve existing evaluation criteria and historical scores | | vault_query_graph | Query relationships and dependencies between alternatives | | vault_add_edge | Record evaluation result relationships | ## Context Management - Each SOP executes as a subagent with independent context - Strategy layer maintains State Ledger to track progress - Score matrix is passed between SOPs as structured data - Sensitivity analysis results are aggregated into final synthesis ## Available Strategies Optional, no fixed order; the final leaf is always a sop. | Strategy | When to use | | --- | --- | | best-option-selection | Select the single best candidate from a set using WSM, TOPSIS, AHP, MAUT, or VIKOR methods. | | category-sorting | Classify candidates into predefined categories using ELECTRE-Tri, FlowSort, AHPSort, or DRSA methods. | | full-ranking | Produce a complete ordering of all candidates using PROMETHEE I/II, ELECTRE III, or MAVT methods. | | non-compensatory-screening | Eliminate non-qualifying candidates using conjunctive rules, dominance filtering, lexicographic ordering, or veto thresholds. | | weight-elicitation | Determine criteria weights using AHP, Swing, BWM, MACBETH, or Simos methods. | ## Available SOPs Optional, no fixed order; the final leaf is always a sop. | SOP | When to use | | --- | --- | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. | | convergence-paper-overview | Paper landscape scan at abstract level — discover MCDA, voting theory, Delphi, and optimization methodology papers. | | convergence-paper-research | Full-text deep reading of methodology papers — complete understanding of algorithms, proofs, and implementation details. | | convergence-paper-search | Paper AI summary reading — deeper understanding of specific methodology papers without full-text commitment. | | convergence-saturation-detection | Determines when to stop iterating — coverage threshold met or marginal returns diminishing. Shared across all campaigns. | | convergence-sensitivity-analysis | Tests conclusion robustness by perturbing parameters and observing rank changes. Shared across scoring, portfolio, and steel-manning campaigns. | | convergence-web-research | Deep web research with full-page extraction — detailed methodology guides, tutorials, implementation references. | | convergence-web-search | Quick web scan to discover relevant pages — methodology references, case studies, best practices for convergence methods. | ## Available Campaigns Optional, no fixed order; the final leaf is always a sop. | Campaign | When to use | | --- | --- | | pairwise-ranking | Pairwise Ranking Campaign — produce global rankings through pairwise comparisons and voting aggregation using Bradley-Terry, Elo, TrueSkill, Condorcet, Borda, Schulze methods. |