best-option-selection
The best-option-selection skill applies compensatory multi-criteria decision methods including WSM, TOPSIS, AHP, MAUT, and VIKOR to identify a single superior alternative from a moderate candidate set. Use it when a decision requires selecting one best option from three to fifteen alternatives where trade-offs between criteria are acceptable and a structured, defensible recommendation is needed.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/best-option-selection && cp -r /tmp/best-option-selection/skills/best-option-selection ~/.claude/skills/best-option-selectionSKILL.md
# Best-Option Selection **Purpose:** Select the single best-performing alternative from a candidate set, supporting WSM, TOPSIS, AHP, MAUT, VIKOR, and other methods. **When to use:** - User needs to select "the best one" from multiple candidates - Decision scenario allows compensatory trade-offs (high scores offset low scores) - Moderate number of candidates (3-15) ## Budget | Base SOP | Target | ±10% Range | |----------|--------|------------| | criterion-definition | 5-8 criteria | 4-9 | | weight-elicitation-sop | 1 weight vector | 1 | | alternative-scoring | 1 score matrix | 1 | | normalization | 1 normalized matrix | 1 | | scoring-synthesis | 1 recommendation | 1 | ## State Ledger ```yaml strategy: best-option-selection status: pending criteria_defined: false weights_computed: false scores_computed: false normalized: false synthesized: false selected_method: null result: null ``` ## Available Tactics - **scoring-matrix-construction** — Standard workflow: define criteria → assign weights → score → aggregate → sensitivity ## Available SOPs ### Import (from scoring-matrix-construction) - criterion-definition - weight-elicitation-sop - alternative-scoring - normalization ### Subagent - scoring-synthesis ## Execution Guidance 1. Invoke scoring-matrix-construction tactic to build the score matrix 2. Select aggregation method based on problem characteristics (WSM for simple scenarios, TOPSIS when ideal solution reference is needed, VIKOR when compromise solution is needed) 3. Invoke scoring-synthesis to produce final recommendation 4. If user questions the result, switch methods, recompute, and compare ## Output Format ```markdown ## Best Option Recommendation **Recommended:** [Alternative name] **Overall Score:** [Score value] **Method Used:** [WSM/TOPSIS/AHP/MAUT/VIKOR] ### Score Ranking | Rank | Alternative | Overall Score | Key Strengths | |------|-------------|---------------|---------------| ### Sensitivity Notes [Impact of weight changes on the result] ``` <!-- BEGIN available-tables (generated) --> ## Available Tactics Optional, no fixed order; the final leaf is always a sop. | Tactic | When to use | | --- | --- | | convergence-scoring-matrix-construction | Build a complete scoring matrix through criterion definition, weighting, scoring, normalization, and sensitivity testing. | <!-- 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