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/best-option-selection

Select the single best candidate from a set using WSM, TOPSIS, AHP, MAUT,

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill best-option-selection --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/best-option-selection

Context preview

The summary Claude sees to decide when to auto-load this skill.

Select the single best candidate from a set using WSM, TOPSIS, AHP, MAUT,

SKILL.md

best-option-selection.SKILL.md
name: best-option-selection
description: Select the single best candidate from a set using WSM, TOPSIS, AHP, MAUT,
  or VIKOR methods.
dependencies:
  tactics:
  - convergence-scoring-matrix-construction

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

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

## 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) -->

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Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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Repo: yogsoth-ai/de-anthropocentric-research-engine