/best-option-selection
Select the single best candidate from a set using WSM, TOPSIS, AHP, MAUT,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill best-option-selection --agent claude-codeHow 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.mdname: 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) -->
Read more
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) -->
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

