Babysitter promethee-evaluator
PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) skill for outranking-based multi-criteria analysis
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/business/decision-intelligence/skills/promethee-evaluator" ~/.claude/skills/a5c-ai-babysitter-promethee-evaluator && rm -rf "$T"
manifest:
library/specializations/domains/business/decision-intelligence/skills/promethee-evaluator/SKILL.mdtags
source content
PROMETHEE Evaluator
Overview
The PROMETHEE Evaluator skill implements the Preference Ranking Organization Method for Enrichment Evaluation methodology for multi-criteria decision analysis. It uses outranking relations based on pairwise comparisons of alternatives, allowing for flexible preference modeling through various preference functions.
Capabilities
- Preference function selection (Usual, U-shape, V-shape, Level, Linear, Gaussian)
- Unicriterion preference degree calculation
- Multicriteria preference index computation
- PROMETHEE I partial ranking
- PROMETHEE II complete ranking
- GAIA plane visualization
- Walking weights sensitivity analysis
- Net flow calculation
Used By Processes
- Multi-Criteria Decision Analysis (MCDA)
- Vendor Selection Analysis
- Resource Allocation Decisions
Usage
Preference Functions
- Usual (Type I): Binary preference (1 if better, 0 otherwise)
- U-shape (Type II): Indifference threshold q
- V-shape (Type III): Linear with preference threshold p
- Level (Type IV): Combination of q and p thresholds
- Linear (Type V): Linear between q and p thresholds
- Gaussian (Type VI): Normal distribution with sigma parameter
Configuration Example
# Define PROMETHEE configuration config = { "alternatives": ["Alt A", "Alt B", "Alt C", "Alt D"], "criteria": [ { "name": "Cost", "weight": 0.3, "type": "cost", "preference_function": "linear", "parameters": {"p": 20000, "q": 5000} }, { "name": "Quality", "weight": 0.4, "type": "benefit", "preference_function": "gaussian", "parameters": {"sigma": 10} }, { "name": "Delivery", "weight": 0.3, "type": "cost", "preference_function": "v_shape", "parameters": {"p": 5} } ], "performance_matrix": [ [100000, 85, 12], # Alt A [120000, 92, 8], # Alt B [80000, 78, 15], # Alt C [110000, 88, 10] # Alt D ] }
Flow Calculations
- Positive Flow (Phi+): How much an alternative outranks others
- Negative Flow (Phi-): How much an alternative is outranked
- Net Flow (Phi): Phi+ - Phi- (used for complete ranking)
PROMETHEE I vs II
- PROMETHEE I: Partial ranking based on Phi+ and Phi- separately (allows incomparabilities)
- PROMETHEE II: Complete ranking based on net flow Phi
GAIA Visualization
The GAIA plane provides:
- 2D projection of criteria and alternatives
- Decision axis showing weight sensitivity
- Clustering of similar alternatives
- Criteria correlation identification
Input Schema
{ "alternatives": ["string"], "criteria": [ { "name": "string", "weight": "number", "type": "benefit|cost", "preference_function": "usual|u_shape|v_shape|level|linear|gaussian", "parameters": "object" } ], "performance_matrix": "2D array of numbers", "options": { "method": "PROMETHEE_I|PROMETHEE_II", "gaia_visualization": "boolean", "sensitivity_analysis": "boolean" } }
Output Schema
{ "ranking": [ { "alternative": "string", "rank": "number", "phi_plus": "number", "phi_minus": "number", "phi_net": "number" } ], "outranking_matrix": "2D array", "partial_ranking": { "preferred_pairs": ["object"], "incomparable_pairs": ["object"] }, "gaia_data": { "alternative_coordinates": "object", "criteria_axes": "object", "decision_axis": "object" }, "sensitivity_results": "object" }
Best Practices
- Select appropriate preference functions based on criterion characteristics
- Use PROMETHEE I when incomparabilities are meaningful
- Set thresholds (p, q) based on domain expertise
- Analyze GAIA plane for insights beyond rankings
- Validate results with stakeholders
- Compare with other MCDA methods for robustness
Integration Points
- Receives weights from AHP Calculator or Stakeholder Preference Elicitor
- Feeds into Decision Visualization for GAIA planes
- Connects with ELECTRE Comparator for method comparison
- Supports Sensitivity Analyzer for weight robustness testing