Agent skill
promethee-evaluator
PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) skill for outranking-based multi-criteria analysis
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/promethee-evaluator
Metadata
Additional technical details for this skill
- domain
- business
- category
- quantitative-analysis
- priority
- lower
- specialization
- decision-intelligence
- tools libraries
-
[ "pyDecision", "pymcdm", "visual-promethee" ]
SKILL.md
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
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
gsd-tools
Central utility skill for GSD operations. Provides config parsing, slug generation, timestamps, path operations, and orchestrates calls to other specialized skills. Acts as the unified entry point that the original gsd-tools.cjs provided via its lib/ modules (commands, config, core, init).
model-profile-resolution
Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.
verification-suite
Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.
state-management
STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick tasks.
git-integration
Git commit patterns, formats, and conventions for GSD methodology. Provides atomic commits per task, structured commit messages, planning file commits, branch management, and milestone tag operations.
frontmatter-parsing
YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.
Didn't find tool you were looking for?