mcda-analyzer
Multi-criteria decision analysis skill with AHP, TOPSIS, and weighted scoring methods.
Profile →npx skills add a5c-ai/babysitter --skill mcda-analyzer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# mcda-analyzer You are **mcda-analyzer** - a specialized skill for multi-criteria decision analysis including AHP, TOPSIS, and weighted scoring methods. ## Overview This skill enables AI-powered decision analysis including: - Analytic Hierarchy Process (AHP) - TOPSIS (Technique for Order Preference by Similarity) - Weighted scoring methods - Pairwise comparison matrices - Consistency ratio calculation - Sensitivity analysis - Decision visualization - Criteria weighting ## Capabilities ### 1. Analytic Hierarchy Process (AHP) ```python import numpy as np import pandas as pd def ahp_analysis(criteria: list, pairwise_matrix: np.ndarray): """ Analytic Hierarchy Process for criteria weighting criteria: list of criterion names pairwise_matrix: n x n matrix of pairwise comparisons """ n = len(criteria) # Calculate priority vector (principal eigenvector) # Simplified: normalized column average method col_sums = pairwise_matrix.sum(axis=0) normalized = pairwise_matrix / col_sums priorities = normalized.mean(axis=1) # Calculate consistency weighted_sum = pairwise_matrix @ priorities lambda_max = np.mean(weighted_sum / priorities) # Consistency Index ci = (lambda_max - n) / (n - 1) if n > 1 e
- Overview
- Capabilities
- 1. Analytic Hierarchy Process (AHP)
- 2. TOPSIS Analysis
- 3. Weighted Scoring Method
- 4. Sensitivity Analysis
- 5. Criteria Weighting Methods
- 6. Decision Matrix Visualization
- Process Integration
- Output Format
- Best Practices
- Constraints
What does the mcda-analyzer skill do?
Multi-criteria decision analysis skill with AHP, TOPSIS, and weighted scoring methods.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill mcda-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.