decision-tree-analyzer
Decision tree analysis skill with expected value, risk analysis, and utility theory.
npx skills add a5c-ai/babysitter --skill decision-tree-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.
# decision-tree-analyzer You are **decision-tree-analyzer** - a specialized skill for decision tree analysis including expected value calculations, risk analysis, and utility theory applications. ## Overview This skill enables AI-powered decision tree analysis including: - Decision tree construction - Expected Monetary Value (EMV) calculation - Expected Value of Perfect Information (EVPI) - Expected Value of Sample Information (EVSI) - Risk profiles and sensitivity - Utility function application - Decision rollback analysis - Multi-stage sequential decisions ## Capabilities ### 1. Decision Tree Construction ```python import numpy as np from dataclasses import dataclass from typing import List, Dict, Optional from enum import Enum class NodeType(Enum): DECISION = "decision" CHANCE = "chance" TERMINAL = "terminal" @dataclass class TreeNode: node_id: str node_type: NodeType name: str value: float = 0 # For terminal nodes probability: float = 1.0 # For chance branches children: List['TreeNode'] = None parent: Optional['TreeNode'] = None def __post_init__(self): if self.children is None: self.children = [] def build_decision_tree(structure: dict): """ Build decision tree from structure
- Overview
- Capabilities
- 1. Decision Tree Construction
- 2. Expected Monetary Value (EMV)
- 3. Expected Value of Perfect Information (EVPI)
- 4. Risk Profile Analysis
- 5. Utility Function Analysis
- 6. Sensitivity Analysis
- Process Integration
- Output Format
- Best Practices
- Constraints
What does the decision-tree-analyzer skill do?
Decision tree analysis skill with expected value, risk analysis, and utility theory.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill decision-tree-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.