Agent Decision Engine

Autonomous AI decision engine with multi-objective optimization, risk assessment, decision trees, and reinforcement learning for robust decision-making.

Security Scan

The skill's README (SKILL.md) claims a single DecisionEngine class and shows usage that does not match the actual code files — the code provides separate modules but no combined DecisionEngine API; otherwise there are no network calls or credential requests.

Purpose & Capability

The SKILL.md and description claim an integrated "DecisionEngine" class that exposes methods like optimize(), assessRisk(), buildDecisionTree(), and qLearn() on a single instance. The code in src/ exports separate modules (DecisionTree, RiskAssessor, MultiObjective, ReinforcementLearner) and default-exports DecisionTree; there is no DecisionEngine wrapper class. That mismatch means the stated purpose (a single combined engine) is not implemented by the provided code.

Instruction Scope

SKILL.md usage examples instruct importing { DecisionEngine } from './src/index.js' and calling engine.qLearn(...). Those instructions will fail against the provided src/index.js exports. Aside from the API mismatch, the instructions are otherwise narrow and do not request reading files, credentials, or sending data externally — no scope creep to external endpoints is present.

Install Mechanism

No install spec is provided (instruction-only install), and all code is included in the package. There are no downloads, postinstall scripts, or external installers. This is low-risk from an installation mechanism standpoint.

Credentials

The skill declares no required environment variables, no credentials, and no config paths. The code does not reference external secrets or environment access. Requested privileges are proportionate (none).

Persistence & Privilege

Flags indicate always:false and default autonomous invocation allowed. The skill does not request persistent system privileges, does not modify other skills, and does not perform any self-enablement steps in the package.

What to consider before installing

This package appears to implement decision-making modules (decision tree, risk assessor, multi-objective optimizer, and a Q-learning learner) but the SKILL.md shows a different API (a single DecisionEngine class) that does not exist in src/. Before installing or enabling this skill: 1) Do not assume the SKILL.md usage examples will work — either the author forgot to include a wrapper class, or the documentation is stale. 2) Inspect src/index.js and the module exports; if you expect a single DecisionEngine API, ask the author to provide the wrapper or adapt your code to import the modules individually (e.g., import { MultiObjective, RiskAssessor, ReinforcementLearner } from './src'). 3) Verify tests locally (npm run test) to ensure the modules run as you expect. 4) Note there is no homepage/author and the owner ID is an opaque string—treat the source as untrusted until provenance is confirmed. 5) Although there are no network calls or credential requests in the code, always run third-party code in a sandbox or isolated environment before using it with sensitive data. If the project is updated to include a DecisionEngine implementation consistent with SKILL.md (or the documentation is corrected), the mismatch concern would be resolved and the skill could be considered coherent.

Autonomous decision engine for AI agents with multi-objective optimization, risk assessment, decision trees, and reinforcement learning capabilities.

Features

• Multi-Objective Optimization: Pareto optimization with configurable weights and constraints
• Risk Assessment: Probability evaluation, impact analysis, and risk matrices
• Decision Trees: Build, evaluate, prune, and visualize decision paths
• Reinforcement Learning: Q-Learning with customizable reward functions

Usage

import { DecisionEngine } from './src/index.js';

const engine = new DecisionEngine();

// Multi-objective optimization
const result = engine.optimize([
  { name: 'cost', value: 100, weight: 0.4, minimize: true },
  { name: 'quality', value: 85, weight: 0.6, minimize: false }
]);

// Risk assessment
const risk = engine.assessRisk({
  probability: 0.3,
  impact: 0.8,
  mitigation: ['backup plan', 'monitoring']
});

// Decision tree
const tree = engine.buildDecisionTree({
  options: ['A', 'B', 'C'],
  outcomes: [0.7, 0.5, 0.9]
});

// Q-Learning
const action = engine.qLearn({
  state: [1, 0, 1],
  actions: ['move', 'stay', 'attack'],
  reward: 10
});

API

DecisionEngine

Main class combining all decision-making capabilities.

optimize(objectives, constraints)

Multi-objective optimization with Pareto front.

assessRisk(riskConfig)

Evaluate and score risks.

buildDecisionTree(config)

Build and evaluate decision trees.

qLearn(config)

Q-Learning for sequential decision making.

License

MIT