valora.ai

mcp
Security Audit
Fail
Health Warn
  • No license — Repository has no license file
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 17 GitHub stars
Code Fail
  • rm -rf — Recursive force deletion command in .devcontainer/prepare-terminal.sh
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
This tool is an orchestration platform that coordinates a network of specialized AI agents to automate the entire software development lifecycle. It handles everything from initial planning and architecture to code generation and deployment validation.

Security Assessment
Overall Risk: Medium. The repository contains a `rm -rf` recursive force deletion command inside a development container script (`.devcontainer/prepare-terminal.sh`). While often used for cleaning up build artifacts, recursive force deletions can be dangerous if a path variable is ever empty or undefined, potentially wiping critical system files. The tool does not request explicitly dangerous base permissions. Because it is designed to orchestrate software development, expect it to execute shell commands, make network requests to external LLM APIs (OpenAI, Anthropic, Google), and access your local file system heavily to read and write project code. No hardcoded secrets were detected.

Quality Assessment
The project appears to be actively maintained, with the most recent code push occurring today. It has a small but growing level of community trust, evidenced by 17 GitHub stars. However, there is a major legal and usage concern: the repository lacks a formal license file. Despite the README displaying an "MIT" badge, the actual repository has no license terms defined, which technically means default restrictive copyright laws apply. This absence makes it legally risky to use, modify, or distribute in commercial or open-source projects.

Verdict
Use with caution due to a dangerous recursive deletion command in a script and the complete absence of a software license.
SUMMARY

VALORA (Versatile Agent Logic for Orchestrated Response Architecture) - The future of software development is not about replacing developers, but amplifying their capabilities with intelligent AI collaboration

README.md

VALORA

VALORA

The future of software development is not about replacing developers, but amplifying their capabilities with intelligent AI collaboration.

FeaturesQuick StartArchitectureCommandsDocumentation

Version Node TypeScript License

Anthropic OpenAI Google Cursor Local


🏛️ About VALORA

VALORA (Versatile Agent Logic for Orchestrated Response Architecture) is a next-generation TypeScript-based platform designed to orchestrate a sophisticated network of AI agents to automate the complete software development lifecycle. By moving beyond simple "code generation", VALORA manages the delicate interplay between requirements, architecture, and deployment. VALORA provides intelligent automation while maintaining human oversight.

Why VALORA?

Intelligent Orchestration: VALORA coordinates 11 specialised AI agents, from @lead technical oversight to @secops-engineer compliance, ensuring the right expert is assigned to every task.

Three-Tier Flexibility: The engine adapts to your resources, offering MCP Sampling, Guided Completion, or API Fallback modes.

Phased Governance: Every project follows a rigorous 8-phase lifecycle, moving from initialisation and planning through implementation to validation and PR creation.

Strategic Optimisation: To balance depth and speed, VALORA assigns specific LLMs (like GPT-5 for planning or Claude Haiku for validation) based on the task's complexity.

VALORA is not a replacement for the developer; it is the high-fidelity instrument through which the developer conducts a full symphony of AI agents.

✨ Features

🤖 Multi-Agent Collaboration

11 specialised AI agents with distinct expertise:

  • @lead — Technical oversight & architecture
  • @product-manager — Requirements & prioritisation
  • @software-engineer-* — Implementation specialists
  • @platform-engineer — Infrastructure & DevOps
  • @qa — Testing & quality assurance
  • @secops-engineer — Security & compliance
  • @ui-ux-designer — Design & accessibility

⚡ Three-Tier Execution

Flexible execution modes for every use case:

Tier Mode Cost
1 MCP Sampling Free*
2 Guided Completion Free
3 API Fallback Pay-per-use
3 Local Models Free**

*When available in Cursor
**Requires a running local model server (e.g. Ollama)

Zero configuration required — works immediately with your Cursor subscription.

💎 Model Optimisation

Strategic AI model assignment for cost efficiency:

Model Use Case
GPT-5 Thinking Deep analysis, planning
Claude Sonnet Implementation, reviews
Claude Haiku Fast tasks, validation

31% strategic • 31% execution • 38% fast

🔒 Security & Compliance

Enterprise-grade security controls:

  • Credential Guard — Env var redaction, output scanning, sensitive file blocking
  • Command Guard — Blocks exfiltration, network, eval, and remote access patterns
  • Prompt Injection Detection — Risk-scored scanning of tool results with quarantine/redaction
  • MCP Hardening — Tool definition validation, tool-set drift detection, approval workflows
  • Supply Chain Hardening — Frozen lockfile, blocked install scripts, vulnerability overrides
  • Audit Logging — Complete operation trail with security event tracking

🌳 Worktree Dashboard & Statistics

Live visibility into parallel explorations:

  • Worktree Diagram Panel — Real-time tree view of git worktrees in the valora dash dashboard
  • Exploration Status — Color-coded branches with status icons (▶ running, ✓ completed, ✗ failed)
  • Session-Exploration Linking — Explorations create linked sessions; the dashboard shows exploration details (task, worktrees, status) in the session details view
  • Worktree Usage Stats — Per-session tracking of worktree creation, concurrency, and duration
┌─ Git Worktrees (3) ──────────┐
│ ● main  abc1234              │
│ ├── exploration/exp-abc-jwt  │
│ │   def5678  ▶ RUNNING       │
│ └── feature/new-api          │
│     ghi9012                  │
└──────────────────────────────┘

🔌 External MCP Integration

Connect to 15 external MCP servers with user approval:

Category Servers
Browser/Test Playwright, Chrome DevTools, BrowserStack
Design Figma, Storybook
Development GitHub, Serena, Context7
Infrastructure Terraform, Firebase, Google Cloud
Data MongoDB, Elastic
Observability Grafana, DeepResearch

🚀 Quick Start

Prerequisites

  • Node.js 18+

Installation

# Install globally
pnpm add -g @windagency/valora          # pnpm
yarn global add @windagency/valora      # yarn
npm install -g @windagency/valora       # npm

# Verify installation
valora --version
# Should output: 2.3.1

Project Setup

Initialise VALORA in your project:

cd your-project
valora init         # Minimal setup (.valora/config.json)
valora init --full  # Full setup with override directories

Your First Command

# Create an implementation plan
valora plan "Add user authentication with OAuth"

The engine will:

  1. Select the appropriate agent (@lead)
  2. Gather codebase context
  3. Generate a detailed implementation plan
  4. Provide step-by-step guidance

Zero-Config Usage with Cursor subscription

No API keys? No problem. The engine works immediately using Guided Completion Mode:

valora plan "Add dark mode toggle"
# → Generates structured prompt for Cursor AI
# → Uses your Cursor subscription (free)

Optional: API Configuration

For fully autonomous execution with cloud providers:

valora config setup --quick

# Or set environment variables
export ANTHROPIC_API_KEY=sk-ant-...
export OPENAI_API_KEY=sk-...

Optional: Local Models (No API Key)

Run fully offline with Ollama or any OpenAI-compatible server:

# Install and start Ollama
ollama pull llama3.1
ollama serve

# Use it directly
valora plan "Add auth" --provider local --model llama3.1

# Or configure as default
export LOCAL_BASE_URL=http://localhost:11434/v1
export LOCAL_DEFAULT_MODEL=llama3.1

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                                 VALORA                                  │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   ┌─────────────┐  ┌──────────────┐  ┌─────────────┐  ┌─────────────┐   │
│   │ CLI Layer   │  │ Orchestrator │  │ Agent Layer │  │ LLM Layer   │   │
│   │             │──│              │──│             │──│             │   │
│   │ • Commands  │  │ • Pipeline   │  │ • Registry  │  │ • Anthropic │   │
│   │ • Wizard    │  │ • Executor   │  │ • Selection │  │ • OpenAI    │   │
│   │ • Output    │  │ • Context    │  │ • Loading   │  │ • Google    │   │
│   │             │  │              │  │             │  │ • Local     │   │
│   └─────────────┘  └──────────────┘  └─────────────┘  └─────────────┘   │
│                                                                         │
│   ┌─────────────┐  ┌──────────────┐  ┌─────────────┐  ┌─────────────┐   │
│   │ Session     │  │ Config       │  │ MCP         │  │ Services    │   │
│   │             │  │              │  │             │  │             │   │
│   │ • State     │  │ • Loader     │  │ • Server    │  │ • Logging   │   │
│   │ • Context   │  │ • Schema     │  │ • Tools     │  │ • Cleanup   │   │
│   │ • History   │  │ • Providers  │  │ • Prompts   │  │ • Utils     │   │
│   └─────────────┘  └──────────────┘  └─────────────┘  └─────────────┘   │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Key Design Principles

Principle Implementation
Modularity Loosely coupled components with clear interfaces
Extensibility Plugin architecture for agents, commands, providers
Testability Comprehensive test suites (unit, integration, e2e)
Observability Structured logging and session tracking
Resilience Graceful fallbacks and error recovery

📋 Commands

Complete Command Reference

Command Agent Description
refine-specs @product-manager Collaboratively refine specifications
create-prd @product-manager Generate Product Requirements Document
create-backlog @product-manager Decompose PRD into tasks
fetch-task @product-manager Retrieve next priority task
refine-task @product-manager Clarify task requirements
gather-knowledge @lead Analyse codebase context
plan @lead Create implementation plan
review-plan @lead Validate plan quality
implement Dynamic Execute code changes
assert @asserter Validate implementation
test @qa Execute test suites
review-code @lead Code quality review
review-functional @lead Functional review
commit @lead Create conventional commits
create-pr @lead Generate pull request
feedback @product-manager Capture outcomes

Command Categories

┌─────────────────────┐  ┌─────────────────────┐  ┌─────────────────────┐
│ Planning            │  │ Implementation      │  │ Delivery            │
├─────────────────────┤  ├─────────────────────┤  ├─────────────────────┤
│ • refine-specs      │  │ • implement         │  │ • commit            │
│ • create-prd        │  │ • assert            │  │ • create-pr         │
│ • plan              │  │ • test              │  │ • feedback          │
│ • review-plan       │  │ • review-code       │  │                     │
│ • gather-knowledge  │  │ • review-functional │  │                     │
└─────────────────────┘  └─────────────────────┘  └─────────────────────┘

📚 Documentation

👤 User Guide

Getting started, workflows,
and daily usage

User Guide

💻 Developer Guide

Architecture, codebase,
and contributions

Developer Guide

🏛️ Architecture

System design
and decisions

Architecture

Documentation Structure

documentation/
├── README.md                    # Documentation entry point
├── user-guide/                  # For users
│   ├── quick-start.md           # 5-minute getting started
│   ├── workflows.md             # Common patterns
│   └── commands.md              # Command reference
├── developer-guide/             # For developers
│   ├── setup.md                 # Development environment
│   ├── codebase.md              # Code structure
│   └── contributing.md          # How to contribute
├── architecture/                # For architects
│   ├── system-architecture.md   # C4 diagrams
│   ├── components.md            # Component design
│   └── data-flow.md             # Data flow patterns
└── adr/                         # Decision records
    ├── 001-multi-agent-architecture.md
    ├── ...
    ├── 008-pretooluse-cli-enforcement.md
    └── 009-supply-chain-hardening.md

🎯 Use Cases

New Feature Development

valora refine-specs "User authentication with OAuth"
valora create-prd
valora create-backlog
valora fetch-task && valora plan
valora implement
valora review-code && valora commit
valora create-pr

Bug Fix Workflow

valora plan "Fix: Login timeout issue"
valora implement
valora test --type=all
valora commit --scope=fix

Code Review

valora review-code --focus=security
valora review-functional --check-a11y=true

🔧 Project Structure

valora/                          # npm package root
├── bin/                         # CLI entry points
│   ├── valora.js                # Main CLI
│   └── mcp.js                   # MCP server
├── src/                         # TypeScript source
│   ├── ast/                     # AST-based code intelligence (tree-sitter parsing, symbol index)
│   ├── cli/                     # Command-line interface
│   ├── config/                  # Configuration management
│   ├── executor/                # Pipeline execution
│   ├── llm/                     # LLM provider integrations
│   ├── lsp/                     # LSP integration (language server protocol client)
│   ├── mcp/                     # MCP server implementation
│   ├── security/                # Agentic AI security (credential, command, injection guards)
│   ├── session/                 # Session management
│   │   └── worktree-stats-tracker.ts  # Worktree usage statistics
│   ├── ui/                      # Terminal UI (dashboard, panels)
│   ├── utils/                   # Utilities & path resolution
│   └── ...
├── data/                        # Built-in resources (shipped with package)
│   ├── agents/                  # Agent definitions (11 agents)
│   ├── commands/                # Command specifications (24 commands)
│   ├── prompts/                 # Structured prompts by phase
│   ├── templates/               # Document templates
│   ├── hooks/                   # Hook scripts
│   ├── config.default.json      # Default configuration
│   ├── hooks.default.json       # Default hooks config
│   └── external-mcp.default.json # External MCP server registry
├── dist/                        # Compiled output (gitignored)
├── tests/                       # Test suites
├── documentation/               # Comprehensive docs
└── package.json

Project-Level Overrides (.valora/)

When installed in a project, VALORA supports a .valora/ directory for local overrides:

.valora/                         # Project-specific configuration
├── config.json                  # Project settings (overrides defaults)
├── agents/                      # Custom/override agent definitions
├── commands/                    # Custom/override command specs
├── prompts/                     # Custom/override prompts
├── templates/                   # Custom/override templates
├── sessions/                    # Session state (gitignored)
├── logs/                        # Execution logs (gitignored)
├── index/                       # Codebase symbol index (gitignored)
└── cache/                       # Cache data (gitignored)

Resources in .valora/ take precedence over built-in data/ resources.


🌟 Why VALORA?

Traditional Development

  • ❌ Context switching between tools
  • ❌ Manual documentation
  • ❌ Inconsistent code reviews
  • ❌ Repetitive commit messages
  • ❌ Time-consuming PR creation

With AI Orchestration

  • ✅ Unified workflow automation
  • ✅ Auto-generated documentation
  • ✅ Comprehensive AI-powered reviews
  • ✅ Intelligent commit messages
  • ✅ One-command PR creation

Innovation Highlights

Innovation Impact
Multi-Agent Orchestration Specialised agents produce expert-level output
Three-Tier Execution Flexibility from free to fully automated
Session Persistence Context flows naturally between commands
Dynamic Agent Selection Right expert for every task
Quality Gates Multiple checkpoints prevent technical debt

🛠️ Technology Stack

Category Technologies
Runtime Node.js 18+, TypeScript 5.x
Package Manager pnpm 10.x
Build tsc, tsc-alias
Testing Vitest, Playwright
LLM SDKs @anthropic-ai/sdk, openai, @google/generative-ai
CLI UI Ink (React), Chalk, Commander
Validation Zod
Code Intelligence web-tree-sitter
MCP @modelcontextprotocol/sdk

📄 Licence

MIT © Damien TIVELET


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