Shotgun CLI

Product Introduction

1. Shotgun CLI is a local-first command line interface tool that leverages multiple AI agents to generate technical specifications and research artifacts for software development projects. It integrates directly with code repositories to maintain context awareness while producing decision-ready documentation. The tool operates entirely on your local machine using your own AI API keys, ensuring data privacy and control.

2. The core value of Shotgun CLI lies in its ability to eliminate ambiguity between developer intent and AI-generated outputs by creating precise technical specs upfront. It reduces iterative debugging cycles with AI code-generation tools by 60-80% through structured research, architecture planning, and solution design phases. This ensures AI assistants like Cursor or Claude Code produce functionally accurate code aligned with project requirements.

Main Features

1. Shotgun CLI automatically scans and analyzes your Git repository to maintain full context awareness during specification generation, including existing code structure, dependencies, and architectural patterns. This repo-aware design prevents redundant or conflicting outputs from AI code-generation tools.
2. The tool deploys four specialized AI agents (Research, Product Requirements, Architecture, and Solution Design) that collaborate to break down complex problems into executable technical plans. Each agent operates with distinct temperature settings and model configurations optimized for its role.
3. Shotgun CLI implements a local-first architecture where all AI processing occurs on your machine using your own API keys for OpenAI, Anthropic, or other LLM providers. Specifications are saved as Markdown files in your project directory with full version control integration.

Problems Solved

1. The product addresses the 72% error rate in initial AI-generated code outputs caused by vague or incomplete requirements. Shotgun CLI eliminates this by forcing explicit technical specification before code generation begins.
2. It specifically targets software engineers and technical leads who use AI coding assistants like Cursor, GitHub Copilot, or Claude Code but struggle with output quality.
3. Typical scenarios include migrating legacy systems with AI assistance, implementing complex features requiring cross-service coordination, and onboarding new developers to existing codebases through AI-generated documentation.

Unique Advantages

1. Unlike generic AI documentation tools, Shotgun CLI combines repository context analysis with multi-agent debate systems that simulate technical planning sessions between senior engineers. This produces specifications with 40% more implementation-ready details than single-model approaches.
2. The local-first architecture enables enterprise-grade security by never transmitting code or specifications to third-party servers, while still supporting multiple AI providers through configurable API endpoints.
3. Competitive advantages include native integration with VS Code via extension, automatic generation of architecture decision records (ADRs), and compatibility with 18+ LLMs through a unified configuration interface.

Frequently Asked Questions (FAQ)

1. How does Shotgun CLI handle large monorepos with multiple services? The tool performs dependency mapping through package.json/Makefile analysis and creates scoped specifications using service boundary detection algorithms, preventing cross-contamination of context between unrelated components.
2. Can I use multiple AI models simultaneously? Yes, Shotgun CLI supports model stacking where different agents can use separate LLMs (e.g., Claude-3 for research, GPT-4 for architecture), with automatic output validation through consensus scoring.
3. What security measures protect my API keys? All credentials are stored in AES-256 encrypted .env files local to your machine, with automatic key rotation support and zero phoning home to Shotgun servers.
4. How does specification versioning work? Generated specs include Git commit hashes in their headers and automatically create pull request summaries when pushed to remote repositories.
5. What IDEs or editors are supported? The CLI outputs standard Markdown files compatible with any editor, while dedicated extensions for VS Code and JetBrains IDEs provide real-time spec previews and AI feedback loops.