Shotgun
Shotgun is a CLI tool that transforms what you want to work on into a complete flow of research to specs to plans to tasks to implementation with full codebase understanding.
Overview
Shotgun is a Python-based command-line tool designed to transform abstract development ideas into structured implementation flows. Through five core modes: Research, Specify, Plan, Tasks, and Implement, it helps developers complete the full journey from concept to code with AI assistance. Before any operation, Shotgun indexes the entire codebase to build a searchable code graph, ensuring all decisions are based on actual code structure and dependencies, providing more accurate contextual understanding and recommendations.
Key Features
- Five Core Modes: Research, Specify, Plan, Tasks, and Implement form a complete development workflow.
- Complete Codebase Understanding: Indexes the entire codebase before starting any work, building a real-time code graph.
- Deterministic Artifacts: Generated specs, plans, and tasks are version-controlled Markdown documents for easy review and iteration.
- Multi-Source Query: Simultaneously query codebase, web, GitHub, and docs for comprehensive research foundation.
- Export Capability: Supports export to agents.md ecosystem, compatible with various code generation tools.
Use Cases
- New Developer Onboarding: Quickly map the entire architecture and generate documentation that matches actual code.
- Refactoring Projects: Fully understand dependencies before making changes, preventing refactors from becoming rewrites.
- New Feature Development: Precisely locate feature placement and prevent duplicate implementations.
- Project Migration: Map legacy systems, plan new architecture, track change deltas, and migrate in safe stages.
- Team Collaboration: Generate version-controlled spec documents to facilitate knowledge sharing and decision recording.
Technical Highlights
- Built on Python with pipx for isolated installation, deployable in 30 seconds.
- Supports multiple LLM providers including OpenAI, Anthropic, and Gemini.
- Real-time code graph technology ensures all recommendations are based on the latest code state.
- Human-in-the-loop checkpoints require human review at key decision points, maintaining control.
- Telemetry and change tracking features reduce rework and late-night incidents.