AI Context Optimizer

AI Context Optimizer is a VS Code extension that helps developers generate higher-quality AI prompts from real workspace context instead of manual copy/paste snippets.

It is built for enterprise and large-repository workflows where relevance, token efficiency, and safety matter. The extension analyzes your workspace, builds semantic/dependency context, ranks likely-relevant files, and generates an AI-ready prompt you can use with GitHub Copilot, ChatGPT, Claude, Gemini, or other LLM chat interfaces.

Why teams use it

• Reduces time spent collecting files and code context manually.
• Improves prompt quality and consistency across developers.
• Helps control token usage with budget-aware context assembly.
• Adds local safety checks for sensitive data before sharing prompts externally.

Supported AI assistants

• GitHub Copilot
• ChatGPT
• Claude
• Gemini
• Other LLM tools that accept plain text/Markdown prompts

The generated prompt format is AI-platform independent.

✨ Features

🧠 AI Prompt Optimization

• Optimizes raw developer prompts with a validation pipeline.
• Includes quality analysis before/after optimization when enabled.

🎯 Intent Analysis

• Performs rule-based intent analysis on prompt text.
• Extracts intent signals, technologies, operations, and entities used downstream in ranking.

🔎 Smart Keyword Extraction

• Derives keyword signals from intent analysis and prompt content.
• Uses these signals in candidate generation and relevance scoring.

🔗 Dependency-aware Context Ranking

• Builds a dependency graph and applies dependency-aware ranking.
• Scores candidates using keyword, symbol, file-name, config, recency, and dependency signals.

📦 Workspace Context Assembly

• Assembles a structured context package from ranked files.
• Includes Markdown/JSON context export support internally.

📏 Smart Context Budget

• Enforces token budget limits during context assembly.
• Prioritizes higher-value context when budget is constrained.

⚡ Token Reduction

• Uses smart-context generators and section prioritization to reduce output size.
• Includes token counting command for selection/file analysis.

📊 Dashboard

• Provides a webview dashboard with optimization history and pipeline summaries.
• Displays workspace, indexing, dependency, pipeline, and Copilot workflow state.

✅ Prompt Quality Analysis

• Optional quality analyzer evaluates clarity/structure/specificity/context signals.
• Integrated into optimization workflow.

🔐 Sensitive Data Detection

• Detects common secret patterns (API keys, tokens, passwords, connection strings, etc.).
• Optional auto-masking behavior.

🕘 Prompt History

• Stores prompt/optimization history in extension global state when enabled.
• Search and clear actions are available in the dashboard.

📋 Automatic Clipboard Integration

• One-click prompt generation supports automatic clipboard copy.
• Includes explicit command to copy last generated prompt.

🧩 Multiple Prompt Styles

• Prompt generation supports formatter-driven styles.
• Implemented formatter styles: Balanced, Minimal, Detailed, Debug, ArchitectureReview.
• CodeReview and BugFix appear in configuration/options, but currently fall back to the default formatter when a dedicated formatter is not registered.

📤 Export Support

• Export generated prompt as markdown, json, or plaintext.

🏗 Architecture

User Prompt
  ↓
Intent Analysis
  ↓
Keyword Extraction
  ↓
Candidate Generation
  ↓
Dependency-aware Ranking
  ↓
Context Assembly
  ↓
Markdown Prompt Generation
  ↓
Automatic Clipboard Copy
  ↓
GitHub Copilot (or other AI assistant)

📸 Screenshots

Dashboard

(Add Screenshot)

Prompt Generation

(Add Screenshot)

Intent Analysis

(Add Screenshot)

📦 Installation

Install from VS Code Marketplace

1. Open VS Code.
2. Open Extensions (Ctrl+Shift+X / Cmd+Shift+X).
3. Search for AI Context Optimizer.
4. Select Install.

Marketplace listing: https://marketplace.visualstudio.com/items?itemName=msinha.ai-context-optimizer

Install from VSIX

1. Download the .vsix package.
2. In VS Code, open Extensions view.
3. Use the ... menu and choose Install from VSIX....
4. Select the downloaded file.

🚀 Quick Start

1. Open a workspace folder in VS Code.
2. Run AI Context Optimizer: Generate Copilot Prompt.
3. Enter your task prompt.
4. Let the extension validate/build index and dependency graph as needed.
5. Paste the generated prompt into GitHub Copilot (or another supported assistant).

⌨️ Commands

Note: Some developer-oriented commands are contributed but hidden from the default Command Palette menu visibility.

⚙️ Configuration

🤖 Supported AI Assistants

• GitHub Copilot
• ChatGPT
• Claude
• Gemini
• Other LLM assistants that accept generated prompts

Prompts are generated as structured text/Markdown and are not tied to one AI vendor.

🧪 Example Workflow

Developer Prompt
  ↓
Optimized Prompt
  ↓
GitHub Copilot
  ↓
Better AI Response

⚡ Performance

AI Context Optimizer is designed around local analysis pipelines and token-aware context construction.

• Uses dependency-aware relevance ranking to prioritize meaningful files.
• Applies adaptive token budgeting during context assembly.
• Reduces context size through smart-context generation and section prioritization.
• Supports enterprise-scale workspaces through workspace indexing and graph-based analysis.

🔐 Privacy

• Source code is processed locally inside VS Code by this extension.
• Source code does not leave your machine unless you manually submit generated content to an external AI assistant.
• Clipboard access is used to copy generated prompts when copy options are enabled.
• No telemetry pipeline is implemented in the extension codebase.
• No hidden background uploads are performed by the extension.

📋 Requirements

• VS Code: ^1.125.0
• Operating systems: Windows, macOS, Linux (where supported by VS Code)

🗺 Roadmap

Planned items:

• Explainability Engine enhancements
• Smart Context Generator enhancements
• Enhanced GitHub Copilot integration
• Workspace Intelligence improvements
• AI Recommendation capabilities

Roadmap items above are future work and not presented as currently shipped features.

🤝 Contributing

Contributions are welcome.

1. Fork the repository.
2. Create a feature branch.
3. Add or update tests where applicable.
4. Submit a pull request with a clear description.

Repository: https://github.com/msinha53/ai-context-optimizer

📄 License

MIT License. See LICENSE.

🆘 Support

• GitHub Issues: https://github.com/msinha53/ai-context-optimizer/issues
• Feature requests: open an issue with the enhancement label
• Discussions: use GitHub Discussions if enabled for the repository

📝 Changelog

See CHANGELOG.md.