Codebase Graph
Map any repo into an interactive D3 dependency graph plus a Markdown onboarding guide: entry points, module relationships, circular dependencies, and dead-code candidates, with refactor suggestions. Parses TypeScript, Python, Java, Go, and Rust imports, exports, and calls. Self-contained HTML, no source changes.
- Generate an interactive D3 visualization of module dependencies
- Identify circular dependencies and dead code candidates automatically
- Identify entry points and core modules for rapid developer onboarding
$25
· or 125 creditsSecure checkout via Stripe
Included in download
- Generate an interactive D3 visualization of module dependencies
- Identify circular dependencies and dead code candidates automatically
- terminal, file_write, file_read automation included
- Ready for TypeScript (imports
Sample input
Analyze this repository's structure to identify the main entry points, check for circular dependencies, and find any unused files or core architectural modules.
Sample output
📦 Entry Points: src/main.ts 🔄 Cycles Detected: src/auth.ts -> src/user.ts -> src/auth.ts ⚠️ Dead Code: src/utils/legacy-formatter.ts (0 inbound refs) 🚀 Core Modules: src/app.ts (Centrality: 0.82) Refactor: Split 'auth.ts' types to break the circular dependency with 'user.ts'.
Map any repo into an interactive D3 dependency graph plus a Markdown onboarding guide: entry points, module relationships, circular dependencies, and dead-code candidates, with refactor suggestions. Parses TypeScript, Python, Java, Go, and Rust imports, exports, and calls. Self-contained HTML, no source changes.
$25
· or 125 creditsSecure checkout via Stripe
Also available in a bundle
Included in download
- Generate an interactive D3 visualization of module dependencies
- Identify circular dependencies and dead code candidates automatically
- terminal, file_write, file_read automation included
- Ready for TypeScript (imports
- Instant install
Sample input
Analyze this repository's structure to identify the main entry points, check for circular dependencies, and find any unused files or core architectural modules.
Sample output
📦 Entry Points: src/main.ts 🔄 Cycles Detected: src/auth.ts -> src/user.ts -> src/auth.ts ⚠️ Dead Code: src/utils/legacy-formatter.ts (0 inbound refs) 🚀 Core Modules: src/app.ts (Centrality: 0.82) Refactor: Split 'auth.ts' types to break the circular dependency with 'user.ts'.
About This Skill
What it does
The Codebase Graph skill transforms complex, unfamiliar repositories into interactive visual maps and structured onboarding guides. It parses your project's directory structure, imports, and configuration files to build a comprehensive dependency model. Unlike manual inspection, it automatically identifies how data flows through your application, pinpointing where execution starts and where bottlenecks or technical debt reside.
Why use this skill
Onboarding a new developer or planning a major refactor can take days of manual code tracing. This skill automates that process by providing a high-fidelity graph that reveals hidden relationships between modules. It goes beyond simple file-tree views by detecting circular dependencies, identifying likely dead code, and ranking modules by their "centrality" to the architecture. It's an essential tool for developers who need to understand a large codebase quickly without getting lost in the weeds.
Supported tools
- Languages: Multi-language support including TypeScript, JavaScript, and Python.
- Parsing: Uses AST-based analysis and framework-native metadata (like package.json) for high accuracy.
- Visualization: Generates a standalone, interactive D3.js force-directed graph.
- Reports: Produces detailed Markdown summaries including entry points and refactor suggestions.
Use Cases
- Generate an interactive D3 visualization of module dependencies
- Identify circular dependencies and dead code candidates automatically
- Identify entry points and core modules for rapid developer onboarding
- Generate a markdown report with specific refactoring recommendations
Known Limitations
First run on large codebases (10k+ files) may take 30-60 seconds. Generated graph can be large; use filters to reduce complexity. Dynamic imports and reflection-based dependencies may be missed. Python's dynamic typing limits call graph accuracy.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/codebase-graph -o /tmp/codebase-graph.zip && unzip -o /tmp/codebase-graph.zip -d ~/.claude/skills && rm /tmp/codebase-graph.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Security Scanned
Passed automated security review
Permissions
Allowed Hosts
File Scopes
Codebase Graph needs terminal access to run language parsers (esprima for JavaScript/TypeScript, tree-sitter for Python/Go/Java/Rust). These are read-only parsers that extract structural information from source files. File read access allows the skill to scan directories and read source code to analyze imports, exports, class inheritance, and function call dependencies. Only reads files with supported extensions; ignores binary files and node_modules. File write access creates HTML visualization files and markdown summaries in a `./code-graph/` directory. All output files are safe to gitignore. The skill never modifies source code. No browser, network, or environment variable access is required. The generated HTML graph is self-contained and runs locally.
Compatible agents: - Claude Code - Codex Supported languages: - JavaScript/TypeScript (imports, exports, calls) - Python (imports, class inheritance) - Go (package imports, struct embedding) - Java (imports, class extensions) - Rust (module imports, trait implementations) Output formats: - Interactive HTML graph (D3.js, searchable, zoomable) - Markdown summary report - JSON graph data for custom processing No external API calls. All parsing happens locally.
Creator
JustHandled Labs builds focused agent skills for the work nobody wants to do by hand. Each one is a single repeatable job done well: catching the security and data mistakes that quietly ship, keeping docs and tests honest, gating the commands an agent is about to run, sharpening writing, and handling the founder chores around launches, outreach, and brand setup. Not generic AI productivity. Specific workflows that are easy to run, review, and repeat. Maintained by H.J. Westerfield, with a background in communications, editing, project coordination, customer support, and practical AI systems. Tools for people who want useful automation without theatrical complexity.
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