Swark
ExtensionFreeCreate architecture diagrams from code automatically using LLMs
Capabilities10 decomposed
llm-driven codebase-to-architecture diagram generation
Medium confidenceAnalyzes selected folder contents by sending full source code to GitHub Copilot, which performs language-agnostic structural inference to identify architectural components, relationships, and dependencies. Outputs Mermaid.js diagram syntax representing the inferred architecture. Uses LLM reasoning rather than deterministic AST parsing, enabling support across all programming languages without language-specific parsers.
Uses GitHub Copilot's LLM reasoning to infer architecture from source code without language-specific parsers, enabling universal language support and semantic understanding of architectural patterns that deterministic tools cannot capture. Locked exclusively to Copilot (no alternative provider support), which simplifies authentication but eliminates flexibility.
Faster than manual diagram creation and more semantically aware than regex-based code analysis tools, but less deterministic and less customizable than dedicated architecture analysis frameworks like Structurizr or PlantUML with explicit syntax.
interactive folder selection and scoped code analysis
Medium confidenceProvides a file picker dialog allowing users to select a specific folder within their VS Code workspace for analysis. Extension reads all files within the selected directory (excluding files outside workspace scope) and sends their full content to Copilot. Scope is strictly bounded to user-selected folder; no automatic recursive analysis of parent directories or external dependencies.
Provides explicit user control over analysis scope via interactive folder picker, ensuring only selected code is sent to Copilot. This is a privacy-first design choice that prevents accidental exposure of unrelated code, unlike tools that automatically analyze entire workspaces.
More privacy-conscious than tools that automatically scan entire repositories, but less convenient than automated full-codebase analysis for users who want comprehensive architecture visualization without manual folder selection.
mermaid.js diagram code generation and editing
Medium confidenceGenerates Mermaid.js diagram syntax representing the inferred architecture and writes it to a markdown file in the `swark-output` folder with timestamp-based naming (`<date>__<time>__diagram.md`). Generated Mermaid code is human-readable and fully editable post-generation, allowing users to refine or customize diagrams after creation. Output is rendered in VS Code as markdown or via external Mermaid Live Editor link.
Outputs human-editable Mermaid.js syntax rather than binary image formats, enabling post-generation refinement and version control integration. This design prioritizes flexibility and collaboration over immediate visual polish.
More editable and version-controllable than tools that output PNG/SVG images, but requires Mermaid knowledge and additional tooling for rendering compared to tools that generate ready-to-view diagrams.
github copilot-integrated authentication and api communication
Medium confidenceLeverages existing GitHub Copilot authentication within VS Code, eliminating need for separate API key configuration or credential management. Extension communicates exclusively with GitHub Copilot API (no third-party services involved) to send code for analysis and receive diagram generation instructions. Authentication state is inherited from Copilot extension; no additional setup required beyond Copilot installation.
Eliminates separate credential management by piggybacking on GitHub Copilot's existing VS Code authentication, reducing user friction and centralizing API access control. This is a deliberate architectural choice to simplify onboarding but sacrifices provider flexibility.
Simpler onboarding than tools requiring separate API key configuration, but less flexible than multi-provider tools that support OpenAI, Anthropic, and self-hosted models.
keybinding-triggered command execution
Medium confidenceProvides keyboard shortcuts (`cmd+shift+r` on macOS, `ctrl+shift+r` on Windows) that invoke the `Swark: Create Architecture Diagram` command from the command palette. Keybindings are pre-configured and trigger the full analysis-and-generation workflow without requiring menu navigation or command palette typing.
Pre-configured platform-specific keybindings (macOS vs Windows) reduce setup friction compared to tools requiring manual keybinding configuration. However, rebinding capability is undocumented, limiting customization.
Faster than command palette invocation for power users, but less discoverable than menu-based access for new users unfamiliar with keybindings.
timestamped diagram output organization
Medium confidenceAutomatically generates timestamped filenames (`<date>__<time>__diagram.md`) for each diagram and stores them in a `swark-output` folder at workspace root. Each diagram generation also produces a metadata log file containing run timestamp and list of analyzed files. This approach creates an audit trail of diagram generation history without overwriting previous diagrams.
Automatic timestamped file organization creates an implicit version history without requiring explicit versioning commands, enabling historical comparison of architecture diagrams. However, lack of cleanup strategy means users must manually manage folder growth.
Better for historical tracking than tools that overwrite diagrams, but less sophisticated than dedicated version control systems that support branching, diffing, and cleanup policies.
optional test file inclusion for coverage visualization
Medium confidenceAllows users to optionally include test files in the analysis input to enable visualization of test coverage relationships within the architecture diagram. Test files are treated as optional input metadata that Copilot can use to infer testing patterns and coverage across architectural components. Mechanism for enabling/disabling test file inclusion is undocumented.
Attempts to bridge architecture visualization and test coverage by including test files in LLM analysis, enabling semantic understanding of testing patterns. However, the feature is poorly documented and its actual output is unclear.
More integrated than separate test coverage tools, but less precise than dedicated test coverage analysis frameworks that provide quantitative metrics and detailed coverage reports.
language-agnostic code analysis via llm inference
Medium confidenceSupports all programming languages through LLM-based semantic analysis rather than language-specific parsers. Copilot infers architectural structure, components, and relationships from source code without requiring language-specific AST parsing or grammar rules. This approach enables universal language support but sacrifices determinism and precision of syntax-aware analysis.
Eliminates language-specific parser dependencies by relying on Copilot's LLM reasoning, enabling true universal language support without maintaining multiple grammar rules. This trades determinism for flexibility and ease of maintenance.
More flexible than language-specific tools like Structurizr or PlantUML that require explicit syntax, but less precise than deterministic AST-based analysis that can guarantee structural accuracy.
vs code command palette integration
Medium confidenceRegisters the `Swark: Create Architecture Diagram` command in VS Code's command palette, making the feature discoverable via `Ctrl+Shift+P` (or `Cmd+Shift+P` on macOS) command palette search. Command palette integration provides discoverability for users unfamiliar with keybindings and enables command execution from any editor state.
Standard VS Code command palette integration provides discoverability and accessibility for users unfamiliar with extension-specific keybindings, following VS Code UX conventions.
More discoverable than keybindings alone, but slower to invoke than direct keyboard shortcuts for power users.
workspace-scoped file system access with privacy boundaries
Medium confidenceRestricts file system access to the VS Code workspace root and user-selected folders within it. Extension cannot read files outside the workspace, preventing accidental exposure of system files or unrelated code. All file reading is performed within VS Code's sandbox and workspace context, enforcing privacy boundaries at the extension level.
Enforces workspace-level privacy boundaries by design, preventing accidental exposure of files outside the project scope. This is a deliberate privacy-first architectural choice that limits scope but increases security.
More privacy-conscious than tools that automatically scan entire file systems, but less convenient than tools that automatically discover and analyze all dependencies.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Swark, ranked by overlap. Discovered automatically through the match graph.
Mermaid
The official Mermaid Editor plugin by the Mermaid open source team, now with AI-powered diagramming! Create, edit and preview diagrams seamlessly within VS Code
CodeViz | Visual codebase maps
Fast codebase understanding and navigation
AppMap
AI-driven chat with a deep understanding of your code. Build effective solutions using an intuitive chat interface and powerful code visualizations.
FileScopeMCP
** - Analyzes your codebase identifying important files based on dependency relationships. Generates diagrams and importance scores per file, helping AI assistants understand the codebase. Automatically parses popular programming languages, Python, Lua, C, C++, Rust, Zig.
Fynix Code Assistant: Your Comprehensive AI Copilot, Code Generation, Ensure Code Quality, AI-Driven Flow Diagrams, and Task Execution through Natural Language Commands
Fynix Code Assistant is an advanced AI coding platform that elevates your coding experience. Whether coding, testing, or reviewing, it provides real-time AI assistance within your development environment, supporting languages like Python, JavaScript, TypeScript, Java, PHP, Go, and more.
MetaGPT
🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
Best For
- ✓Solo developers and small teams documenting existing codebases
- ✓Engineering leads onboarding new team members to complex repositories
- ✓Technical writers generating architecture documentation from source code
- ✓Teams maintaining legacy systems without existing architecture diagrams
- ✓Developers working in monorepos who want to diagram individual services or modules
- ✓Teams with privacy concerns who want to limit code exposure to specific folders
- ✓Projects where different architectural layers need separate visualizations
- ✓Teams using Mermaid.js in documentation pipelines (e.g., Markdown-based wikis, GitHub README files)
Known Limitations
- ⚠LLM-based inference is non-deterministic — same codebase may produce slightly different diagrams across runs
- ⚠No control over diagram granularity or abstraction level — Copilot determines what constitutes an 'architectural component'
- ⚠Requires GitHub Copilot authentication; cannot function offline or with alternative LLM providers
- ⚠Performance degrades with very large codebases due to token limits in Copilot API (exact limit unknown)
- ⚠No support for incremental updates — each invocation generates a new diagram from scratch
- ⚠No recursive analysis of dependencies outside selected folder — external imports/dependencies are not automatically included
Requirements
Input / Output
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Create architecture diagrams from code automatically using LLMs
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