- Home
- Skills
- Sopaco
- Deepwiki Rs
- Skill Litho
skill-litho_skill
- Rust
677
GitHub Stars
5
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill sopaco/deepwiki-rs --skill skill-litho- configuration.md3.8 KB
- examples.md6.7 KB
- integration.md11.3 KB
- SKILL.md3.0 KB
- troubleshooting.md8.0 KB
Overview
This skill is an AI-powered Rust documentation generation engine that turns codebases into clear, structured technical documentation and C4 architecture diagrams. It analyzes source code across languages, produces architecture models, and generates professional specs ready for teams and intelligent agents. Use it to automate documentation, reveal system structure, and create AI-ready context for downstream tools.
How this skill works
The engine scans the repository, extracts code structure, and builds multi-level C4 models (Context, Container, Component, Code). It uses configurable LLMs for research and natural-language drafting, and outputs a navigable documentation tree with diagrams and detailed topic deep-dives. Optional flags let you choose fast or powerful models, skip preprocessing, or integrate into CI pipelines.
When to use it
- You need a complete C4 architecture set (Context, Container, Component, Code).
- You want automated, repeatable documentation in CI/CD for evolving codebases.
- You must generate technical specifications and design decision records from source.
- You need quick codebase insights without a full documentation run.
- You want to create AI-ready context bundles for other agents or tools.
Best practices
- Start with a focused path using -p to limit scope for large repos and iterate.
- Use --model-efficient for preliminary scans and --model-powerful for final drafts to balance cost and quality.
- Batch large repositories and enable skipping phases (--skip-preprocessing) when memory is limited.
- Place generated docs under version control and include them in CI for continuous updates.
- Review generated architecture diagrams and add domain-specific corrections before publishing.
Example use cases
- Generate a full project-docs/ tree with architecture overview, workflow, and deep dives for onboarding new engineers.
- Run quick analysis on a pull request to summarize code changes and impacted components.
- Embed in CI to refresh documentation automatically when the main branch changes.
- Produce C4 diagrams for an executive architecture review or technical roadmap.
- Create AI-ready context artifacts for downstream agents to improve code search and automated code review.
FAQ
It supports Rust, Python, Java, Go, C#, JavaScript/TypeScript and can analyze any language with recognizable structure.
How do I balance speed vs. depth?
Use --model-efficient for fast scans and drafts, then run with --model-powerful for deep analysis and final documentation; combine both in a two-stage workflow.