rust-deps-visualizer
ASCII art visualization of Rust project dependency trees with optional feature flag display.
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What it does
Generates tree-format dependency graphs with configurable depth (default: 3 levels) and optional feature flag annotations
Supports visual enhancements including dependency categorization (runtime, serialization, development) and optional size visualization in megabytes
Parses cargo metadata and cargo tree output to extract and format dependencies with standard box-drawing characters
Trigge
Installation Guide
How to use rust-deps-visualizer on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
rust-deps-visualizer
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches rust-deps-visualizer from zhanghandong/rust-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate rust-deps-visualizer. Access via /rust-deps-visualizer in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Rust Dependencies Visualizer
Generate ASCII art visualizations of your Rust project's dependency tree.
Usage
/rust-deps-visualizer [--depth N] [--features]
Options:
--depth N: Limit tree depth (default: 3)--features: Show feature flags
Output Format
Simple Tree (Default)
my-project v0.1.0
├── tokio v1.49.0
│ ├── pin-project-lite v0.2.x
│ └── bytes v1.x
├── serde v1.0.x
│ └── serde_derive v1.0.x
└── anyhow v1.x
Feature-Aware Tree
my-project v0.1.0
├── tokio v1.49.0 [rt, rt-multi-thread, macros, fs, io-util]
│ ├── pin-project-lite v0.2.x
│ └── bytes v1.x
├── serde v1.0.x [derive]
│ └── serde_derive v1.0.x (proc-macro)
└── anyhow v1.x [std]
Implementation
Step 1: Parse Cargo.toml for direct dependencies
cargo metadata --format-version=1 --no-deps 2>/dev/null
Step 2: Get full dependency tree
cargo tree --depth=${DEPTH:-3} ${FEATURES:+--features} 2>/dev/null
Step 3: Format as ASCII art tree
Use these box-drawing characters:
├──for middle items└──for last items│for continuation lines
Visual Enhancements
Dependency Categories
my-project v0.1.0
│
├─[Runtime]─────────────────────
│ ├── tokio v1.49.0
│ └── async-trait v0.1.x
│
├─[Serialization]───────────────
│ ├── serde v1.0.x
│ └── serde_json v1.x
│
└─[Development]─────────────────
├── criterion v0.5.x
└── proptest v1.x
Size Visualization (Optional)
my-project v0.1.0
├── tokio v1.49.0 ████████████ 2.1 MB
├── serde v1.0.x ███████ 1.2 MB
├── regex v1.x █████ 890 KB
└── anyhow v1.x ██ 120 KB
─────────────────
Total: 4.3 MB
Workflow
- Check for Cargo.toml in current directory
- Run
cargo treewith specified options - Parse output and generate ASCII visualization
- Optionally categorize by purpose (runtime, dev, build)
Related Skills
| When | See |
|---|---|
| Crate selection advice | m11-ecosystem |
| Workspace management | m11-ecosystem |
| Feature flag decisions | m11-ecosystem |
List & Monetize Your Skill
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- TTariq Chawla★★★★★Dec 28, 2024
We added rust-deps-visualizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- IIsabella Bhatia★★★★★Dec 16, 2024
Useful defaults in rust-deps-visualizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- CCamila Chawla★★★★★Dec 16, 2024
Keeps context tight: rust-deps-visualizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
- CChaitanya Patil★★★★★Dec 12, 2024
rust-deps-visualizer has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ZZara Ramirez★★★★★Nov 23, 2024
rust-deps-visualizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- IIsabella Tandon★★★★★Nov 19, 2024
Keeps context tight: rust-deps-visualizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
- DDiego Shah★★★★★Nov 7, 2024
I recommend rust-deps-visualizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- OOlivia Yang★★★★★Nov 7, 2024
We added rust-deps-visualizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- PPiyush G★★★★★Nov 3, 2024
Solid pick for teams standardizing on skills: rust-deps-visualizer is focused, and the summary matches what you get after install.
- CCamila Agarwal★★★★★Oct 26, 2024
rust-deps-visualizer reduced setup friction for our internal harness; good balance of opinion and flexibility.
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