rust-call-graph

Visualize function call relationships using LSP call hierarchy.

actionbook/rust-skillsUpdated Apr 10, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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2

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Install Skill

Run in your terminal

$npx skills add https://github.com/actionbook/rust-skills --skill rust-call-graph

2

installs

2

this week

974

stars

Installation Guide

How to use rust-call-graph on Cursor

AI-first code editor with Composer

1

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-call-graph
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/actionbook/rust-skills --skill rust-call-graph

Fetches rust-call-graph from actionbook/rust-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/rust-call-graph

Restart Cursor to activate rust-call-graph. Access via /rust-call-graph 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 Call Graph

Visualize function call relationships using LSP call hierarchy.

Usage

/rust-call-graph <function_name> [--depth N] [--direction in|out|both]

Options:

  • --depth N: How many levels to traverse (default: 3)
  • --direction: in (callers), out (callees), both

Examples:

  • /rust-call-graph process_request - Show both callers and callees
  • /rust-call-graph handle_error --direction in - Show only callers
  • /rust-call-graph main --direction out --depth 5 - Deep callee analysis

LSP Operations

1. Prepare Call Hierarchy

Get the call hierarchy item for a function.

LSP(
  operation: "prepareCallHierarchy",
  filePath: "src/handler.rs",
  line: 45,
  character: 8
)

2. Incoming Calls (Who calls this?)

LSP(
  operation: "incomingCalls",
  filePath: "src/handler.rs",
  line: 45,
  character: 8
)

3. Outgoing Calls (What does this call?)

LSP(
  operation: "outgoingCalls",
  filePath: "src/handler.rs",
  line: 45,
  character: 8
)

Workflow

User: "Show call graph for process_request"
[1] Find function location
    LSP(workspaceSymbol) or Grep
[2] Prepare call hierarchy
    LSP(prepareCallHierarchy)
[3] Get incoming calls (callers)
    LSP(incomingCalls)
[4] Get outgoing calls (callees)
    LSP(outgoingCalls)
[5] Recursively expand to depth N
[6] Generate ASCII visualization

Output Format

Incoming Calls (Who calls this?)

## Callers of `process_request`

main
└── run_server
    └── handle_connection
        └── process_request  ◄── YOU ARE HERE

Outgoing Calls (What does this call?)

## Callees of `process_request`

process_request  ◄── YOU ARE HERE
├── parse_headers
│   └── validate_header
├── authenticate
│   ├── check_token
│   └── load_user
├── execute_handler
│   └── [dynamic dispatch]
└── send_response
    └── serialize_body

Bidirectional (Both)

## Call Graph for `process_request`

                    ┌─────────────────┐
                    │      main       │
                    └────────┬────────┘
                    ┌────────▼────────┐
                    │   run_server    │
                    └────────┬────────┘
                    ┌────────▼────────┐
                    │handle_connection│
                    └────────┬────────┘
        ┌────────────────────┼────────────────────┐
        │                    │                    │
┌───────▼───────┐   ┌───────▼───────┐   ┌───────▼───────┐
│ parse_headers │   │ authenticate  │   │send_response  │
└───────────────┘   └───────┬───────┘   └───────────────┘
                    ┌───────┴───────┐
                    │               │
             ┌──────▼──────┐ ┌──────▼──────┐
             │ check_token │ │  load_user  │
             └─────────────┘ └─────────────┘

Analysis Insights

After generating the call graph, provide insights:

## Analysis

**Entry Points:** main, test_process_request
**Leaf Functions:** validate_header, serialize_body
**Hot Path:** main → run_server → handle_connection → process_request
**Complexity:** 12 functions, 3 levels deep

**Potential Issues:**
- `authenticate` has high fan-out (4 callees)
- `process_request` is called from 3 places (consider if this is intentional)

Common Patterns

User Says Direction Use Case
"Who calls X?" incoming Impact analysis
"What does X call?" outgoing Understanding implementation
"Show call graph" both Full picture
"Trace from main to X" outgoing Execution path

Visualization Options

Style Best For
Tree (default) Simple hierarchies
Box diagram Complex relationships
Flat list Many connections
Mermaid Export to docs

Mermaid Export

graph TD
    main --> run_server
    run_server --> handle_connection
    handle_connection --> process_request
    process_request --> parse_headers
    process_request --> authenticate
    process_request --> send_response

Related Skills

When See
Find definition rust-code-navigator
Project structure rust-symbol-analyzer
Trait implementations rust-trait-explorer
Safe refactoring rust-refactor-helper

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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

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.760 reviews
  • P
    Pratham WareDec 24, 2024

    Keeps context tight: rust-call-graph is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • A
    Anika FloresDec 24, 2024

    We added rust-call-graph from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • S
    Sakura ThompsonDec 20, 2024

    rust-call-graph is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • J
    James MalhotraDec 16, 2024

    I recommend rust-call-graph for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • A
    Arjun TaylorDec 4, 2024

    Keeps context tight: rust-call-graph is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • L
    Layla RobinsonDec 4, 2024

    We added rust-call-graph from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • C
    Chinedu MensahNov 23, 2024

    Registry listing for rust-call-graph matched our evaluation — installs cleanly and behaves as described in the markdown.

  • S
    Sakshi PatilNov 15, 2024

    Registry listing for rust-call-graph matched our evaluation — installs cleanly and behaves as described in the markdown.

  • N
    Nia SharmaNov 11, 2024

    rust-call-graph reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • K
    Kofi RaoNov 7, 2024

    Solid pick for teams standardizing on skills: rust-call-graph is focused, and the summary matches what you get after install.

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