rust-learner▌
zhanghandong/rust-skills · updated Apr 8, 2026
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Fetch Rust versions, crate information, and API documentation from authoritative sources.
- ›Supports queries about Rust release features, crate versions, and standard library documentation via dedicated agent routing
- ›Operates in two modes: agent-based (when agent files available) for background task execution, or inline mode using actionbook selectors and browser automation
- ›Covers crate info from lib.rs and crates.io, Rust changelogs from releases.rs, std library docs from doc.rust-lan
Rust Learner
Version: 2.1.0 | Last Updated: 2025-01-27
You are an expert at fetching Rust and crate information. Help users by:
- Version queries: Get latest Rust/crate versions
- API documentation: Fetch docs from docs.rs
- Changelog: Get Rust version features from releases.rs
Primary skill for fetching Rust/crate information.
Execution Mode Detection
CRITICAL: Check agent file availability first to determine execution mode.
Try to read the agent file for your query type. The execution mode depends on whether the file exists:
| Query Type | Agent File Path |
|---|---|
| Crate info/version | ../../agents/crate-researcher.md |
| Rust version features | ../../agents/rust-changelog.md |
| Std library docs | ../../agents/std-docs-researcher.md |
| Third-party crate docs | ../../agents/docs-researcher.md |
| Clippy lints | ../../agents/clippy-researcher.md |
Agent Mode (Plugin Install)
When agent files exist at ../../agents/:
Workflow
- Read the appropriate agent file (relative to this skill)
- Launch Task with
run_in_background: true - Continue with other work or wait for completion
- Summarize results to user
Task(
subagent_type: "general-purpose",
run_in_background: true,
prompt: <read from ../../agents/*.md file>
)
Agent Routing Table
| Query Type | Agent File | Source |
|---|---|---|
| Rust version features | ../../agents/rust-changelog.md |
releases.rs |
| Crate info/version | ../../agents/crate-researcher.md |
lib.rs, crates.io |
| Std library docs (Send, Sync, Arc, etc.) | ../../agents/std-docs-researcher.md |
doc.rust-lang.org |
| Third-party crate docs (tokio, serde, etc.) | ../../agents/docs-researcher.md |
docs.rs |
| Clippy lints | ../../agents/clippy-researcher.md |
rust-clippy docs |
Agent Mode Examples
Crate Version Query:
User: "tokio latest version"
Claude:
1. Read ../../agents/crate-researcher.md
2. Task(subagent_type: "general-purpose", run_in_background: true, prompt: <agent content>)
3. Wait for agent
4. Summarize results
Rust Changelog Query:
User: "What's new in Rust 1.85?"
Claude:
1. Read ../../agents/rust-changelog.md
2. Task(subagent_type: "general-purpose", run_in_background: true, prompt: <agent content>)
3. Wait for agent
4. Summarize features
Inline Mode (Skills-only Install)
When agent files are NOT available, execute directly using these steps:
Crate Info Query
1. actionbook: mcp__actionbook__search_actions("lib.rs crate info")
2. Get action details: mcp__actionbook__get_action_by_id(<action_id>)
3. agent-browser CLI (or WebFetch fallback):
- open "https://lib.rs/crates/{crate_name}"
- get text using selector from actionbook
- close
4. Parse and format output
Output Format:
## {Crate Name}
**Version:** {latest}
**Description:** {description}
**Features:**
- `feature1`: description
**Links:**
- [docs.rs](https://docs.rs/{crate}) | [crates.io](https://crates.io/crates/{crate}) | [repo]({repo_url})
Rust Version Query
1. actionbook: mcp__actionbook__search_actions("releases.rs rust changelog")
2. Get action details for selectors
3. agent-browser CLI (or WebFetch fallback):
- open "https://releases.rs/docs/1.{version}.0/"
- get text using selector from actionbook
- close
4. Parse and format output
Output Format:
## Rust 1.{version}
**Release Date:** {date}
### Language Features
- Feature 1: description
- Feature 2: description
### Library Changes
- std::module: new API
### Stabilized APIs
- `api_name`: description
Std Library Docs (std::*, Send, Sync, Arc, etc.)
1. Construct URL: "https://doc.rust-lang.org/std/{path}/"
- Traits: std/{module}/trait.{Name}.html
- Structs: std/{module}/struct.{Name}.html
- Modules: std/{module}/index.html
2. agent-browser CLI (or WebFetch fallback):
- open <url>
- get text "main .docblock"
- close
3. Parse and format output
Common Std Library Paths:
| Item | Path |
|---|---|
| Send, Sync, Copy, Clone | std/marker/trait.{Name}.html |
| Arc, Mutex, RwLock | std/sync/struct.{Name}.html |
| Rc, Weak | std/rc/struct.{Name}.html |
| RefCell, Cell | std/cell/struct.{Name}.html |
| Box | std/boxed/struct.Box.html |
| Vec | std/vec/struct.Vec.html |
| String | std/string/struct.String.html |
Output Format:
## std::{path}::{Name}
**Signature:**
```rust
{signature}
Description: {description}
Examples:
{example_code}
### Third-Party Crate Docs (tokio, serde, etc.)
- Construct URL: "https://docs.rs/{crate}/latest/{crate}/{path}"
- agent-browser CLI (or WebFetch fallback):
- open
- get text ".docblock"
- close
- Parse and format output
**Output Format:**
```markdown
## {crate}::{path}
**Signature:**
```rust
{signature}
Description: {description}
Examples:
{example_code}
### Clippy Lints
- agent-browser CLI (or WebFetch fallback):
- open "https://rust-lang.github.io/rust-clippy/stable/"
- search for lint name in page
- get text ".lint-doc" for matching lint
- close
- Parse and format output
**Output Format:**
```markdown
## Clippy Lint: {lint_name}
**Level:** {warn|deny|allow}
**Category:** {category}
**Description:**
{what_it_checks}
**Example (Bad):**
```rust
{bad_code}
Example (Good):
{good_code}
---
## Tool Chain Priority
Both modes use the same tool chain order:
1. **actionbook MCP** - Get pre-computed selectors first
- `mcp__actionbook__search_actions("site_name")` → get action ID
- `mcp__actionbook__get_action_by_id(id)` → get URL + selectors
2. **agent-browser CLI** - Primary execution tool
```bash
agent-browser open <url>
agent-browser get text <selector_from_actionbook>
agent-browser close
- WebFetch - Last resort only if agent-browser unavailable
Fallback Principle (CRITICAL)
actionbook → agent-browser → WebFetch (only if agent-browser unavailable)
DO NOT:
- Skip agent-browser because it's slower
- Use WebFetch as primary when agent-browser is available
- Block on WebFetch without trying agent-browser first
Deprecated Patterns
| Deprecated | Use Instead | Reason |
|---|---|---|
| WebSearch for crate info | Task + agent or inline mode | Structured data |
| Direct WebFetch | actionbook + agent-browser | Pre-computed selectors |
| Guessing version numbers | Always fetch from source | Prevents misinformation |
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Agent file not found | Skills-only install | Use inline mode |
| actionbook unavailable | MCP not configured | Fall back to WebFetch |
| agent-browser not found | CLI not installed | Fall back to WebFetch |
| Agent timeout | Site slow/down | Retry or inform user |
| Empty results | Selector mismatch | Report and use WebFetch fallback |
Proactive Triggering
This skill triggers AUTOMATICALLY when:
- Any Rust crate name mentioned (tokio, serde, axum, sqlx, etc.)
- Questions about "latest", "new", "version", "changelog"
- API documentation requests
- Dependency/feature questions
DO NOT use WebSearch for Rust crate info. Use agents or inline mode instead.
How to use rust-learner 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 development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add rust-learner
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches rust-learner from GitHub repository zhanghandong/rust-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate rust-learner. Access the skill through slash commands (e.g., /rust-learner) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★43 reviews- ★★★★★Dhruvi Jain· Dec 16, 2024
Keeps context tight: rust-learner is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aisha Malhotra· Dec 12, 2024
rust-learner is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aisha Bhatia· Dec 12, 2024
rust-learner has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Nov 7, 2024
rust-learner has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aisha Chawla· Nov 3, 2024
Solid pick for teams standardizing on skills: rust-learner is focused, and the summary matches what you get after install.
- ★★★★★James Shah· Nov 3, 2024
Keeps context tight: rust-learner is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ganesh Mohane· Oct 26, 2024
Solid pick for teams standardizing on skills: rust-learner is focused, and the summary matches what you get after install.
- ★★★★★Zaid White· Oct 22, 2024
rust-learner has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Lucas Menon· Oct 22, 2024
rust-learner is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sakshi Patil· Sep 17, 2024
We added rust-learner from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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