rust-learner
Learn Rust language features and crate updates. Use when user asks about Rust version changelog, what's new in Rust, crate updates, Cargo.toml dependencies, tokio/serde/axum features, or any Rust ecosystem questions.
Works with
---
name: rust-learner
description: Learn Rust language features and crate updates. Use when user asks about Rust version changelog, what's new in Rust, crate updates, Cargo.toml dependencies, tokio/serde/axum features, or any Rust ecosystem questions.
license: Apache-2.0
---
# Rust Learner
Learn Rust by fetching real-time information about Rust language features and crate updates.
## ⚠️ Tool Priority & Waiting Rule
**Priority:**
1. ✅ `browser-fetcher` agent (preferred)
2. ⚠️ `Fetch` / `WebFetch` (only after all browser-fetcher agents fail)
3. ⚠️ `WebSearch` (only when search engine results are needed)
**⛔ No "Racing Ahead":**
- After launching browser-fetcher agents, **MUST wait for ALL of them to complete**
- **DO NOT** use WebSearch/Fetch as "supplements" while waiting
- Only use fallback tools after **ALL** browser-fetcher agents have failed
## Workflow
### Step 1: actionbook MCP
```
search_actions("lib.rs crate") → get action ID
get_action_by_id(id) → get URL and selectors
```
### Step 2: Launch browser-fetcher agents
```
Launch multiple browser-fetcher agents in parallel
```
### Step 3: Wait for ALL agents to complete
```
⛔ DO NOT use other tools during this time
✅ Wait for TaskOutput to return all results
```
### Step 4: Summarize results
- If agents succeed: summarize content for user
- If ALL agents fail: use Fetch as fallback
## Example
```
User: Query tokio latest version
✅ CORRECT:
1. Launch browser-fetcher: lib.rs/crates/tokio
2. Launch browser-fetcher: crates.io/crates/tokio
3. Wait for BOTH agents to complete
4. Summarize results
❌ WRONG:
1. Launch browser-fetcher agents
2. While waiting, use WebSearch("tokio latest") ← Racing ahead!
3. Mix multiple result sources
```More Writing & Documentation skills
paper-context-resolver
lllllllama/rigorpilot-skills
Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.
repo-intake-and-plan
lllllllama/rigorpilot-skills
Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
minimal-run-and-audit
lllllllama/rigorpilot-skills
Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

