sandbox:language-environment-config
This skill should be used when detecting programming languages in projects, determining language versions from config files, installing development environments (Rust, Python, Node.js), setting up LSP servers, installing CLI development tools, or configuring shell environments (oh-my-zsh, starship). Provides knowledge for language detection and environment setup in sandboxes.
What this skill does
# language-environment-config
Detect programming languages, parse version requirements, and install complete development environments for Rust, Python, and Node.js in Docker-based sandboxes.
## Purpose
This skill provides knowledge for setting up language-specific development environments inside sandbox containers. Focus on the three primary languages (Rust, Python, Node.js) and their associated tooling, LSP servers, and development utilities.
## Language Detection
### Detection Strategy
Identify languages by scanning for configuration and dependency files:
**Rust indicators:**
- `Cargo.toml` - Rust project manifest
- `Cargo.lock` - Dependency lock file
- `rust-toolchain.toml` - Rust version specification
- `.rs` files in `src/` directory
**Python indicators:**
- `pyproject.toml` - Modern Python project config
- `requirements.txt` - Dependency list
- `setup.py` - Package setup script
- `Pipfile` - Pipenv config
- `.python-version` - Python version file
- `.py` files
**Node.js indicators:**
- `package.json` - npm project manifest
- `package-lock.json` - npm lock file
- `pnpm-lock.yaml` - pnpm lock file
- `.nvmrc` - Node version specification
- `.node-version` - Alternative version file
- `.js`, `.ts`, `.jsx`, `.tsx` files
### Detection Script
Use the `scripts/detect_languages.py` script to analyze a project:
```python
# Returns detected languages and their config files
python3 scripts/detect_languages.py /path/to/project
```
Output format:
```json
{
"rust": {
"detected": true,
"indicators": ["Cargo.toml", "src/main.rs"]
},
"python": {
"detected": true,
"indicators": ["pyproject.toml", "requirements.txt"]
},
"nodejs": {
"detected": true,
"indicators": ["package.json", ".nvmrc"]
}
}
```
## Version Detection
### Version Files and Parsing
Each language has standard files for version specification:
#### Rust Version Detection
**Primary: `rust-toolchain.toml`**
```toml
[toolchain]
channel = "1.93.0"
# or
channel = "stable"
# or
channel = "nightly"
```
Parse with:
```python
import toml
config = toml.load("rust-toolchain.toml")
version = config["toolchain"]["channel"]
```
**Legacy: `rust-toolchain` (plain text)**
```
1.93.0
```
#### Python Version Detection
**Primary: `pyproject.toml`**
```toml
[project]
requires-python = ">=3.14.2"
```
Parse with:
```python
import toml
config = toml.load("pyproject.toml")
version_spec = config["project"]["requires-python"]
# Extract version: ">=3.14.2" → "3.14.2"
```
**Alternative: `.python-version`**
```
3.14.2
```
**requirements.txt (less precise)**
```
python>=3.14
```
#### Node.js Version Detection
**Primary: `.nvmrc`**
```
18.20.0
```
Or semantic versions:
```
lts/*
node
v18
```
**Alternative: `.node-version`**
```
18.20.0
```
**package.json engines field**
```json
{
"engines": {
"node": ">=18.0.0"
}
}
```
### Version Parsing Script
Use `scripts/parse_versions.py`:
```python
# Extract version requirements from project files
python3 scripts/parse_versions.py /path/to/project
```
Output:
```json
{
"rust": "1.93.0",
"python": "3.14.2",
"nodejs": "18.20.0"
}
```
## Language Installation
### Rust Installation
**Install via rustup** (preferred for version flexibility):
```dockerfile
# Install specific version
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | \
sh -s -- -y --default-toolchain 1.93.0
# Install stable (current)
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | \
sh -s -- -y --default-toolchain stable
# Install nightly
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | \
sh -s -- -y --default-toolchain nightly
```
**Critical: Version keywords vs numbers**
✅ **GOOD - Always current:**
```dockerfile
RUN rustup install stable
RUN rustup install nightly
RUN rustup default stable
```
❌ **BAD - Becomes outdated:**
```dockerfile
RUN rustup install 1.93.0 # Fixed version, will be old
```
**When to use specific versions:**
- Project requires exact version (from rust-toolchain.toml)
- User explicitly requests specific version
**Components to install:**
```dockerfile
RUN rustup component add clippy rustfmt rust-analyzer
```
**Standard tools:**
```dockerfile
# After Rust is installed
RUN cargo install cargo-dist cargo-deny cargo-release cocogitto
```
### Python Installation
**Install via apt** (system Python):
```dockerfile
RUN apt-get update && apt-get install -y \
python3 \
python3-pip \
python3-venv \
&& rm -rf /var/lib/apt/lists/*
```
**Install via uv** (for specific versions):
```dockerfile
# Install uv first
RUN pip3 install --no-cache-dir uv
# Install specific Python version
RUN uv python install 3.14.2
# Install current stable
RUN uv python install
# Python doesn't have official LTS - use current stable
# When user asks for LTS, install current stable instead
```
**Critical: Version keywords**
✅ **GOOD - Always current:**
```dockerfile
RUN uv python install # Latest stable
RUN pip3 install --upgrade pip # Latest pip
```
❌ **BAD - Becomes outdated:**
```dockerfile
RUN uv python install 3.14 # Major version, but not latest patch
RUN uv python install 3.14.2 # Specific version, will be old
```
**Standard tools:**
```dockerfile
RUN pip3 install --no-cache-dir uv ruff mypy black pytest
```
### Node.js Installation
**Install via nvm** (preferred for version flexibility):
```dockerfile
ENV NVM_DIR="/root/.nvm"
# Install nvm
RUN curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
# Install current stable
RUN . "$NVM_DIR/nvm.sh" && nvm install node && nvm alias default node
# Install LTS
RUN . "$NVM_DIR/nvm.sh" && nvm install --lts && nvm alias default lts/*
# Install nightly (latest, including pre-release)
RUN . "$NVM_DIR/nvm.sh" && nvm install node --latest-npm
# Install specific version (when required)
RUN . "$NVM_DIR/nvm.sh" && nvm install 18.20.0
```
**Critical: Version keywords**
✅ **GOOD - Always current:**
```dockerfile
RUN nvm install node # Current stable
RUN nvm install --lts # Current LTS
RUN nvm install node --latest-npm # Nightly/latest
```
❌ **BAD - Becomes outdated:**
```dockerfile
RUN nvm install 18.20.0 # Specific version, will be old
RUN nvm install 18 # Major version, not latest
```
**Standard tools:**
```dockerfile
RUN npm install -g pnpm typescript ts-node
```
## LSP Server Installation
### rust-analyzer
**Install via rustup:**
```dockerfile
RUN rustup component add rust-analyzer
```
**Or via cargo (latest):**
```dockerfile
RUN cargo install rust-analyzer
```
### pyright (Python)
**Install via npm:**
```dockerfile
RUN npm install -g pyright
```
**Or via pip:**
```dockerfile
RUN pip3 install pyright
```
### typescript-language-server
**Install via npm:**
```dockerfile
RUN npm install -g typescript-language-server typescript
```
### Claude Code LSP Plugins
When user requests LSP plugins from marketplace:
```markdown
Add these plugins in Sandbox.toml:
[claude.plugins]
plugins = [
"rust-analyzer-lsp@claude-plugins-official",
"pyright-lsp@claude-plugins-official",
"typescript-lsp@claude-plugins-official"
]
```
Note: LSP servers must be installed in container. Plugins just configure Claude Code to use them.
## CLI Tools Installation
### Tools List
**Standard tools to install:**
- ripgrep, fd, eza, jq, just
- repomix, scc, sg (ast-grep)
- bfs, xan, rga, pdfgrep, fq
- shellcheck, zizmor, git-cliff, has
### Installation Methods
**Via apt (if available):**
```dockerfile
RUN apt-get update && apt-get install -y \
ripgrep \
fd-find \
jq \
pdfgrep \
shellcheck \
&& rm -rf /var/lib/apt/lists/*
# Create symlinks where needed
RUN ln -s $(which fdfind) /usr/local/bin/fd
```
**Via cargo:**
```dockerfile
RUN cargo install \
eza \
just \
repomix \
scc \
bfs \
xan \
git-cliff \
ast-grep \
ripgrep_all \
zizmor \
has
```
**Via download (for binaries like fq):**
```dockerfile
RUN wget https://github.com/wadeRelated in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.