ruby
Appwrite Ruby SDK skill. Use when building server-side Ruby applications with Appwrite, including Rails and Sinatra integrations. Covers user management, database/table CRUD, file storage, and functions via API keys.
What this skill does
# Appwrite Ruby SDK
## Installation
```bash
gem install appwrite
```
## Setting Up the Client
```ruby
require 'appwrite'
include Appwrite
client = Client.new
.set_endpoint('https://<REGION>.cloud.appwrite.io/v1')
.set_project(ENV['APPWRITE_PROJECT_ID'])
.set_key(ENV['APPWRITE_API_KEY'])
```
## Code Examples
### User Management
```ruby
users = Users.new(client)
# Create user
user = users.create(user_id: ID.unique, email: '[email protected]', password: 'password123', name: 'User Name')
# List users
list = users.list(queries: [Query.limit(25)])
# Get user
fetched = users.get(user_id: '[USER_ID]')
# Delete user
users.delete(user_id: '[USER_ID]')
```
### Database Operations
> **Note:** Use `TablesDB` (not the deprecated `Databases` class) for all new code. Only use `Databases` if the existing codebase already relies on it or the user explicitly requests it.
>
> **Tip:** Prefer keyword arguments (e.g., `database_id: '...'`) for all SDK method calls. Only use positional arguments if the existing codebase already uses them or the user explicitly requests it.
```ruby
tables_db = TablesDB.new(client)
# Create database
db = tables_db.create(database_id: ID.unique, name: 'My Database')
# Create row
doc = tables_db.create_row(
database_id: '[DATABASE_ID]',
table_id: '[TABLE_ID]',
row_id: ID.unique,
data: { title: 'Hello World' }
)
# Query rows
results = tables_db.list_rows(
database_id: '[DATABASE_ID]',
table_id: '[TABLE_ID]',
queries: [Query.equal('title', 'Hello World'), Query.limit(10)]
)
# Get row
row = tables_db.get_row(database_id: '[DATABASE_ID]', table_id: '[TABLE_ID]', row_id: '[ROW_ID]')
# Update row
tables_db.update_row(
database_id: '[DATABASE_ID]',
table_id: '[TABLE_ID]',
row_id: '[ROW_ID]',
data: { title: 'Updated' }
)
# Delete row
tables_db.delete_row(database_id: '[DATABASE_ID]', table_id: '[TABLE_ID]', row_id: '[ROW_ID]')
```
#### String Column Types
> **Note:** The legacy `string` type is deprecated. Use explicit column types for all new columns.
| Type | Max characters | Indexing | Storage |
|------|---------------|----------|---------|
| `varchar` | 16,383 | Full index (if size ≤ 768) | Inline in row |
| `text` | 16,383 | Prefix only | Off-page |
| `mediumtext` | 4,194,303 | Prefix only | Off-page |
| `longtext` | 1,073,741,823 | Prefix only | Off-page |
- `varchar` is stored inline and counts towards the 64 KB row size limit. Prefer for short, indexed fields like names, slugs, or identifiers.
- `text`, `mediumtext`, and `longtext` are stored off-page (only a 20-byte pointer lives in the row), so they don't consume the row size budget. `size` is not required for these types.
```ruby
# Create table with explicit string column types
tables_db.create_table(
database_id: '[DATABASE_ID]',
table_id: ID.unique,
name: 'articles',
columns: [
{ key: 'title', type: 'varchar', size: 255, required: true }, # inline, fully indexable
{ key: 'summary', type: 'text', required: false }, # off-page, prefix index only
{ key: 'body', type: 'mediumtext', required: false }, # up to ~4 M chars
{ key: 'raw_data', type: 'longtext', required: false }, # up to ~1 B chars
]
)
```
### Query Methods
```ruby
# Filtering
Query.equal('field', 'value') # == (or pass array for IN)
Query.not_equal('field', 'value') # !=
Query.less_than('field', 100) # <
Query.less_than_equal('field', 100) # <=
Query.greater_than('field', 100) # >
Query.greater_than_equal('field', 100) # >=
Query.between('field', 1, 100) # 1 <= field <= 100
Query.is_null('field') # is null
Query.is_not_null('field') # is not null
Query.starts_with('field', 'prefix') # starts with
Query.ends_with('field', 'suffix') # ends with
Query.contains('field', 'sub') # contains
Query.search('field', 'keywords') # full-text search (requires index)
# Sorting
Query.order_asc('field')
Query.order_desc('field')
# Pagination
Query.limit(25) # max rows (default 25, max 100)
Query.offset(0) # skip N rows
Query.cursor_after('[ROW_ID]') # cursor pagination (preferred)
Query.cursor_before('[ROW_ID]')
# Selection & Logic
Query.select(['field1', 'field2']) # return only specified fields
Query.or([Query.equal('a', 1), Query.equal('b', 2)]) # OR
Query.and([Query.greater_than('age', 18), Query.less_than('age', 65)]) # AND (default)
```
### File Storage
```ruby
storage = Storage.new(client)
# Upload file
file = storage.create_file(bucket_id: '[BUCKET_ID]', file_id: ID.unique, file: InputFile.from_path('/path/to/file.png'))
# List files
files = storage.list_files(bucket_id: '[BUCKET_ID]')
# Delete file
storage.delete_file(bucket_id: '[BUCKET_ID]', file_id: '[FILE_ID]')
```
#### InputFile Factory Methods
```ruby
InputFile.from_path('/path/to/file.png') # from filesystem path
InputFile.from_string('Hello world', 'hello.txt') # from string content
```
### Teams
```ruby
teams = Teams.new(client)
# Create team
team = teams.create(team_id: ID.unique, name: 'Engineering')
# List teams
list = teams.list
# Create membership (invite user by email)
membership = teams.create_membership(
team_id: '[TEAM_ID]',
roles: ['editor'],
email: '[email protected]'
)
# List memberships
members = teams.list_memberships(team_id: '[TEAM_ID]')
# Update membership roles
teams.update_membership(team_id: '[TEAM_ID]', membership_id: '[MEMBERSHIP_ID]', roles: ['admin'])
# Delete team
teams.delete(team_id: '[TEAM_ID]')
```
> **Role-based access:** Use `Role.team('[TEAM_ID]')` for all team members or `Role.team('[TEAM_ID]', 'editor')` for a specific team role when setting permissions.
### Serverless Functions
```ruby
functions = Functions.new(client)
# Execute function
execution = functions.create_execution(function_id: '[FUNCTION_ID]', body: '{"key": "value"}')
# List executions
executions = functions.list_executions(function_id: '[FUNCTION_ID]')
```
#### Writing a Function Handler (Ruby runtime)
```ruby
# src/main.rb — Appwrite Function entry point
def main(context)
# context.req.body — raw body (String)
# context.req.body_json — parsed JSON (Hash or nil)
# context.req.headers — headers (Hash)
# context.req.method — HTTP method
# context.req.path — URL path
# context.req.query — query params (Hash)
context.log("Processing: #{context.req.method} #{context.req.path}")
if context.req.method == 'GET'
return context.res.json({ message: 'Hello from Appwrite Function!' })
end
context.res.json({ success: true }) # JSON
# context.res.text('Hello') # plain text
# context.res.empty # 204
# context.res.redirect('https://...') # 302
end
```
### Server-Side Rendering (SSR) Authentication
SSR apps using Ruby frameworks (Rails, Sinatra, etc.) use the **server SDK** to handle auth. You need two clients:
- **Admin client** — uses an API key, creates sessions, bypasses rate limits (reusable singleton)
- **Session client** — uses a session cookie, acts on behalf of a user (create per-request, never share)
```ruby
require 'appwrite'
include Appwrite
# Admin client (reusable)
admin_client = Client.new
.set_endpoint('https://<REGION>.cloud.appwrite.io/v1')
.set_project('[PROJECT_ID]')
.set_key(ENV['APPWRITE_API_KEY'])
# Session client (create per-request)
session_client = Client.new
.set_endpoint('https://<REGION>.cloud.appwrite.io/v1')
.set_project('[PROJECT_ID]')
session = cookies['a_session_[PROJECT_ID]']
session_client.set_session(session) if session
```
#### Email/Password Login (Sinatra)
```ruby
post '/login' do
account = Account.new(admin_client)
session = accRelated 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.