appwrite-python

appwrite/agent-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/appwrite/agent-skills --skill appwrite-python
0 commentsdiscussion
summary

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.

skill.md

Appwrite Python SDK

Installation

pip install appwrite

Setting Up the Client

from appwrite.client import Client
from appwrite.id import ID
from appwrite.query import Query
from appwrite.services.users import Users
from appwrite.services.tablesdb import TablesDB
from appwrite.services.storage import Storage
from appwrite.services.functions import Functions
from appwrite.enums.o_auth_provider import OAuthProvider

import os

client = (Client()
    .set_endpoint('https://<REGION>.cloud.appwrite.io/v1')
    .set_project(os.environ['APPWRITE_PROJECT_ID'])
    .set_key(os.environ['APPWRITE_API_KEY']))

Code Examples

User Management

users = Users(client)

# Create user
user = users.create(ID.unique(), '[email protected]', None, 'password123', 'User Name')

# List users
result = users.list([Query.limit(25)])

# Get user
fetched = users.get('[USER_ID]')

# Delete user
users.delete('[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='...') over positional arguments for all SDK method calls. Only use positional style if the existing codebase already uses it or the user explicitly requests it.

tables_db = TablesDB(client)

# Create database
db = tables_db.create(ID.unique(), 'My Database')

# Create row
doc = tables_db.create_row('[DATABASE_ID]', '[TABLE_ID]', ID.unique(), {
    'title': 'Hello World'
})

# Query rows
results = tables_db.list_rows('[DATABASE_ID]', '[TABLE_ID]', [
    Query.equal('title', 'Hello World'),
    Query.limit(10)
])

# Get row
row = tables_db.get_row('[DATABASE_ID]', '[TABLE_ID]', '[ROW_ID]')

# Update row
tables_db.update_row('[DATABASE_ID]', '[TABLE_ID]', '[ROW_ID]', {
    'title': 'Updated'
})

# Delete row
tables_db.delete_row('[DATABASE_ID]', '[TABLE_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.
# 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

# Filtering
Query.equal('field', 'value')             # == (or pass list 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 (string or array)
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_queries([Query.equal('a', 1), Query.equal('b', 2)])   # OR
Qu
how to use appwrite-python

How to use appwrite-python 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 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 appwrite-python
2

Execute installation command

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

$npx skills add https://github.com/appwrite/agent-skills --skill appwrite-python

The skills CLI fetches appwrite-python from GitHub repository appwrite/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/appwrite-python

Reload or restart Cursor to activate appwrite-python. Access the skill through slash commands (e.g., /appwrite-python) 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

GET_STARTED →

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

  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

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.672 reviews
  • Liam Kim· Dec 16, 2024

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

  • Fatima Chen· Dec 16, 2024

    We added appwrite-python from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Anika Johnson· Dec 16, 2024

    Useful defaults in appwrite-python — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Naina Gonzalez· Dec 8, 2024

    appwrite-python has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Olivia Desai· Dec 8, 2024

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

  • Liam Diallo· Nov 27, 2024

    Useful defaults in appwrite-python — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Naina Perez· Nov 7, 2024

    Registry listing for appwrite-python matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Fatima Nasser· Nov 7, 2024

    appwrite-python fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kiara Gupta· Oct 26, 2024

    appwrite-python fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Nia Jackson· Oct 26, 2024

    Registry listing for appwrite-python matched our evaluation — installs cleanly and behaves as described in the markdown.

showing 1-10 of 72

1 / 8