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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionappwrite-pythonExecute the skills CLI command in your project's root directory to begin installation:
Fetches appwrite-python from appwrite/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate appwrite-python. Access via /appwrite-python in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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pip install appwrite
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']))
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]')
Note: Use
TablesDB(not the deprecatedDatabasesclass) for all new code. Only useDatabasesif 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]')
Note: The legacy
stringtype 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
]
)
# 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
QuPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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Solid pick for teams standardizing on skills: appwrite-python is focused, and the summary matches what you get after install.
We added appwrite-python from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in appwrite-python — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
appwrite-python has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend appwrite-python for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in appwrite-python — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for appwrite-python matched our evaluation — installs cleanly and behaves as described in the markdown.
appwrite-python fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
appwrite-python fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for appwrite-python matched our evaluation — installs cleanly and behaves as described in the markdown.
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