armor-connect
Connect a new data source to AnomalyArmor. Handles "connect my database", "add snowflake", "setup postgres", "connect warehouse".
What this skill does
# Connect Data Source
Guide users through connecting a new data source (database, warehouse) to AnomalyArmor.
## Prerequisites
- AnomalyArmor API key configured (`~/.armor/config.yaml` or `ARMOR_API_KEY` env var)
- Python SDK installed (`pip install anomalyarmor`)
- Database credentials ready
## When to Use
- "Connect my Snowflake warehouse"
- "Add my PostgreSQL database"
- "Setup Databricks connection"
- "Connect a new data source"
## Supported Data Sources
- `snowflake` - Snowflake Data Cloud
- `postgresql` - PostgreSQL
- `databricks` - Databricks
- `bigquery` - Google BigQuery
- `redshift` - Amazon Redshift
- `mysql` - MySQL
- `clickhouse` - ClickHouse
## Steps
1. Ask user for data source type if not specified
2. Collect connection configuration (credentials, host, database, etc.)
3. Create the asset with `client.assets.create()`
4. Test the connection with `client.assets.test_connection()`
5. If successful, trigger schema discovery with `client.assets.trigger_discovery()`
6. Track discovery progress with `client.jobs.status()`
## Example Usage
### Connect Snowflake
```python
from anomalyarmor import Client
import time
client = Client()
# Create the asset
asset = client.assets.create(
name="Analytics Warehouse",
source_type="snowflake",
connection_config={
"account": "abc123.us-east-1",
"warehouse": "COMPUTE_WH",
"database": "ANALYTICS",
"user": "anomalyarmor_user",
"password": "your_password", # Or use key pair auth
"role": "ANOMALYARMOR_ROLE" # Optional
},
description="Main analytics data warehouse"
)
print(f"Created asset: {asset.id}")
# Test connection
result = client.assets.test_connection(asset.id)
if result.success:
print("Connection successful!")
else:
print(f"Connection failed: {result.error_message}")
exit(1)
# Trigger schema discovery
job = client.assets.trigger_discovery(asset.id)
print(f"Discovery started: {job.job_id}")
# Wait for discovery to complete
while True:
status = client.jobs.status(job.job_id)
print(f"Discovery progress: {status.get('progress', 0)}%")
if status.get('status') in ('completed', 'failed'):
break
time.sleep(5)
if status.get('status') == 'completed':
print("Schema discovery complete!")
else:
print(f"Discovery failed: {status.get('error')}")
```
### Connect PostgreSQL
```python
from anomalyarmor import Client
client = Client()
asset = client.assets.create(
name="Production Database",
source_type="postgresql",
connection_config={
"host": "db.example.com",
"port": 5432,
"database": "production",
"user": "readonly_user",
"password": "your_password",
"sslmode": "require"
}
)
# Test and discover...
```
### Connect Databricks
```python
from anomalyarmor import Client
client = Client()
asset = client.assets.create(
name="Databricks Lakehouse",
source_type="databricks",
connection_config={
"host": "adb-1234567890.1.azuredatabricks.net",
"http_path": "/sql/1.0/warehouses/abc123",
"access_token": "dapi..."
}
)
```
## Connection Config by Source Type
### Snowflake
```python
{
"account": "abc123.us-east-1",
"warehouse": "COMPUTE_WH",
"database": "ANALYTICS",
"user": "user",
"password": "password",
"role": "ROLE_NAME" # optional
}
```
### PostgreSQL
```python
{
"host": "hostname",
"port": 5432,
"database": "dbname",
"user": "user",
"password": "password",
"sslmode": "require" # optional
}
```
### BigQuery
```python
{
"project_id": "my-project",
"credentials_json": "{...}" # Service account JSON
}
```
## Security Notes
- Never hardcode credentials in scripts
- Use environment variables or secure vaults
- Create read-only database users for AnomalyArmor
- Ensure network connectivity (whitelist IPs if needed)
## Follow-up Actions
After connecting, use:
- `/armor:monitor` to set up freshness and schema monitoring
- `/armor:analyze` to generate AI intelligence
- `/armor:status` to verify health
Related 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.