ReddRadar
Agent skill
analyzing-data
Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/analyzing-data
SKILL.md
Data Analysis
Answer business questions by querying the data warehouse. The kernel starts automatically on first use.
Prerequisites
uv must be installed:
curl -LsSf https://astral.sh/uv/install.sh | sh
Scripts are located relative to this skill file.
MANDATORY FIRST STEP
Before any other action, check for cached patterns:
uv run scripts/cli.py pattern lookup "<user's question>"
This is NON-NEGOTIABLE. Patterns contain proven strategies that save time and avoid failed queries.
Workflow
Analysis Progress:
- [ ] Step 1: pattern lookup (check for cached strategy)
- [ ] Step 2: concept lookup (check for known tables)
- [ ] Step 3: Search codebase for table definitions (Grep)
- [ ] Step 4: Read SQL file to get table/column names
- [ ] Step 5: Execute query via kernel (run_sql)
- [ ] Step 6: learn_concept (ALWAYS before presenting results)
- [ ] Step 7: learn_pattern (ALWAYS if discovery required)
- [ ] Step 8: record_pattern_outcome (if you used a pattern in Step 1)
- [ ] Step 9: Present findings to user
CLI Commands
Kernel Management
uv run scripts/cli.py start # Start kernel with Snowflake
uv run scripts/cli.py exec "..." # Execute Python code
uv run scripts/cli.py status # Check kernel status
uv run scripts/cli.py restart # Restart kernel
uv run scripts/cli.py stop # Stop kernel
uv run scripts/cli.py install plotly # Install additional packages
Concept Cache (concept -> table mappings)
# Look up a concept
uv run scripts/cli.py concept lookup customers
# Learn a new concept
uv run scripts/cli.py concept learn customers HQ.MART_CUST.CURRENT_ASTRO_CUSTS -k ACCT_ID
# List all concepts
uv run scripts/cli.py concept list
# Import concepts from warehouse.md
uv run scripts/cli.py concept import -p /path/to/warehouse.md
Pattern Cache (query strategies)
# Look up patterns for a question
uv run scripts/cli.py pattern lookup "who uses operator X"
# Learn a new pattern
uv run scripts/cli.py pattern learn operator_usage \
-q "who uses X operator" \
-q "which customers use X" \
-s "1. Query TASK_RUNS for operator_class" \
-s "2. Join with ORGS on org_id" \
-t "HQ.MODEL_ASTRO.TASK_RUNS" \
-t "HQ.MODEL_ASTRO.ORGANIZATIONS" \
-g "TASK_RUNS is huge - always filter by date"
# Record pattern outcome
uv run scripts/cli.py pattern record operator_usage --success
# List all patterns
uv run scripts/cli.py pattern list
# Delete a pattern
uv run scripts/cli.py pattern delete operator_usage
Table Schema Cache
# Look up cached table schema
uv run scripts/cli.py table lookup HQ.MART_CUST.CURRENT_ASTRO_CUSTS
# Cache a table schema
uv run scripts/cli.py table cache DB.SCHEMA.TABLE -c '[{"name":"id","type":"INT"}]'
# List all cached tables
uv run scripts/cli.py table list
# Delete from cache
uv run scripts/cli.py table delete DB.SCHEMA.TABLE
Cache Management
# View cache statistics
uv run scripts/cli.py cache status
# Clear all caches
uv run scripts/cli.py cache clear
# Clear only stale entries (older than 90 days)
uv run scripts/cli.py cache clear --stale-only
Quick Start Example
# 1. Check for existing patterns
uv run scripts/cli.py pattern lookup "how many customers"
# 2. Check for known concepts
uv run scripts/cli.py concept lookup customers
# 3. Execute query
uv run scripts/cli.py exec "df = run_sql('SELECT COUNT(*) FROM HQ.MART_CUST.CURRENT_ASTRO_CUSTS')"
uv run scripts/cli.py exec "print(df)"
# 4. Cache what we learned
uv run scripts/cli.py concept learn customers HQ.MART_CUST.CURRENT_ASTRO_CUSTS -k ACCT_ID
Available Functions in Kernel
Once kernel starts, these are available:
| Function | Description |
|---|---|
run_sql(query, limit=100) |
Execute SQL, return Polars DataFrame |
run_sql_pandas(query, limit=100) |
Execute SQL, return Pandas DataFrame |
pl |
Polars library (imported) |
pd |
Pandas library (imported) |
Table Discovery via Codebase
If concept/pattern cache miss, search the codebase:
Grep pattern="<concept>" glob="**/*.sql"
| Repo Type | Where to Look |
|---|---|
| Gusty | dags/declarative/04_metric/, 06_reporting/, 05_mart/ |
| dbt | models/marts/, models/staging/ |
Known Tables Quick Reference
| Concept | Table | Key Column | Date Column |
|---|---|---|---|
| customers | HQ.MART_CUST.CURRENT_ASTRO_CUSTS | ACCT_ID | - |
| organizations | HQ.MODEL_ASTRO.ORGANIZATIONS | ORG_ID | CREATED_TS |
| deployments | HQ.MODEL_ASTRO.DEPLOYMENTS | DEPLOYMENT_ID | CREATED_TS |
| task_runs | HQ.MODEL_ASTRO.TASK_RUNS | - | START_TS |
| dag_runs | HQ.MODEL_ASTRO.DAG_RUNS | - | START_TS |
| users | HQ.MODEL_ASTRO.USERS | USER_ID | - |
| accounts | HQ.MODEL_CRM.SF_ACCOUNTS | ACCT_ID | - |
Large tables (always filter by date): TASK_RUNS (6B rows), DAG_RUNS (500M rows)
Query Tips
- Use LIMIT during exploration
- Filter early with WHERE clauses
- Prefer pre-aggregated tables (
METRICS_*,MART_*,AGG_*) - For 100M+ row tables: no JOINs or GROUP BY on first query
Reference
- reference/discovery-warehouse.md - Large table handling, warehouse discovery
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