Query your data warehouse to answer business questions with cached patterns and concept mappings.
Works with
Supports pattern lookup and caching for repeated question types, with outcome recording to improve future queries
Includes concept-to-table mapping cache and table schema discovery via INFORMATION_SCHEMA or codebase grep
Provides run_sql() and run_sql_pandas() kernel functions returning Polars or Pandas DataFrames for analysis
CLI commands for managing concept, pattern, and table cach
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionanalyzing-dataExecute the skills CLI command in your project's root directory to begin installation:
Fetches analyzing-data from astronomer/agents 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 analyzing-data. Access via /analyzing-data 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.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Answer business questions by querying the data warehouse. The kernel auto-starts on first exec call.
All CLI commands below are relative to this skill's directory. Before running any scripts/cli.py command, cd to the directory containing this file.
Pattern lookup — Check for a cached query strategy:
uv run scripts/cli.py pattern lookup "<user's question>"
If a pattern exists, follow its strategy. Record the outcome after executing:
uv run scripts/cli.py pattern record <name> --success # or --failure
Concept lookup — Find known table mappings:
uv run scripts/cli.py concept lookup <concept>
Table discovery — If cache misses, search the codebase (Grep pattern="<concept>" glob="**/*.sql") or query INFORMATION_SCHEMA. See reference/discovery-warehouse.md.
Execute query:
uv run scripts/cli.py exec "df = run_sql('SELECT ...')"
uv run scripts/cli.py exec "print(df)"
Cache learnings — Always cache before presenting results:
# Cache concept → table mapping
uv run scripts/cli.py concept learn <concept> <TABLE> -k <KEY_COL>
# Cache query strategy (if discovery was needed)
uv run scripts/cli.py pattern learn <name> -q "question" -s "step" -t "TABLE" -g "gotcha"
Present findings to user.
| Function | Returns |
|---|---|
run_sql(query, limit=100) |
Polars DataFrame |
run_sql_pandas(query, limit=100) |
Pandas DataFrame |
pl (Polars) and pd (Pandas) are pre-imported.
uv run scripts/cli.py warehouse list # List warehouses
uv run scripts/cli.py start [-w name] # Start kernel (with optional warehouse)
uv run scripts/cli.py exec "..." # Execute Python code
uv run scripts/cli.py status # Kernel status
uv run scripts/cli.py restart # Restart kernel
uv run scripts/cli.py stop # Stop kernel
uv run scripts/cli.py install <pkg> # Install package
uv run scripts/cli.py concept lookup <name> # Look up
uv run scripts/cli.py concept learn <name> <TABLE> -k <KEY_COL> # Learn
uv run scripts/cli.py concept list # List all
uv run scripts/cli.py concept import -p /path/to/warehouse.md # Bulk import
uv run scripts/cli.py pattern lookup "question" # Look up
uv run scripts/cli.py pattern learn <name> -q "..." -s "..." -t "TABLE" -g "gotcha" # Learn
uv run scripts/cli.py pattern record <name> --success # Record outcome
uv run scripts/cli.py pattern list # List all
uv run scripts/cli.py pattern delete <name> # Delete
uv run scripts/cli.py table lookup <TABLE> # Look up schema
uv run scripts/cli.py table cache <TABLE> -c '[...]' # Cache schema
uv run scripts/cli.py table list # List cached
uv run scripts/cli.py table delete <TABLE> # Delete
uv run scripts/cli.py cache status # Stats
uv run scripts/cli.py cache clear [--stale-only] # Clear
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Solid pick for teams standardizing on skills: analyzing-data is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: analyzing-data is focused, and the summary matches what you get after install.
I recommend analyzing-data for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for analyzing-data matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: analyzing-data is focused, and the summary matches what you get after install.
analyzing-data has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added analyzing-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
analyzing-data fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added analyzing-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added analyzing-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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