---
name: ccc-data
description: "Data router: ETL pipelines, SQL optimization, dashboards, ML, data quality, analytics, reports, vector search. Use to build, query or visualize data."
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash
  - Glob
  - Grep
  - Skill
  - Artifact
  - AskUserQuestion
argument-hint: "[pipeline | sql | viz | ml | quality | analytics | reporting | vector]"
model: inherit
effort: medium
---

# /ccc-data — data work, routed

**CC Commander** · /ccc-data · the right data method for the job, applied to your stack.

Data sub-skills (`data-pipeline`, `sql-optimization`, `data-visualization`, `machine-learning`, `data-quality`, `analytics-setup`, `reporting`, `vector-search`) ship with the Commander CLI toolkit. Invoke one with the Skill tool when it is listed in this session; otherwise apply the focus in [references/routing.md](references/routing.md). The user's selected model is preserved.

Codex: ask pickers as plain-text questions; write dashboards to a local HTML file instead of publishing.

## When to use / when not to

- Use for pipelines, warehouse and query work, dashboards, ML models, data validation, analytics instrumentation, recurring reports and embeddings.
- Use `/ccc-research` (`data-ingestion`) to summarize a large document, `/ccc-saas` (`database-designer`) to design an application schema, `/ccc-smb-ops` for owner-level cash and expense numbers.

## What happens

### 1. Detect the stack

Run in one Bash call, silent on failure:

```bash
ls dbt_project.yml dags airflow.cfg dagster.yaml workspace.yaml requirements.txt pyproject.toml 2>/dev/null
test -f package.json && grep -oE '"(posthog-js|mixpanel-browser|recharts|chart\.js|d3|@tremor/react|@pinecone-database/pinecone|@qdrant/js-client-rest|pg|drizzle-orm|prisma)"' package.json | sort -u
grep -lE 'pgvector|scikit-learn|torch|tensorflow|pandas|polars' requirements.txt pyproject.toml 2>/dev/null
```

Render one line:

> 📊 Stack: `<tools detected | none detected>`

### 2. Route by intent, or ask

An argument or a clear intent routes straight to the sub-skill in [references/routing.md](references/routing.md). A request that spans several (an analytics stack, an ML feature) follows the matching campaign in that file, one sub-skill per step.

Otherwise ask with AskUserQuestion (header "Data"), recommended first:

- 🟢 Build or fix a pipeline — `data-pipeline` (Airflow, dbt, Dagster, incremental loads)
- Speed up queries — `sql-optimization` (plans, indexes, rewrites)
- Chart or dashboard — `data-visualization`
- More — ML, data quality, analytics, reports or vector search (a follow-up picker with the four best matches)

### 3. Look at the data before changing it

- Large files: read a sample, never the whole file — `head -n 50 <file>` for CSV/JSON lines, `python3 -c "import pyarrow.parquet as p; print(p.read_schema('<file>'))"` for Parquet. For document-scale inputs use `data-ingestion` from `/ccc-research`.
- Queries: capture the current plan first (`EXPLAIN (ANALYZE, BUFFERS)` on PostgreSQL, `EXPLAIN ANALYZE` on MySQL) against a non-production database, and keep it for the before/after comparison.
- On the Claude API, the Files API can ingest CSV, JSON and Parquet without spending context; mention it when the user is building an API integration.

### 4. Do the work

Follow the routed sub-skill. Then:

- Code or SQL changed: run the project's test or check command (`dbt build --select <model>`, `pytest`, `npm test`), re-run the captured plan for query work, and `git status --short`.
- Dashboard wanted as a shareable page: write a self-contained HTML file, then offer with AskUserQuestion: 🟢 Keep it local · Publish as a private claude.ai page. Publish only on an explicit yes, with the Artifact tool (`icon: "chart"`). Without the Artifact tool, give the file path.
- Recurring report: build and run it once by hand, then offer `/schedule` (Routines) or `/loop` for the cadence.

## Done when

- Routing only: the sub-skill was invoked or its guidance applied, and the user knows which one.
- Code or SQL changed: the check command exited 0 in this turn and its output is reported; query work reports the before/after plan cost or timing.
- Dashboard or report: the file exists (`test -s <file> && echo ok` printed `ok`), and a published page has a URL from the Artifact tool.

## Guardrails

- Never run `DROP`, `TRUNCATE`, `DELETE` without `WHERE`, a backfill, or a migration against a production database; propose it and let the user run it.
- Never create indexes on a production table without the user's OK at that moment (they can lock writes).
- Never write warehouse credentials, API keys or connection strings into the repo or a report; use the project's secret store or environment variables.
- Never publish a dashboard, send a report, or create a schedule without explicit authorization in this conversation. Never publish data containing personal information.

## See also

- `/ccc-research` — large documents and comparative research
- `/ccc-saas` — application schema, analytics in a SaaS product
- `/ccc-testing` — data-pipeline and query regression tests
- `/ccc-loop` — recurring jobs and gated loops

---

> ⚙️ **Fable contract:** plan before build · verifier ≠ worker · prove before alarm · loops need gates · leave durable state — `rules/fable-method.md`