Visual guide: data-skill.vercel.app
Family hub: digital-product-skill.vercel.app — the 15-skill Digital Product family and its composer.
Make product and business data trustworthy, governed, and usable — the event contract and
tracking plan, data contracts and schema evolution, ingestion and movement, transformation and
modeling, the metrics/semantic layer and BI, data quality and testing, lineage and observability,
governance/PII/retention, experiment measurement validity, and platform/cost choices, across any
warehouse or stack. data is the analytical and governed data plane of the
Digital Product Skills family, a peer of frontend, backend, and
ai in the Build stage: an agent skill for treating data as a product — distinct from backend
(data as application state) and ai (data as model context).
It sits between architecture (which decides data-flow boundaries, NFR budgets, and the threat
model) and quality/operate (which independently verify and run the monitoring platform). It owns
the analytical plane — contracts, transformation, metrics, trust gates, governance — not the
serving substrate or the model cognition. It is a warehouse-, tool-, and stack-agnostic router
grounded in the current platform landscape, not a single-vendor dbt/warehouse tutorial.
A faceted router: pick one primary job × at most one base data-plane surface (the regulated overlay stacks additively on top).
Primary jobs — event taxonomy & tracking · data contracts & schema evolution · ingestion & movement · transformation & modeling · metrics, semantics & BI · quality & testing ⭐ · lineage & observability · governance, PII & retention · experiment measurement foundations · platform & cost.
Surface overlays — product analytics (default) · warehouse platform · operational activation ·
AI data plane · regulated (additive). The base surface reshapes how every job applies for that
data-plane shape; surface-regulated stacks on top of whichever base surface applies rather than
replacing it.
The differentiator: the convergent data-quality taxonomy is the flagship — not an afterthought: 9 core DQ buckets plus PR-time blast-radius diff plus PII, known-vs-unknown-unknowns layering (rule tests catch the former, statistical/anomaly baselines the latter), and a PK-test floor that's non-negotiable. The through-line is trust is a gate, not a dashboard — untested data is broken data, and every artifact names a concrete pick, what NOT to build, and the trade-off, capped at 3–5 decisions per pass. And a distinctive value-add: small-data-first cost discipline — the COST rule (a distributed system must beat one laptop thread before its complexity is justified), narrow-the-DAG staging, one-authoritative-definition metrics, and platform-layer governance (app-layer PII redaction is bypassed by any query).
Running through all of it is the flagship invariant — retrieval-first / anti-staleness: warehouse
prices, tool versions, spec revisions, and licenses churn quarterly, so every volatile fact is
date-stamped and re-verified against the live source — reading the LICENSE file, never a repo's API
license field, because this stack is a minefield of stale-root-LICENSE traps and open-core splits.
The handoff.yaml even carries a volatile_facts block so a stale value can't leak downstream. Full
attribution in SOURCES.md.
npx skills add gabros20/data-skill -g -yOr use it in Codex with $data.
$data design a tracking plan and event contract for our new checkout flow
$data add data quality tests and anomaly baselines to our dbt project
$data our experiment results feel off — check for SRM before we trust them
$data help me pick a warehouse and orchestrator for a startup on a budget
$data build the governed RAG-corpus pipeline for our AI product's retrieval index
| User intent | Reads | Contribution |
|---|---|---|
| Design/repair event tracking — naming grammar, tracking plan, instrumentation QA, consent-aware collection | event-taxonomy-and-tracking | One enforced naming grammar, entity-first modeling, validate-at-ingestion/quarantine, consent gates collection ("denied" is tested) |
| Write/enforce a data contract; evolve a schema safely across producers and consumers | data-contracts-and-schema-evolution | ODCS contract shape, the 7 compatibility modes + who-upgrades-first, contract-as-executable-test, write-audit-publish under the brand |
| Move data — ELT/ETL, sync strategy, CDC, streaming decision, reverse-ETL, failed-sync diagnosis | ingestion-and-movement | Sync-type decision table (by table shape), at-least-once/resumability semantics, CDC + outbox, symptom→cause→fix with destructive-action gating |
| Transform and model — staging→marts layering, materialization, SQL style, modeling choice | transformation-and-modeling | Narrow-the-DAG/widen-the-tables, lint-rule thresholds, materialization ladder, Kimball/OBT/Vault trade-offs |
| Define metrics once — semantic layer, BI serving, dashboards-as-code vs GUI, NL-to-data | metrics-semantics-and-bi | Metric-type taxonomy, one-authoritative-definition anti-drift, code-first vs GUI-first BI line, 4-tier NL fallback |
| Prove the data is right — checks, anomaly detection, PR-time diff, test placement ⭐ | quality-and-testing | Convergent DQ taxonomy (9 core buckets + PR-diff + PII), known- vs unknown-unknowns layering, PK test floor, blast-radius diff before merge |
| Trace lineage and observe pipelines — impact analysis, incident response for data | lineage-and-observability | OpenLineage model + facet gotchas, five pillars/data downtime, blast-radius workflow, catalog choice |
| Govern the data — PII classification/masking, retention/erasure, catalogs, mesh-vs-central | governance-pii-and-retention | Platform-layer masking (app-layer is bypassed), Art.17-with-exceptions erasure, tombstone + scheduled hard-delete |
| Make experiment measurement trustworthy — assignment, SRM, variance reduction, peeking | experiment-measurement-foundations | SRM-before-trust, deterministic salted assignment + namespaces, CUPED + non-applicability, peek-safe sequential testing |
| Pick the platform — warehouse/lakehouse/engine choice, table formats, orchestrator, cost, licenses | platform-and-cost | Small-data-first (COST rule), workload-shape-before-pricing, Iceberg-REST interop, task-vs-asset-vs-flow orchestrators |
Surface overlay (add at most one base; the regulated overlay stacks additively):
| Data plane | Reads | Reshapes |
|---|---|---|
| Product analytics — a product emitting events, funnels, product metrics (default / start-simplest) | surface-product-analytics | Tracking plan first, identity resolution, small-data default engine, self-serve vs SQL split |
| Warehouse platform — a central analytical platform serving an org | surface-warehouse-platform | ELT + staging→marts spine, semantic layer + BI serving, org-scale trust gates, mesh-vs-central |
| Operational activation — warehouse data flowing back into operations | surface-operational-activation | CDC/outbox + reverse-ETL, freshness SLAs, idempotency under at-least-once |
| AI data plane — the data substrate of an AI product | surface-ai-data-plane | RAG corpus as governed ETL, LLM-trace/token warehousing, point-in-time correctness, eval datasets as governed data |
| Regulated ⭐ (additive) — GDPR/CCPA/HIPAA/SOC2 applies, stacks on rather than replacing the base | surface-regulated | Verified consent gating, mandatory platform-layer masking, Art.17-with-exceptions erasure ops, retention schedules, audit trails |
Plus handoff — the handoff.yaml companion (with
volatile_facts) to backend, ai, quality, operate, growth, and frontend when downstream
work is expected.
A working data asset plus the decision record behind it: a tracking plan or data contract, an
ingestion/transformation pipeline, a semantic-layer metric definition, a DQ test suite with anomaly
baselines, lineage/observability wiring, a PII/governance audit, an experiment-measurement validity
check — plus fillable checklists and a handoff.yaml companion (carrying volatile_facts) that
feeds backend/ai/quality/operate/growth/frontend downstream, grounded in what each
artifact names and date-stamped for re-verification, never a frozen template dump.
16 references under skills/data/references/ (10 primary jobs, 5 surface overlays, 1 handoff
contract) and 9 fillable assets under skills/data/assets/:
- Assets:
tracking-plan-starter.md·data-contract-starter.odcs.yaml·dq-check-matrix.md·sync-strategy-table.md·schema-compatibility-cheatsheet.md·warehouse-cost-checklist.md·metric-definition-template.md·pii-audit-checklist.md·handoff.yaml
data is the analytical and governed data plane, a peer of frontend, backend, and ai in the
Build stage: it consumes architecture's data-flow boundaries, NFR budgets, threat model, and
handoff.yaml upstream, and hands its contracts, DQ gates, and lineage/telemetry to quality and
operate downstream. It references rather than owns the serving substrate (backend: OLTP
schemas, the DB/vector store as served infra), model cognition (ai: retrieval behavior, prompts,
evals), instrumentation emission (frontend/backend: client/server code emitting against the
contract data defines), experiment design (growth: the OEC and interpretation), and business
outcomes (product: which metrics matter). It works standalone with no upstream artifact required,
and never silently invokes a sibling skill.
skills/data/ runtime skill (SKILL.md + 16 references + 9 assets + agents/openai.yaml)
evals/ activation · traversal · output · compression-ablation fixtures
research/ 4-channel research corpora + build-gate synthesis
docs/ installation and usage
.codex-plugin/ plugin manifest
- docs/installation.md · docs/usage.md · docs/recipes.md
- CHANGELOG.md — release history (current: v0.1.0)
- SOURCES.md — source attribution and license posture
- CONTRIBUTING.md — local validation (
scripts/lint-skill,scripts/check-sync)
MIT — see LICENSE.