# Data Governance

> Establishes ownership, definitions, quality, access, and lineage for the organization's data. Use this when metrics disagree between teams, when nobody knows which dataset is authoritative, when setting up data ownership or access policy, when data quality is unreliable, or before opening a dataset to a wider audience.

- **Type:** Skill
- **Install:** `agentstack add skill-cbrock84-headcount-data-governance`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [cbrock84](https://agentstack.voostack.com/s/cbrock84)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [cbrock84](https://github.com/cbrock84)
- **Source:** https://github.com/cbrock84/headcount/tree/main/plugins/data-analytics/skills/data-governance
- **Website:** https://cbrock84.github.io/headcount/

## Install

```sh
agentstack add skill-cbrock84-headcount-data-governance
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Data governance

Governance has a reputation for bureaucracy because it is usually implemented as approval queues.
Done properly it is the opposite: it makes data usable without asking anyone.

## Start with definitions, not policy

The highest-value governance artifact is a metric dictionary. For each business metric:

- The **plain-language definition** — what it counts, and what it deliberately excludes.
- The **computation**, unambiguously: source table, filters, time grain, timezone.
- The **owner** — a person who decides when it is disputed.
- **Known caveats** — when it is misleading, and what changed historically.

Most metric disputes dissolve once both parties read the same definition and discover they were
measuring different things. Almost none require a policy.

Watch the ones that look obvious. "Active customer," "revenue," and "signup" each have half a dozen
defensible definitions, and the ambiguity surfaces at the worst moment.

## Ownership

Every dataset has a named owner accountable for its quality and access — a person, not a team.
Unowned datasets decay, and nobody notices until a decision is made on stale data.

The owner should sit with the business meaning, not with the pipeline. The team that generates the
data understands what it means; the platform team understands how it moves.

## Quality, measured rather than asserted

Test data like code, continuously, and alert on failures:

- **Freshness** — did it arrive when expected?
- **Volume** — is the row count within its normal range? A silent drop to zero is the classic
  failure.
- **Uniqueness and nullity** on key fields.
- **Referential integrity** across joins.
- **Distribution** — has the shape shifted in a way nothing explains?

The point is finding breakage before a decision is made on it. A pipeline that fails loudly is
better than one that silently produces yesterday's numbers.

## Access

Default to open for internal, non-personal data. Restrictive-by-default drives the shadow spreadsheet
layer, which is genuinely less safe than a governed warehouse.

Personal, financial, and regulated data are the exception: least privilege, purpose stated, reviewed
periodically, with Legal & Risk involved on anything with a lawful-basis question.

## Lineage

Know where a number came from and what feeds it. Without lineage, you cannot answer the two
questions that matter during an incident: what broke upstream, and what downstream is now wrong.

## Never

- Let two systems each claim to be the source of truth for the same fact.
- Fix a data-quality issue in a dashboard. Fix it upstream or it recurs in every other consumer.
- Retire a dataset because it looks unused — you cannot see every consumer. Deprecate, announce,
  then remove.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [cbrock84](https://github.com/cbrock84)
- **Source:** [cbrock84/headcount](https://github.com/cbrock84/headcount)
- **License:** MIT
- **Homepage:** https://cbrock84.github.io/headcount/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-cbrock84-headcount-data-governance
- Seller: https://agentstack.voostack.com/s/cbrock84
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.