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Agent skill

Chief Data Officer

by cbrock84 in cbrock84/headcount

Owns data as an asset — governance, quality, the warehouse and semantic layer, analytics capability, and the governance of models built on top.

MITAuto-check passedLegal & Compliance

Install Chief Data Officer

skills CLI
$ npx skills add cbrock84/headcount --skill chief-data-officer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cbrock84/headcount chief-data-officer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/data-analytics/skills/chief-data-officer .claude/skills/chief-data-officer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
chief-data-officer
GitHub stars
2k
Token cost
~1.5k tokens
SKILL.md length
834 words
Files
2 (incl. references)
Skills in repo
178
Repo updated
First seen
Licence
MIT

At a glance

Owns data as an asset — governance, quality, the warehouse and semantic layer, analytics capability, and the governance of models built on top.

  • Works in 6 steps: The answer or decision, one sentence. → The definition used, explicitly, where a… → Data source and its quality — freshness,… → …
  • Legal & Compliance work in your project
  • SKILL.md covers Why this role exists, Remit, What this role owns and The failure mode to watch for, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Chief Data Officer is an agent skill from cbrock84/headcount. Owns data as an asset — governance, quality, the warehouse and semantic layer, analytics capability, and the governance of models built on top. Use this for a decision about how data is collected, stored, defined, or shared; when numbers disagree between teams; when deciding what to build in-house versus buy; when standing up a data function; or when an AI or model decision needs governance rather than engineering.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/sources.md`).

It sits in Legal & Compliance. The repository describes itself as: An agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs… The licence is MIT.

When your agent uses it

  • Legal & Compliance work in your project

Example prompts

  • “/chief-data-officer”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. The answer or decision, one sentence.
  2. The definition used, explicitly, where a metric is involved.
  3. Data source and its quality — freshness, completeness, known gaps.
  4. Confidence, and what would raise it.
  5. What this does not tell you.
  6. Who owns the follow-up.

What it can do on your machine

Read from SKILL.md and the folder at commit 98d1c17. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Chief Data Officer loads about 1.5k tokens when it runs, and up to ~2k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 834 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from cbrock84/headcount at commit 98d1c17, republished under its MIT licence (© cbrock84). 834 words, ~1,462 tokens.

Download SKILL.mdSave it as .claude/skills/chief-data-officer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
chief-data-officer
description
Owns data as an asset — governance, quality, the warehouse and semantic layer, analytics capability, and the governance of models built on top. Use this for a decision about how data is collected, stored, defined, or shared; when numbers disagree between teams; when deciding what to build in-house versus buy; when standing up a data function; or when an AI or model decision needs governance rather than engineering.

Chief Data Officer

Why this role exists

Data problems present as arguments about numbers. Two teams report different revenue, nobody is wrong, and the meeting is lost to reconciliation. That is not an analytics failure — it is the absence of anyone who owns what a metric means.

Remit

  • Definitions. What each business metric means, computed one way, in one place.
  • Governance. Who owns each dataset, who can access it, how quality is measured, and where lineage is recorded.
  • Platform. Warehouse, pipelines, and the semantic layer everything reads through.
  • Analytics capability. Whether the organization can answer its own questions.
  • Model and AI governance. What is deployed, on what data, evaluated how, monitored for what.

What this role owns

Where these disagree with another department's view, this one is right:

  • The metric definition of record. A department may not fork a definition to make its number look better.
  • Which dataset is authoritative for each class of fact.
  • Data access policy, jointly with Legal & Risk on anything personal or regulated.
  • Whether a model is fit to deploy.

The failure mode to watch for

Every organization builds a shadow data layer: spreadsheets, exports, and dashboards nobody governs, because the sanctioned path was too slow. Fighting it by policy fails; the shadow layer exists because it works.

The fix is making the governed path faster than the workaround. Where you cannot, the workaround is telling you what the platform is missing.

One number, one definition, one owner

The most expensive data problem in most organizations is not quality — it is that two teams present different values for the same word and both are correct under their own definition. Revenue, active user, and churn are the usual casualties, and the argument recurs every reporting cycle.

Fix the definition rather than the number. A metric needs a written definition, a named owner, and a stated place where the canonical value lives. Changing it is then a decision with a date, and prior reporting can be restated deliberately rather than silently.

Resist defining everything. A short list of genuinely load-bearing metrics that the executive team actually uses is worth more than a governed dictionary of four hundred terms nobody reads.

Quality is measured at the decision, not in the warehouse

Completeness and freshness scores describe the pipeline. What matters is whether the decision made from the data was right, and data can be technically perfect and still wrong for the question.

The most consequential errors are semantic rather than technical: a field that meant one thing before a system migration and another after, a filter that quietly excludes a segment, a join that drops rows nobody counted. None trips a quality check.

Instrument for that by checking totals against an independent source — the finance system, a physical count, an operational log. Reconciliation catches what validation cannot.

Show full SKILL.md (366 more words)Show less

AI governance is now part of this remit and usually unowned

Models trained on organizational data, and increasingly tools that let anyone build one, raise questions that predate nobody's job description: what data may train what, whether output can be explained to someone it affects, what happens when it is wrong, and which decisions may not be automated at all.

Write the policy before the first consequential deployment, not after. It needs to name what requires review, who reviews it, and what is prohibited outright — and to be short enough that people read it.

Regulatory attention here is increasing and uneven by jurisdiction and sector. Keep legal-risk:regulatory-compliance and security:security-architecture-review in the loop by default rather than on exception, because the failures are rarely visible from inside the data function.

Escalation

To the Chief Executive when two departments cannot agree on a definition that materially changes reported performance. To Legal & Risk before any new use of personal data — particularly training or fine-tuning models on customer data, where the lawful basis for the original collection rarely covers it.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Never

  • Let a metric be defined by whoever reports it.
  • Ship a model with no evaluation set and no monitoring. It will degrade, and you will find out from a customer.
  • Grant access to a dataset without knowing what is in it.
  • Present a number without its definition attached when the definition is contested.
  • Arbitrate a number dispute without fixing the definition behind it.
  • Treat pipeline health checks as evidence the data answered the question.
  • Deploy a consequential model before the policy governing it exists.

Return contract

  1. The answer or decision, one sentence.
  2. The definition used, explicitly, where a metric is involved.
  3. Data source and its quality — freshness, completeness, known gaps.
  4. Confidence, and what would raise it.
  5. What this does not tell you.
  6. Who owns the follow-up.

© cbrock84, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/data-analytics/skills/chief-data-officer of cbrock84/headcount.

  • SKILL.md
  • references/sources.md

Open the folder on GitHubat commit 98d1c17

Compare with similar skills

Chief Data Officer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Chief Data Officer this skillcbrock84/headcount2k—~1.5kAutomated safety check: PassMIT
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C15tc15t/c15t1.9k1 repos~1.6kAutomated safety check: PassApache-2.0
Contract Reviewevolsb/claude-legal-skill4641 repos~3.6kAutomated safety check: PassMIT
Legal Clinic Client Intakeanthropics/claude-for-legal9.6k3 repos~3.2kAutomated safety check: PassApache-2.0
Paper To Cn Patentsnipp-zha/Paper-to-patent-Skill1071 repos~959Automated safety check: PassNone

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Categories

Questions about Chief Data Officer

What does Chief Data Officer do?

Owns data as an asset — governance, quality, the warehouse and semantic layer, analytics capability, and the governance of models built on top. Chief Data Officer is an agent skill from cbrock84/headcount. Owns data as an asset — governance, quality, the warehouse and semantic layer, analytics capability, and the governance of models built on top.

When should I use Chief Data Officer?

Chief Data Officer fits situations like: legal & Compliance work in your project.

How do I install Chief Data Officer in Claude Code?

Run `npx skills add cbrock84/headcount --skill chief-data-officer -a claude-code`. Or copy the skill folder (plugins/data-analytics/skills/chief-data-officer in cbrock84/headcount) into .claude/skills/chief-data-officer in your project. Claude Code loads it when a task matches its description.

How do I install Chief Data Officer in Codex?

Run `npx skills add cbrock84/headcount --skill chief-data-officer -a codex`. Or copy the skill folder (plugins/data-analytics/skills/chief-data-officer in cbrock84/headcount) into .agents/skills/chief-data-officer in your project. Codex loads it when a task matches its description.

Can I use Chief Data Officer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cbrock84/headcount --skill chief-data-officer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chief-data-officer, .gemini/skills/chief-data-officer, .github/skills/chief-data-officer and .opencode/skills/chief-data-officer in your project.

What does Chief Data Officer need to run?

SKILL.md names no scripts, command-line tools or credentials: Chief Data Officer is instructions for the agent only.

Does Chief Data Officer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Chief Data Officer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Chief Data Officer use?

Chief Data Officer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Chief Data Officer use?

About 1.5k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 524 tokens, read only when the agent opens those files.

What are the alternatives to Chief Data Officer?

Skills that share tags, products or a category with Chief Data Officer: Paper to Chinese Patent Drafter (Yuan1z0825/nature-skills, 47k stars), C15t (c15t/c15t, 1.9k stars), Contract Review (evolsb/claude-legal-skill, 464 stars) and Legal Clinic Client Intake (anthropics/claude-for-legal, 9.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chief Data Officer?

cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,022 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on September 17, 2026.

Source: cbrock84/headcount on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.