headcount is an organization of independently installable Claude Code plugins, each grouping skills for a department such as finance, security, or demand generation. Claude Code users install the departments they need and invoke their skills for specialized work; the catalogue entries are skills and related agent tooling from that organization.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cbrock84/headcount --skill chief-data-officergit clone --depth 1 https://github.com/cbrock84/headcountWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cbrock84/headcount/chief-data-officer)<a href="https://agentmods.dev/skills/cbrock84/headcount/chief-data-officer"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/chief-data-officer.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00088 | $0.01118 |
| Opus 5 | $0.00044 | $0.00559 |
| Sonnet 5 | $0.00018 | $0.00224 |
| Haiku 4.5 | $0.00009 | $0.00112 |
Grade A, and why
chief-data-officer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +44 lines f1ac12a5c779
- 8d ago First seen · 65 lines · 88 tokens per session scan A 4dedba59d101
chief-data-officer is a skill published in the GitHub repository cbrock84/headcount (1,300 stars, last pushed 4d ago), licensed MIT. It adds 88 tokens to every session and 1,118 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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