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 privacy-and-data-protectiongit 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/privacy-and-data-protection)<a href="https://agentmods.dev/skills/cbrock84/headcount/privacy-and-data-protection"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/privacy-and-data-protection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cbrock84/headcount/privacy-and-data-protection"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/privacy-and-data-protection.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00086 | $0.00732 |
| Opus 5.5 | $0.00034 | $0.00293 |
| Sonnet 5.5 | $0.00017 | $0.00146 |
| Haiku 4.5 | $0.00009 | $0.00073 |
Grade A, and why
privacy-and-data-protection 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 1mo 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy and data protection
Not legal advice. Regimes differ by jurisdiction and change; material questions need qualified counsel. This structures the assessment and identifies what to escalate.
Start from the data map
You cannot assess what you have not inventoried. For each category of personal data:
- What is collected, from whom, and where it came from.
- Why — the specific purpose, and the lawful basis where one is required.
- Where it lives, who can reach it, and which vendors receive it.
- How long it is kept, and what deletes it. "Indefinitely" is a finding, not an answer.
- Whether it crosses a border, and under what mechanism.
Most privacy failures are inventory failures: data nobody remembered was being collected, in a system nobody owned.
Design decisions that prevent problems
- Collect less. Every field is a liability with a maintenance cost. The cheapest way to protect data is not to hold it.
- Purpose limitation is real. Data collected for one purpose is not automatically available for another — particularly for training models, which is where this most often goes wrong now.
- Separate identifiers from behavior where analysis does not require linkage.
- Retention with an enforcing mechanism. A policy with no deletion job is a statement of intent.
Consent, where it applies
Specific, informed, freely given, and as easy to withdraw as to give. Pre-ticked boxes, bundled consent, and cookie walls that offer no genuine choice fail on their face in the regimes that require consent.
Note that consent is one lawful basis among several and often the weakest — it can be withdrawn, and then the processing must stop.
Vendors
Any third party processing personal data on your behalf needs a written agreement covering purpose, security, sub-processors, deletion, and assistance with subject rights. Sending data to a vendor without one is a common and easily avoided violation.
Assess the vendor's actual security, not their questionnaire answers, in proportion to the sensitivity of what they will hold.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · +7 lines 265791351918
- 1mo ago First seen · 65 lines · 86 tokens per session scan A f66b59ed7c2a
privacy-and-data-protection is a skill published in the GitHub repository cbrock84/headcount (2,081 stars, last pushed 5d ago), licensed MIT. It adds 86 tokens to every session and 732 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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