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 ux-product-auditorgit 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/ux-product-auditor)<a href="https://agentmods.dev/skills/cbrock84/headcount/ux-product-auditor"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/ux-product-auditor/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/ux-product-auditor"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/ux-product-auditor.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 | $0.01007 |
| Opus 5 | $0.00043 | $0.00504 |
| Sonnet 5 | $0.00017 | $0.00201 |
| Haiku 4.5 | $0.00009 | $0.00101 |
Grade A, and why
ux-product-auditor 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 6d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UX and product auditor
A finding that does not name a consequence is an opinion. Every item in an audit connects a specific friction to a specific outcome.
Audit by walking the real path
Do the actual task, on the actual device, as a first-time user with no context — not a tour of the screens. Most serious problems live in transitions between screens, which a screen-by-screen review never sees.
Then walk it again as a returning user, whose needs are opposite: speed over explanation, and recovery from whatever state they left in.
Lenses
Run each separately; combined, you find only what you were already looking for.
- Comprehension — can a stranger tell what this does and whether it is for them?
- First value — how many steps to the first genuinely useful moment, and how many are avoidable?
- Friction — every field, click, decision, and wait. Which are load-bearing and which are habit?
- Trust — does anything ask for more than it has earned at that point?
- Recovery — what happens on error, empty, slow, offline, or wrong input?
- Accessibility — contrast, keyboard reachability, target size, meaning carried by color alone. Below this floor, some people cannot use the product at all.
Severity
Assign one to every finding, and be strict — an audit where everything is critical has ranked nothing:
- Critical — blocks the primary task, loses data, or excludes a group of users entirely.
- High — measurably costs conversion or activation for many users.
- Medium — friction with a workaround, or affects a narrower path.
- Low — polish, inconsistency, or a preference.
Finding format
Each finding carries: where (the exact screen and step), what the user experiences, why it costs something, the consequence in business terms, severity, and the fix with rough effort.
"The signup form asks for company size before the account exists; users who do not know it guess or abandon; this sits before the only conversion event on the page" is a finding. "Form is too long" is a note.
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.
- 6d ago First seen · 96 lines · 86 tokens per session scan A 8a030df2c91a
ux-product-auditor is a skill published in the GitHub repository cbrock84/headcount (1,335 stars, last pushed 6d ago), licensed MIT. It adds 86 tokens to every session and 1,007 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 2026-09-03.
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