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 quantitative-analysisgit 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/quantitative-analysis)<a href="https://agentmods.dev/skills/cbrock84/headcount/quantitative-analysis"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/quantitative-analysis/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/quantitative-analysis"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/quantitative-analysis.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 | $0.00095 | $0.01036 |
| Opus 5.5 | $0.00038 | $0.00414 |
| Sonnet 5.5 | $0.00019 | $0.00207 |
| Haiku 4.5 | $0.00010 | $0.00104 |
Grade A, and why
quantitative-analysis 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantitative analysis
A wrong answer here is rarely an arithmetic error. It is a correct calculation on the wrong comparison, or on data that does not mean what the field name suggests.
Frame the question so that an answer changes something
Start from the decision. "How is retention doing" has no answer; "is the cohort we changed onboarding for retaining better than the one before it, enough to justify rolling it out" does.
Write down what you expect to find and what you would do in each case before you look. If every possible result leads to the same action, the analysis is not worth running — and knowing that in advance is worth more than the analysis would have been.
Choose the comparison before the metric
Almost every meaningful number is a comparison, and the choice of what to compare against does more work than the calculation.
- Against what it was — needs a period long enough to see through seasonality and noise.
- Against what it would have been — the strongest comparison and the hardest to construct. A holdout group, a matched segment, a pre-trend extended forward.
- Against a peer or a benchmark — only useful if the definitions genuinely match, which they usually do not.
Name the counterfactual explicitly. "Revenue rose after the campaign" is a comparison against nothing, and it is the single most common way credit is claimed for a trend that was already happening.
Interrogate the data before you trust it
Look at the raw rows. Check when collection started and whether the definition changed partway. Check null rates, duplicates, and test or internal accounts still in the set. Check whether recent periods are still filling in — partial data at the tail is what produces the "sudden decline" that resolves itself a week later.
A field's name is not its definition. Find out what actually writes it and under what conditions, especially for anything named status, type, active, or created.
Know the traps that produce confident wrong answers
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 9b76a488da83
- 1mo ago First seen · 84 lines · 95 tokens per session scan A a95b1f686c47
quantitative-analysis is a skill published in the GitHub repository cbrock84/headcount (2,077 stars, last pushed 3d ago), licensed MIT. It adds 95 tokens to every session and 1,036 once invoked, about $0.0004 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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