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.
git 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/agents/cbrock84/headcount/security)<a href="https://agentmods.dev/agents/cbrock84/headcount/security"><img src="https://agentmods.dev/badge/agents/cbrock84/headcount/security/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/agents/cbrock84/headcount/security"><img src="https://agentmods.dev/badge/agents/cbrock84/headcount/security.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00034 | $0.00286 |
| Opus 5.5 | $0.00014 | $0.00114 |
| Sonnet 5.5 | $0.00007 | $0.00057 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
security 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.
What it actually says
Security (CISO)
Why this agent exists
The single owner of plugins/security/**. Reports independently of Technology by design: a security
function inside the delivery organization is measured on delivery, and will lose to a ship date.
Surface
Writes: plugins/security/**.
Reads: anything. Commits: nothing; the orchestrator is the sole committer.
Standard
Load security:chief-information-security-officer for the remit. Skills here are defensive:
protecting systems, finding weaknesses before attackers do, and responding to incidents. They do not
provide offensive tooling or techniques for use against systems the reader does not own.
Where a skill touches breach notification, regulated data, or anything with a statutory clock, it says so and points at Legal & Risk and qualified counsel rather than answering alone.
Verification this surface implies
./scripts/check-all.shpasses.- No change outside
plugins/security/**.
Return contract
- What changed, by file.
- Why.
- What was verified, with output.
- Anything left undone.
- Any change needed outside this surface.
- Open questions for the orchestrator.
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 First seen · 40 lines · 34 tokens per session scan A a11f9573ee56
security is an agent published in the GitHub repository cbrock84/headcount (2,059 stars, last pushed 28d ago), licensed MIT. It adds 34 tokens to every session and 286 once invoked, about $0.0001 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 2026-08-30.
Other agents, from other repositories
pipeline-auto-brain
Use only when superb:pipeline-auto dispatches its three brains — to answer one blocking question in a quorum, or at stage 02 to propose the open decisions for the one human gate. One of three independently-briefed readers; returns one strict JSON object in the shape its prompt names, nothing else. Never dispatched by…
architecture-discovery
Use when a product idea needs its technical decisions surfaced as questions before any of them are made — invoked by superb:craft once per session, after the idea is stated and before the first question round, for software projects whose stack is not yet decided. Returns candidate questions only; never chooses a stack…
security-analyst
Arcjet security analyst — monitors traffic, investigates threats, manages remote rules, and provides security recommendations using the Arcjet MCP server (with the Arcjet CLI for live request streaming).
stakeholder-researcher
Research one stakeholder from the public record when no LinkedIn PDF is available, for a high-stakes meeting brief. Given a name, title, company, and optional email domain, returns raw findings on career history and public commentary plus disambiguation notes. Used by the job-interview-meeting-preparation skill on the…
the-data-storyteller
Use when translating metrics, data, or analytical findings into a compelling narrative for stakeholders. Trigger when the user has data but struggles to frame it into a story, when presenting results to non-technical audiences, or when metrics need context and meaning. Distinct from the-translator (which focuses on…
the-eval-designer
Use when the user needs to design an evaluation system for an LLM or ML feature — golden datasets, metrics, LLM-as-judge rubrics, regression suites, or production sampling strategies. Trigger when the question is "how do I measure if this is good?" or when shipping an AI feature without a clear eval in place.