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 threat-modelinggit 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/threat-modeling)<a href="https://agentmods.dev/skills/cbrock84/headcount/threat-modeling"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/threat-modeling/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/threat-modeling"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/threat-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 57 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00085 | $0.00744 |
| Opus 5.5 | $0.00034 | $0.00298 |
| Sonnet 5.5 | $0.00017 | $0.00149 |
| Haiku 4.5 | $0.00009 | $0.00074 |
Grade A, and why
threat-modeling 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat modeling
Done at design time this is cheap and changes the design. Done after launch it produces a list of things that are expensive to fix, so the timing is most of the value.
Four questions, in order
1. What are we building? A diagram of the actual data flow — not the org chart, not the marketing architecture. Components, the data moving between them, and where each store lives. If nobody can draw it, that is the first finding.
Mark the trust boundaries: every point where data crosses from something you control to something you do not, or from one privilege level to another. Almost every real vulnerability lives on a boundary.
2. What can go wrong? Walk each boundary and each asset. A usable prompt set:
- Spoofing — can someone claim to be another user, service, or system?
- Tampering — can data be modified in transit, at rest, or in the client?
- Repudiation — can someone deny an action, and would we be able to show otherwise?
- Information disclosure — what leaks: to other users, to logs, to error messages, to the client bundle?
- Denial of service — what is unbounded? Uploads, queries, retries, fan-out.
- Elevation of privilege — can a user reach data or actions belonging to another tenant, role, or account?
Two that catch more real bugs than the classic list: what does the client enforce that the server does not, and what happens on the second attempt — replay, race, and double-submit.
3. What are we going to do about it? For each realistic threat: mitigate, transfer, avoid, or accept. Accepting is legitimate; accepting silently is not.
Prioritize by attacker effort against impact, not by how alarming it sounds. A trivially exploitable tenant-isolation bug outranks a theoretical timing attack every time.
4. Did we do a good job? Re-check the model when the design changes. A threat model that describes last quarter's architecture is worse than none, because it produces false confidence.
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 cfd3dcfb8195
- 1mo ago First seen · 58 lines · 85 tokens per session scan A feecef10d70c
threat-modeling is a skill published in the GitHub repository cbrock84/headcount (2,077 stars, last pushed 2d ago), licensed MIT. It adds 85 tokens to every session and 744 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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