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 revenue-operationsgit 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/revenue-operations)<a href="https://agentmods.dev/skills/cbrock84/headcount/revenue-operations"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/revenue-operations/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/revenue-operations"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/revenue-operations.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 | $0.00073 | $0.00947 |
| Opus 5 | $0.00036 | $0.00474 |
| Sonnet 5 | $0.00015 | $0.00189 |
| Haiku 4.5 | $0.00007 | $0.00095 |
Grade A, and why
revenue-operations 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 5d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revenue operations
Definitions before dashboards
Most revenue reporting arguments are definitional. Write down and get agreement on, in one place:
- What each lifecycle stage means and the observable event that moves a record into it.
- What makes a lead qualified — and by whose judgment.
- When an opportunity is created, and what evidence is required.
- What each pipeline stage requires to be entered, stated as a buyer action rather than a seller feeling. "Prospect has confirmed budget" is observable; "showing strong interest" is not.
- What closed-lost means versus stalled, and when a stalled deal exits the pipeline automatically.
Without these, every number is negotiable and forecasting is a genre of fiction.
The handoff
Where most revenue leaks. Specify: the exact criteria for passing a lead, the SLA for first contact, what context transfers with it, and the route back when it is rejected — including the reason, recorded.
A rejection loop with no recorded reason means marketing keeps sending the same unqualified leads, and both sides believe the other is the problem.
Lead scoring
Scoring exists to route attention, not to produce a number. If sellers do not change what they work on because of the score, it is decoration.
Score on two independent dimensions and keep them separate:
- Fit — do they look like a customer? Company size, industry, geography, role and seniority, technology in use. Static, knowable before any engagement.
- Intent — are they acting like a buyer now? Pricing page visits, repeat sessions, demo request, content depth, response to outreach. Dynamic, and it decays.
Collapsing the two into one score is the standard mistake: a perfect-fit account with no activity and a poor-fit account browsing aggressively land on the same number and get treated identically, which is wrong in both directions.
Build the model from closed-won and closed-lost history, not intuition. Look at what actually separated the two, and be prepared for the finding that a favored attribute has no predictive value.
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.
- 5d ago First seen · 95 lines · 73 tokens per session scan A 5453a1a38e23
revenue-operations is a skill published in the GitHub repository cbrock84/headcount (1,320 stars, last pushed 6d ago), licensed MIT. It adds 73 tokens to every session and 947 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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