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 paid-advertisinggit 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/paid-advertising)<a href="https://agentmods.dev/skills/cbrock84/headcount/paid-advertising"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/paid-advertising/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/paid-advertising"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/paid-advertising.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 | $0.00078 | $0.00864 |
| Opus 5.5 | $0.00031 | $0.00346 |
| Sonnet 5.5 | $0.00016 | $0.00173 |
| Haiku 4.5 | $0.00008 | $0.00086 |
Grade A, and why
paid-advertising 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid advertising
Paid is the fastest way to buy a result and the fastest way to buy nothing. The difference is mostly discipline before launch.
Before spending
- Know the ceiling. Maximum acceptable acquisition cost, derived from margin and payback period, not from what feels affordable.
- Conversion tracking verified end to end, with a test conversion confirmed in the platform. Optimizing against broken tracking teaches the algorithm the wrong thing, and that damage persists.
- The destination is ready. Paid traffic to a page that does not convert is a donation. Fix the page first — it is cheaper.
Structure
Structure follows intent, since intent determines what a click is worth.
- Search — separate by intent tier: brand, high-intent problem terms, category terms, broad research. Never one budget across all four; the broad terms will consume it.
- Social — structure by audience, since creative fatigue and audience saturation are the two variables and you need to see them separately.
- Enough volume per campaign to exit the learning phase. Over-segmentation starves every campaign of the data it needs.
Naming conventions
Decide the convention before the first campaign, because renaming later breaks historical reporting on every platform.
A workable pattern encodes, in fixed order: channel, objective, audience or intent tier, geography, and creative theme — separated consistently, with no spaces. It should be possible to filter a report by any one of those segments without opening a campaign.
Apply it to every level: campaign, ad set, and ad. Inconsistent naming is why cross-channel reporting takes a week to assemble and why nobody trusts it when it arrives.
Creative
Creative is the biggest lever on paid social and the most neglected.
- Test genuinely different angles, not variations of one. Five headlines around one idea is one test.
- The first frame or line does the work. Assume the rest is not seen.
- Match the ad's promise to the landing page exactly. Mismatch shows up as a bounce you will misdiagnose as a targeting problem.
- Expect fatigue and plan refreshes on a schedule; rising cost with flat conversion rate is usually fatigue, not competition.
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 c0041efc4d04
- 1mo ago Changed · +12 lines df6b780bb17e
- 1mo ago First seen · 68 lines · 78 tokens per session scan A b32fe33d6e85
paid-advertising is a skill published in the GitHub repository cbrock84/headcount (2,089 stars, last pushed 8d ago), licensed MIT. It adds 78 tokens to every session and 864 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 2026-08-30.
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