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 agentmods add skills/cbrock84/headcount/marketing-analyticsnpx skills add cbrock84/headcount --skill marketing-analyticsgit 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/marketing-analytics)<a href="https://agentmods.dev/skills/cbrock84/headcount/marketing-analytics"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/marketing-analytics.svg" alt="Measured on agentmods" 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 | $0.00068 | $0.00690 |
| Opus 5 | $0.00034 | $0.00345 |
| Sonnet 5 | $0.00014 | $0.00138 |
| Haiku 4.5 | $0.00007 | $0.00069 |
Grade A, and why
marketing-analytics 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 2d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing analytics
The tracking plan comes first
Dashboards built on bad instrumentation are confident and wrong, which is worse than having none.
Define, in writing, before implementing: every event, when it fires, its properties and their types, and the question each one exists to answer. An event with no question behind it is noise that will be maintained forever.
Naming convention decided once and enforced: object_action, lowercase, past tense. Inconsistent
naming is unfixable later without breaking historical data.
Auditing existing tracking
Numbers nobody trusts usually come from one of:
- Double-firing on route changes in single-page apps.
- Events that stopped when someone changed a selector or a component.
- Definition drift — two tools counting "signup" at different moments.
- Bot and internal traffic never filtered out.
- Consent and blockers removing a meaningful and non-random share of data.
Verify by doing the action yourself and watching the event arrive with the properties you expect. Not by reading the dashboard.
Attribution
Every model is wrong in a known direction. Pick deliberately and state the bias:
- Last-touch — over-credits closing channels: brand search, retargeting. Under-credits everything that created demand.
- First-touch — the mirror image; over-credits discovery.
- Multi-touch — better, and dependent on complete tracking you probably do not have.
- Incrementality testing — the only method that answers "would this have happened anyway." The most expensive and the most trustworthy.
Use one model consistently for decisions, and check it periodically against a holdout. Switching models to make a channel look better is how organizations mislead themselves.
Reporting
Every report answers one question for one audience. Reports built to display everything get read by nobody.
Show the metric, its comparison period, and the decision it informs. A number with no comparison is not information. Where a number moved, the report should say why or say that the cause is unknown — "unknown" is a legitimate and useful finding.
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.
- 2d ago Changed · +19 lines 832f8fa0a256
- 6d ago First seen · 54 lines · 68 tokens per session scan A dcc31dd1ea2f
marketing-analytics is a skill published in the GitHub repository cbrock84/headcount (1,284 stars, last pushed 3d ago), licensed MIT. It adds 68 tokens to every session and 690 once invoked, about $0.0003 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 2026-08-30.
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