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 retentiongit 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/retention)<a href="https://agentmods.dev/skills/cbrock84/headcount/retention"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/retention/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/retention"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/retention.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 55 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 | $0.00077 | $0.00640 |
| Opus 5.5 | $0.00031 | $0.00256 |
| Sonnet 5.5 | $0.00015 | $0.00128 |
| Haiku 4.5 | $0.00008 | $0.00064 |
Grade A, and why
retention 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.
Retention
Separate the two churns first
They have nothing in common but the outcome, and conflating them wastes effort:
- Involuntary — payment failed. Often a large share of total churn, entirely mechanical, and the cheapest thing to fix in the whole business.
- Voluntary — they chose to leave.
Fix involuntary first. Card retries on a sensible schedule, dunning emails that reach a human, pre-expiry notification, and a grace period that does not immediately cut off access. This is recoverable revenue sitting untouched in most companies.
Diagnosing voluntary churn
Ask when the decision was actually made. It is almost never at cancellation — it is weeks earlier, at a failed expectation, an unresolved support issue, or a champion leaving.
Segment churn by tenure, plan, acquisition channel, and activation status. Concentrations tell you the cause:
- Early churn — activation problem, not retention. Fix onboarding.
- Churn at renewal — value not visible enough to justify the line item.
- Churn after a specific event — find the event: a price change, an outage, a redesign, a champion departure.
- Churn concentrated in one channel — an acquisition problem. You are buying the wrong customers, and no retention work fixes that.
Cancellation flow
Make canceling straightforward. Obstruction generates chargebacks, public complaints, and in a growing number of jurisdictions, regulatory exposure.
Do ask why, with specific options plus free text — this is the highest-quality product feedback you will ever receive, from people with no reason to be polite.
Offer a save only where it addresses the stated reason. A discount offered to someone leaving because a feature is missing confirms you were not listening. Pause is often the better offer and is rarely available.
At-risk detection
Build a simple signal from declining usage, a support escalation, a champion going quiet, or a seat count dropping. Then act on it while intervention is still possible — a health score nobody works is a dashboard, not a program.
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 98346e4cf23c
- 1mo ago First seen · 58 lines · 77 tokens per session scan A eda4f20f763d
retention is a skill published in the GitHub repository cbrock84/headcount (2,077 stars, last pushed 2d ago), licensed MIT. It adds 77 tokens to every session and 640 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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