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 onboarding-and-offboardinggit 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/onboarding-and-offboarding)<a href="https://agentmods.dev/skills/cbrock84/headcount/onboarding-and-offboarding"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/onboarding-and-offboarding/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/onboarding-and-offboarding"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/onboarding-and-offboarding.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 | $0.00069 | $0.00784 |
| Opus 5 | $0.00034 | $0.00392 |
| Sonnet 5 | $0.00014 | $0.00157 |
| Haiku 4.5 | $0.00007 | $0.00078 |
Grade A, and why
onboarding-and-offboarding 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 6d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboarding and offboarding
Both ends are cross-functional processes that fail at the handoffs. The failures are predictable, which means they are preventable by design rather than by diligence.
Onboarding starts before day one
Everything procurable in advance should be ready: accounts, hardware, access, a first assignment, and a named person responsible for the human side. A first day spent waiting for a laptop is a permanent first impression, and it is entirely a coordination failure.
The identity and equipment steps are executed by
it-operations:identity-lifecycle-administration and it-operations:endpoint-management; access
policy — what a role should be entitled to — belongs to security:access-and-identity. This skill
owns the sequence and whether it actually completed.
Ramp deliberately
Separate the three things a new person is learning, because they need different support:
- The job — often the part they already have.
- The context — the systems, the codebase, the customers, the history.
- The people — who decides what, who to ask, how things really get done.
The third is the one nobody plans and the one that most determines how quickly someone becomes useful. Make introductions explicit assignments rather than leaving them to chance and extroversion.
Give real work early, scoped to succeed. A meaningful contribution in the first fortnight does more for confidence and belonging than any amount of orientation material.
Check in at deliberate intervals — a week, a month, a quarter — with the question phrased so problems can surface. "What has been more confusing than it should be?" gets an answer; "how's it going?" does not.
Offboarding: knowledge before access
Knowledge capture has to precede the access cutoff, which means starting on the day notice is given, not the day before departure.
Prioritize what is genuinely undocumented and only in one head: the systems they alone maintain, the relationships they hold, the decisions whose reasoning exists nowhere. Ask directly — "what will break, and who will not know why?" — because they know and will usually say.
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.
- 6d ago First seen · 76 lines · 69 tokens per session scan A fc1165493aef
onboarding-and-offboarding is a skill published in the GitHub repository cbrock84/headcount (1,335 stars, last pushed 7d ago), licensed MIT. It adds 69 tokens to every session and 784 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 2026-09-03.
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Use when the user wants to coach someone — a report, peer, or mentee — through a problem rather than solve it for them. Trigger phrases include "coach me through how to talk to [report]", "I want to help [name] figure out X without telling them what to do", "let me practice a coaching conversation", "GROW conversation…
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Use to create or update the user's anchor profile — a single private file (/bettersense-work-reflections/profile.md) that captures who the user is, what they do, what context other skills need to know about them. Read automatically by other skills in the bundle when present, so the user doesn't re-explain themselves…