headcount: 172 skills organized as a company org chart, now with a Chinese README

There is a specific kind of mess that shows up once you have more than about thirty prompts saved. You stop remembering what you have. Two prompts do nearly the same thing and you can't tell which one is current. You paste a whole "mega-prompt" into a session because you can't be bothered to find the right one, and half of it is irrelevant to the task. headcount is one answer to that. It arranges agent skills as a company: one chief executive, 16 departments, 172 skills. Each department installs as its own plugin, so a project loads only what it needs. Skills are addressed as department:skill, which means names never collide across departments. The original repository is cbrock84/headcount, 2047 stars, written in Markdown, MIT licensed. A Chinese translation of the README now exists at yangshun2005/headcount-cn. That's what this post is mostly about, because the translation does something a plain translation usually doesn't. The official one-liner: an agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs in Claude Code and ChatGPT. The README's own numbers are 16 departments and 172 skills. The one-liner says 15+ and 125+. Both appear in the source material; I'm not going to reconcile them, and you shouldn't assume the smaller number is the stale one — the README's department tables are the more granular source. The structural idea is the part worth understanding. Most prompt libraries are a flat list. headcount is a tree with an install boundary at every branch. The CEO office sits at the top with 7 skills, and the departments below it each carry their own skills plus an agent charter in .claude/agents/. That charter is what lets a department be delegated to as a subagent with its own exclusive write scope — the producer/auditor split the agent-hierarchy skill describes. Addressing is namespaced. security:threat-modeling, finance:unit-economics, demand-generation:landing-page-cro-expert. You can ask in natural language and let matching load the skill, or you can call it by name when you want a specific perspective: /finance:financial-modeling Claude Code: /plugin marketplace add cbrock84/headcount /plugin install security@headcount Then add departments as you need them — /plugin install it-operations@headcount, and so on. ChatGPT and Codex use the same repository. You add it as a plugin marketplace (ChatGPT reads the manifest at .agents/plugins/marketplace.json), or you copy the departments you want into your own project's .agents/skills/ directory. The skill files are identical on both sides; only the manifests differ, and both manifests are generated from the same directory tree, so a fix lands in both at once. That's what AGENTS.md is about. Here is the constraint that explains the whole design. On the ChatGPT/Codex side, skill descriptions share a context budget of roughly 8000 characters. All 16 departments together come to roughly 50000. Installing only the departments you'll actually use isn't tidiness — it's the only thing that fits. If you've ever wondered why a plugin system would bother with per-department granularity instead of one big install, that ratio is the answer. Installing a department doesn't touch your project. It adds skills to the assistant. It doesn't write to your repo, add dependencies, or run anything on its own. The Chinese repository is a translation of the original README, dated 2026-10-14, with the original preserved as README_en.md. Stars: zero, as of writing. It was published today. I'm not going to pretend otherwise. The interesting part is that it isn't only a translation. The page carries two clearly separated layers: A 中文快速上手 section at the top, explicitly marked as supplementary orientation material, not part of the original. It answers the questions a Chinese-speaking developer hits in the first five minutes: what problem does this solve, what's the shortest path to running it, what are the requirements, what are the traps. The translated README body below it, which the page states aims to stay faithful to the original. That split matters more than it sounds. A faithful translation of headcount's README gives you the department tables and the install commands, but it doesn't tell you that the ChatGPT-side 8000-character budget is why you should install two departments instead of sixteen. The original README does mention the budget — but it's buried in a section you'd only read after you'd already formed the wrong mental model. The Chinese page pulls it forward into the orientation section, along with a note that installing a department won't modify your repository, and a note that the original doesn't specify OS, hardware, or version requirements. That last one is a small honesty signal worth pointing out. A lot of translated READMEs quietly fill gaps with plausible-sounding requirements. This one writes 原文未说明 — "not stated in the original" — and moves on. Same for the domestic-network section: it says the original doesn't mention mirrors or accelerated downloads, rather than inventing a mirror that may not exist. For a Chinese-speaking developer, the practical difference is the entry cost. The install path is three commands and a natural-language question, and the orientation section gets you there without reading a full README in a second language. The department:skill addressing scheme, the auto-loading behavior, and the "which departments should I install first" question are all answered before the translated body starts. The page also invites issues or PRs for translation inaccuracies, which is the right posture for a translated doc that will drift as the upstream changes. The skills themselves are prose. Markdown files describing how a specialist should approach a problem. Whether security:threat-modeling produces something useful depends on the model you're running it against and the specifics of your system, and I haven't tested it against a real threat model. My sample is zero. Treat the department tables as a map of what's covered, not as evidence of output quality. What is verifiable is the structure. Namespaced addressing that prevents collisions. Per-department install boundaries that respect a real context limit. Agent charters that give a department an exclusive write scope when delegated. And a documentation layer that's been made navigable for a second audience without rewriting the source. If you've got a flat prompt folder that's gotten out of hand, the install is cheap and reversible — you add a marketplace, install one department, and see whether the matching actually fires on your questions. If you read Chinese, start with the Chinese page; the orientation section is the fastest route to a working setup. If you don't, the original README and docs/GETTING-STARTED.md cover the same ground. One thing to watch: the Chinese repo is a translation, so it tracks upstream with a lag. The translation date is 2026-10-14. If the original adds departments after that, the Chinese page won't have them until someone updates it. Chinese documentation: https://github.com/yangshun2005/headcount-cn I'm an independent enterprise AI delivery consultant — private LLM deployment, RAG, agent workflows, AIOps. If your team is stuck somewhere in that stack, my inbox is open.