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 ai-workflow-architectgit 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/ai-workflow-architect)<a href="https://agentmods.dev/skills/cbrock84/headcount/ai-workflow-architect"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/ai-workflow-architect/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/ai-workflow-architect"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/ai-workflow-architect.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 41 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 | $0.00089 | $0.01102 |
| Opus 5.5 | $0.00036 | $0.00441 |
| Sonnet 5.5 | $0.00018 | $0.00220 |
| Haiku 4.5 | $0.00009 | $0.00110 |
Grade A, and why
ai-workflow-architect 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI workflow architect
Most automation fails on selection, not implementation. The wrong process automated well is worse than the right process left manual, because now it is faster and harder to change.
What is worth automating
Score each candidate on four dimensions and require a real answer to each:
- Frequency — how often, honestly measured. Weekly is usually the floor.
- Time cost — per occurrence, times frequency. Most "huge time sinks" are twenty minutes a week.
- Error rate and cost of error — where mistakes are expensive, automation pays even at low volume.
- Stability — how often the process itself changes. A process that changes monthly will break monthly.
Then apply the disqualifiers. Do not automate a process that:
- Nobody has documented. Automating an unexamined process encodes its accidents permanently.
- Requires judgment you cannot specify. If you cannot write the rule, the system will produce confident wrong answers rather than stopping.
- Fails silently. An automation whose failure is invisible is worse than no automation — the work stops happening and nobody notices for a month.
- Should be eliminated instead. The best automation is deleting the step. Ask this before designing anything.
Designing the system
Start with the smallest loop that delivers value end to end, not the full vision. Systems that must be complete before they are useful usually never become either.
- Deterministic where you can, model-driven where you must. Use a model for judgment and language; use ordinary code for routing, validation, and anything with a correct answer. Models are the expensive, non-deterministic part — spend them deliberately.
- Put a human at the consequential step, not at every step. Approval on an irreversible action; no approval on a draft.
- Make failure loud. Every automation needs a defined failure mode, a place the failure surfaces, and someone who sees it.
- Idempotence matters more than it seems. Reruns happen. A workflow that double-sends on retry will eventually double-send.
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 c8be4a569062
- 1mo ago First seen · 96 lines · 89 tokens per session scan A a08fd9645e3f
ai-workflow-architect is a skill published in the GitHub repository cbrock84/headcount (2,080 stars, last pushed 4d ago), licensed MIT. It adds 89 tokens to every session and 1,102 once invoked, about $0.0004 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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