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 performance-managementgit 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/performance-management)<a href="https://agentmods.dev/skills/cbrock84/headcount/performance-management"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/performance-management/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/performance-management"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/performance-management.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 | $0.00059 | $0.00823 |
| Opus 5.5 | $0.00024 | $0.00329 |
| Sonnet 5.5 | $0.00012 | $0.00165 |
| Haiku 4.5 | $0.00006 | $0.00082 |
Grade A, and why
performance-management 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance management
Most review systems are an expensive annual ritual that surprises nobody and improves nothing. The work happens in the ordinary week; the cycle should record it, not discover it.
Performance documentation carries legal weight, particularly around termination, discrimination and accommodation. Employment law varies by jurisdiction — involve qualified counsel before acting on sustained underperformance.
Expectations before assessment
Nobody can be fairly assessed against a standard they were not given. Expectations need to be specific to the level, written down, and shared before the period rather than produced during review as justification.
Separate two axes deliberately, because conflating them is the most common structural flaw:
- Outcomes — what was delivered, which is partly situational.
- Behaviors — how it was done, which is more within the person's control.
Someone who delivered through a collapsing market and someone who delivered by scorching the earth around them are different cases. A single blended rating hides both.
Feedback in the week, not the quarter
Feedback is useful proportional to its proximity to the event. Specific, immediate, and about the work: what happened, what the effect was, what to do differently.
The annual cycle should contain nothing new. A review that surprises someone is a reporting failure by their manager, and the surprise is the finding.
Calibration
Managers rate differently — some systematically generously, some harshly — and the differences are invisible until ratings sit side by side. Calibration exists to make comparison possible, not to fit a distribution.
Forced distributions are the failure mode here. On small teams they are statistically meaningless, and they reliably destroy exactly the collaboration the values section claims to reward.
Calibrate on evidence: what was delivered, at what level, against what expectation. A rating that cannot be defended with an example is a preference.
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 445688c6caa8
- 1mo ago First seen · 79 lines · 59 tokens per session scan A e6cf1809cb91
performance-management is a skill published in the GitHub repository cbrock84/headcount (2,077 stars, last pushed 2d ago), licensed MIT. It adds 59 tokens to every session and 823 once invoked, about $0.0002 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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