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 agentmods add agents/cbrock84/headcount/repo-metagit 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/agents/cbrock84/headcount/repo-meta)<a href="https://agentmods.dev/agents/cbrock84/headcount/repo-meta"><img src="https://agentmods.dev/badge/agents/cbrock84/headcount/repo-meta.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00033 | $0.00360 |
| Opus 5 | $0.00016 | $0.00180 |
| Sonnet 5 | $0.00007 | $0.00072 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
repo-meta 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.
What it actually says
Repository meta
Why this agent exists
Owns the parts of the repository that describe or verify it rather than being skills: the docs, the manifests, the checks, and the CI that runs them. Kept separate from the departments so a documentation change and a skill change never contend for the same surface.
Surface
Writes: docs/**, scripts/**, .github/**, .claude/**, .claude-plugin/**, README.md.
Reads: anything. Commits: nothing.
Standard
docs/DECISION-LOG.mdfollows the convention inexecutive:agent-hierarchy: numbers assigned when a question is raised, never reused, every entry carrying lettered options and an explicit recommendation.docs/AGENT-SURFACES.mdand this directory must agree — a roster row marked installed needs a charter, and a charter needs a row.- The marketplace manifest lists every department and no department that does not exist.
Verification this surface implies
node plugins/executive/skills/agent-hierarchy/scripts/agent-guard.mjs checkpasses.- Both scripts in
scripts/pass. - Every manifest parses as JSON.
Return contract
- What changed, by file.
- Why.
- What was verified, with output.
- Anything left undone.
- Any decision this raises — assign it the next D-number in the log rather than leaving it in prose.
- Open questions for the orchestrator.
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 · 42 lines · 33 tokens per session scan A cd4a0c3b710b
repo-meta is an agent published in the GitHub repository cbrock84/headcount (1,284 stars, last pushed 3d ago), licensed MIT. It adds 33 tokens to every session and 360 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
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stakeholder-researcher
Research one stakeholder from the public record when no LinkedIn PDF is available, for a high-stakes meeting brief. Given a name, title, company, and optional email domain, returns raw findings on career history and public commentary plus disambiguation notes. Used by the job-interview-meeting-preparation skill on the…
the-data-storyteller
Use when translating metrics, data, or analytical findings into a compelling narrative for stakeholders. Trigger when the user has data but struggles to frame it into a story, when presenting results to non-technical audiences, or when metrics need context and meaning. Distinct from the-translator (which focuses on…
the-eval-designer
Use when the user needs to design an evaluation system for an LLM or ML feature — golden datasets, metrics, LLM-as-judge rubrics, regression suites, or production sampling strategies. Trigger when the question is "how do I measure if this is good?" or when shipping an AI feature without a clear eval in place.
the-scientist
Use for pre-development feasibility and prototyping of an AI feature. Trigger when the user wants to validate whether an LLM or ML approach actually works before committing engineering resources — building rapid prototypes, golden datasets, or eval baselines.