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 skills/cbrock84/headcount/data-governancenpx skills add cbrock84/headcount --skill data-governancegit 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/data-governance)<a href="https://agentmods.dev/skills/cbrock84/headcount/data-governance"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/data-governance.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 | $0.00064 | $0.00643 |
| Opus 5 | $0.00032 | $0.00321 |
| Sonnet 5 | $0.00013 | $0.00129 |
| Haiku 4.5 | $0.00006 | $0.00064 |
Grade A, and why
data-governance 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 5d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data governance
Governance has a reputation for bureaucracy because it is usually implemented as approval queues. Done properly it is the opposite: it makes data usable without asking anyone.
Start with definitions, not policy
The highest-value governance artifact is a metric dictionary. For each business metric:
- The plain-language definition — what it counts, and what it deliberately excludes.
- The computation, unambiguously: source table, filters, time grain, timezone.
- The owner — a person who decides when it is disputed.
- Known caveats — when it is misleading, and what changed historically.
Most metric disputes dissolve once both parties read the same definition and discover they were measuring different things. Almost none require a policy.
Watch the ones that look obvious. "Active customer," "revenue," and "signup" each have half a dozen defensible definitions, and the ambiguity surfaces at the worst moment.
Ownership
Every dataset has a named owner accountable for its quality and access — a person, not a team. Unowned datasets decay, and nobody notices until a decision is made on stale data.
The owner should sit with the business meaning, not with the pipeline. The team that generates the data understands what it means; the platform team understands how it moves.
Quality, measured rather than asserted
Test data like code, continuously, and alert on failures:
- Freshness — did it arrive when expected?
- Volume — is the row count within its normal range? A silent drop to zero is the classic failure.
- Uniqueness and nullity on key fields.
- Referential integrity across joins.
- Distribution — has the shape shifted in a way nothing explains?
The point is finding breakage before a decision is made on it. A pipeline that fails loudly is better than one that silently produces yesterday's numbers.
Access
Default to open for internal, non-personal data. Restrictive-by-default drives the shadow spreadsheet layer, which is genuinely less safe than a governed warehouse.
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.
- 5d ago First seen · 67 lines · 64 tokens per session scan A 7b697d19f9f0
data-governance is a skill published in the GitHub repository cbrock84/headcount (1,176 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 643 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 2026-08-30.
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