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 data-engineeringgit 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-engineering)<a href="https://agentmods.dev/skills/cbrock84/headcount/data-engineering"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/data-engineering/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/data-engineering"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/data-engineering.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 | $0.00067 | $0.00779 |
| Opus 5.5 | $0.00027 | $0.00312 |
| Sonnet 5.5 | $0.00013 | $0.00156 |
| Haiku 4.5 | $0.00007 | $0.00078 |
Grade A, and why
data-engineering 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data engineering
Pipelines are production systems whose failures are quiet. A broken service pages someone; a broken pipeline produces plausible numbers that people act on for a week.
This is movement and transformation. Schema and semantics belong to data-analytics:data-modeling,
policy and stewardship to data-analytics:data-governance.
Land raw, transform downstream
Keep an immutable copy of source data exactly as received. Transformation logic will be wrong at some point, and raw data is what lets you reprocess rather than re-request from a source that may no longer have it.
Business logic belongs downstream where it is visible and testable, not buried in ingestion. The
exception is transformation required for privacy — minimization, pseudonymization, dropping fields
you have no basis to hold — which belongs at ingest precisely because raw storage is what the
obligation attaches to. See legal-risk:privacy-and-data-protection.
Idempotence is the property that matters
Every pipeline will be re-run: after a failure, after a fix, after a late-arriving correction. A re-run that double-counts is worse than a failure, because it produces a wrong answer silently.
Design for exactly-once effect at the destination — deterministic keys, merges rather than blind appends, partitioned overwrites. Then re-running is safe and recovery stops being frightening.
Late, duplicate and out-of-order data
Real sources deliver all three. Decide explicitly, per pipeline: how late is an event still accepted, what happens to one arriving after its window closed, and how duplicates are identified.
Distinguish event time from processing time and partition on event time. Aggregations built on arrival time silently reassign yesterday's activity to today whenever a delivery is delayed.
Test data, not just code
Unit tests on transformation logic catch the wrong class of failure. Most damage comes from data that is valid but wrong. Assert on the data itself, in the pipeline, and fail loudly:
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 65d5030a6913
- 1mo ago Changed · +11 lines c8b63de6d3b8
- 1mo ago First seen · 60 lines · 67 tokens per session scan A f13f8f585b12
data-engineering is a skill published in the GitHub repository cbrock84/headcount (2,065 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 779 once invoked, about $0.0003 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 2026-08-30.
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