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 tuanductran/hr-skills --skill hr-datagit clone --depth 1 https://github.com/tuanductran/hr-skillsWrote 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/tuanductran/hr-skills/hr-data)<a href="https://agentmods.dev/skills/tuanductran/hr-skills/hr-data"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-data/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/tuanductran/hr-skills/hr-data"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-data.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 | $0.00086 | $0.02246 |
| Opus 5 | $0.00043 | $0.01123 |
| Sonnet 5 | $0.00017 | $0.00449 |
| Haiku 4.5 | $0.00009 | $0.00225 |
Grade A, and why
hr-data 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 7d 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 — 388 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HR data engineering hiring
Comprehensive Data and Analytics knowledge for HR and recruiters — from understanding modern data ecosystems and AI workflows to evaluating data candidates, interpreting portfolios, and improving technical hiring decisions.
Supported tasks
- Explaining data and analytics concepts for non-technical recruiters
- Understanding modern data ecosystems and AI workflows
- Screening Data Engineers, Data Analysts, and Data Scientists effectively
- Evaluating data portfolios, dashboards, Kaggle projects, and GitHub repositories
- Creating data interview questions and hiring scorecards
- Comparing Data Engineering, Analytics, BI, Data Science, and Machine Learning roles
- Understanding data pipelines, warehousing, and analytics workflows
- Identifying data seniority levels and skill expectations
- Understanding AI, machine learning, and modern data infrastructure
- Writing data-related job descriptions and hiring requirements
- Explaining data terminology used by engineers and analysts
- Understanding collaboration between data, product, engineering, business, and leadership teams
What data engineering and analytics mean in 2026
Modern data work is no longer:
- "just making reports"
- "only writing SQL queries"
- "just training AI models"
In 2026, modern data ecosystems increasingly include:
- cloud-native data platforms
- real-time analytics
- machine learning infrastructure
- AI engineering
- vector databases
- data governance
- observability
- analytics engineering
- large language model (LLM) workflows
- business intelligence automation
Modern data teams are increasingly expected to support:
- product decisions
- AI systems
- forecasting
- automation
- experimentation
- operational analytics
- executive reporting
AI-driven analytics and LLM-integrated workflows continue reshaping modern data teams.
Data ecosystem (2026)
Data processing and analytics
- SQL
- Python
- R
- Pandas
- Polars
Data engineering and pipelines
What ships with it
3 files 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.
- 7d ago First seen · 388 lines · 86 tokens per session scan A c9b7de2b5859
hr-data is a skill published in the GitHub repository tuanductran/hr-skills (57 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 2,246 once invoked, about $0.0004 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 2026-09-03.
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