SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill lab-unit-harmonizationgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/lab-unit-harmonization)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/lab-unit-harmonization"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/lab-unit-harmonization.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 | $0.00082 | $0.02709 |
| Opus 5 | $0.00041 | $0.01354 |
| Sonnet 5 | $0.00016 | $0.00542 |
| Haiku 4.5 | $0.00008 | $0.00271 |
Grade A, and why
lab-unit-harmonization 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 3d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- lab-unit-harmonization — 100% identical, 0 lines differ
- skill-123 — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lab Unit Harmonization
Overview
Lab Unit Harmonization provides techniques and references for standardizing clinical laboratory data from multiple sources. Real-world healthcare data often contains measurements in different units, varying decimal and numeric formats, and data entry inconsistencies that must be resolved before analysis.
This skill covers:
- Unit Conversion: Converting between US conventional and SI units
- Format Standardization: Handling scientific notation, decimal formats, whitespace
- Data Quality Assessment: Identifying and quantifying data issues
- CKD-Specific Labs: Complete reference for chronic kidney disease-related lab features
When to Use This Skill
Use this skill when:
- Harmonizing lab values from multiple hospitals or health systems
- Converting between US conventional and SI units (e.g., mg/dL to µmol/L)
- Merging data from EHRs using different default unit conventions
- Integrating international datasets with mixed unit systems
- Standardizing inconsistent numeric formats (scientific notation, decimals)
- Cleaning whitespace, thousand separators, or European decimal formats
- Validating lab values against expected clinical ranges
- Preparing CKD lab panels for eGFR calculations or staging models
- Building ETL pipelines for clinical data warehouses
- Preprocessing lab data for machine learning models
Data Quality Issues Reference
Real-world clinical lab data contains multiple types of quality issues. The following table summarizes common issues and their typical prevalence in multi-source datasets:
| Issue Type | Description | Typical Prevalence | Example |
|---|---|---|---|
| Incomplete Records | Rows with excessive missing values | 1-5% | Patient record with only 3/62 labs measured |
| Mixed Units | Same analyte reported in different units | 20-40% | Creatinine: mg/dL vs µmol/L |
| Scientific Notation | Large/small values in exponential format | 15-30% | 1.5e3 instead of 1500 |
| Thousand Separators | Commas in large numbers | 10-25% | 1,234.5 vs 1234.5 |
| European Decimals | Comma as decimal separator | 10-20% | 12,5 instead of 12.5 |
| Whitespace Issues | Leading/trailing spaces, tabs | 15-25% | 45.2 vs 45.2 |
| Missing Values | Empty, NULL, or sentinel values | Variable | NaN, -999, blank |
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.
- 3d ago First seen · 260 lines · 82 tokens per session scan A 60eea92e906b
lab-unit-harmonization is a skill published in the GitHub repository benchflow-ai/skillsbench (1,747 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 82 tokens to every session and 2,709 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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