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 legendtkl/agentic-skill-router --skill skill-123git clone --depth 1 https://github.com/legendtkl/agentic-skill-routerWrote 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/legendtkl/agentic-skill-router/skill-123)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-123"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-123/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/legendtkl/agentic-skill-router/skill-123"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-123.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00080 | $0.02706 |
| Opus 5.5 | $0.00032 | $0.01082 |
| Sonnet 5.5 | $0.00016 | $0.00541 |
| Haiku 4.5 | $0.00008 | $0.00271 |
Grade A, and why
skill-123 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.
This is a copy
98% identical to lab-unit-harmonization — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 259 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 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 First seen · 259 lines · 80 tokens per session scan A 2be27656c97e
skill-123 is a skill published in the GitHub repository legendtkl/agentic-skill-router (7 stars, last pushed 4mo ago), licensed MIT. It adds 80 tokens to every session and 2,706 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to lab-unit-harmonization, differing in 3 lines, and is treated as a copy.
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