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-137git 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-137)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-137"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-137/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-137"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-137.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 | $0.00038 | $0.00629 |
| Opus 5 | $0.00019 | $0.00315 |
| Sonnet 5 | $0.00008 | $0.00126 |
| Haiku 4.5 | $0.00004 | $0.00063 |
Grade A, and why
skill-137 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Cleaning and Preparation
Overview
Data Cleaning and Preparation is an essential step in the data analysis process that applies to any dataset, regardless of its source or type. Properly cleaned data is critical for generating reliable insights and driving informed decisions across various fields, including healthcare, finance, and marketing.
This skill covers:
- Missing Value Handling: Strategies to address incomplete data entries.
- Outlier Detection: Techniques to identify and manage anomalies in datasets.
- Data Transformation: Methods for converting data into suitable formats for analysis.
- Normalization and Standardization: General approaches to standardize data across different formats and scales.
When to Use This Skill
Use this skill when:
- You are required to clean any dataset prior to analysis.
- You encounter datasets with missing values, outliers, or inconsistencies.
- Preparing data from multiple sources for integration and reporting.
- You need to ensure data quality before conducting any statistical analysis.
Key Data Cleaning Techniques
1. Handling Missing Values
Missing values can significantly impact data quality. Common strategies include:
- Removing Rows: Eliminate entries with excessive missing values.
- Imputation: Replace missing values with the mean, median, or mode, or use advanced methods like KNN or regression.
2. Outlier Detection
Outliers can skew data analysis. Techniques include:
- Z-Score Method: Identify outliers by measuring how far away from the mean a data point is.
- IQR Method: Use the interquartile range to detect outliers based on the distribution of the data.
3. Data Transformation
Data may require transformation for proper analysis:
- Log Transformation: Apply logarithmic transformation for positively skewed data.
- Normalization: Scale data to fit within a specific range, commonly between 0 and 1.
4. Normalization and Standardization
Use common practices to standardize data:
- Min-Max Normalization: Scale the data based on its minimum and maximum values.
- Z-Score Normalization: Transform data into a standardized format with a mean of 0 and a standard deviation of 1.
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 · 65 lines · 38 tokens per session scan A 5883c7fb9c34
skill-137 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 629 once invoked, about $0.0002 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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