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-098git 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-098)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-098"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-098/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-098"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-098.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 | $0.00027 | $0.00344 |
| Opus 5 | $0.00014 | $0.00172 |
| Sonnet 5 | $0.00005 | $0.00069 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
skill-098 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.
What it actually says
Geospatial Data Processing
Overview
Geospatial data processing involves various techniques to analyze, transform, and visualize geographic information from multiple sources. This guide provides an overview of general methods applicable across many scenarios.
Key Concepts
Types of Geospatial Data
- Vector Data: Points, lines, and polygons representing features.
- Raster Data: Gridded data representing continuous phenomena (e.g., satellite imagery).
Common Geospatial Operations
- Loading Data: Import data from various formats including GeoJSON, Shapefiles, and CSV.
- Transforming Data: Apply transformations to change coordinate systems and formats.
- Visualizing Data: Use various libraries to create visual representations of geospatial information.
Data Loading Techniques
Generic Data Loading
import geopandas as gpd
# Load data from a file (generic)
gdf = gpd.read_file('data_file')
Data Transformation
Coordinate System Transformation
# Transform to a different coordinate system
new_gdf = gdf.to_crs('EPSG:3857')
Visualization Techniques
Basic Visualization
# Plot the GeoDataFrame
gdf.plot()
Summary of Methods
This document has outlined various techniques for processing geospatial data, including loading, transforming, and visualizing. While each method may require specific libraries and approaches, the general principles remain the same across different datasets and applications.
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 · 54 lines · 27 tokens per session scan A bc38a7f0bacc
skill-098 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 344 once invoked, about $0.0001 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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