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-092git 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-092)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-092"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-092/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-092"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-092.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 | $0.00025 | $0.00418 |
| Opus 5 | $0.00013 | $0.00209 |
| Sonnet 5 | $0.00005 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
skill-092 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
Fault Line Analysis with GeoPandas
Overview
Understanding fault lines is crucial for assessing seismic hazards and predicting earthquake behavior. This guide focuses on analyzing fault lines using geospatial data to comprehend their characteristics and potential impacts.
Key Concepts
Types of Fault Lines
- Normal Faults: Caused by extensional forces where the hanging wall moves down.
- Reverse Faults: Caused by compressional forces where the hanging wall moves up.
- Strike-slip Faults: Lateral movement along the fault plane.
Loading Fault Line Data
From Shapefiles
import geopandas as gpd
# Load fault lines from a shapefile
gdf_faults = gpd.read_file('fault_lines.shp')
Visualizing Fault Lines
import matplotlib.pyplot as plt
# Plot the fault lines
gdf_faults.plot(color='red', linewidth=1)
plt.title('Fault Lines Visualization')
plt.show()
Analyzing Fault Properties
Length and Orientation
# Calculate the length of each fault line
gdf_faults['length'] = gdf_faults.geometry.length
# Calculate the orientation of each fault line
gdf_faults['orientation'] = gdf_faults.geometry.angle
Spatial Relationships
Proximity to Urban Areas
Identify urban areas near fault lines to assess risk.
# Load urban area data
gdf_urban = gpd.read_file('urban_areas.shp')
# Calculate distances to urban areas
gdf_faults['nearest_urban_distance'] = gdf_faults.geometry.distance(gdf_urban.unary_union)
Conclusion
Fault line analysis is essential for understanding seismic hazards. By leveraging geospatial data, we can evaluate fault characteristics and their potential impact on surrounding areas.
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 · 70 lines · 25 tokens per session scan A 6ebe15abbb8f
skill-092 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 25 tokens to every session and 418 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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