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-127git 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-127)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-127"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-127/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-127"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-127.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 | $0.00388 |
| Opus 5 | $0.00014 | $0.00194 |
| Sonnet 5 | $0.00005 | $0.00078 |
| Haiku 4.5 | $0.00003 | $0.00039 |
Grade A, and why
skill-127 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 Climate Analysis with GeoPandas
Overview
Climate analysis involves examining data that relates to weather patterns, temperatures, and other environmental factors across different geographic areas. This guide explains how to utilize GeoPandas to analyze climate data.
Key Concepts
Importance of Geospatial Climate Analysis
- Understanding Trends: Analyze changes in climate over time across various regions.
- Impact Assessment: Evaluate the effects of climate change on ecosystems and human activities.
Loading Climate Data
From CSV Files
import pandas as pd
# Load climate data from CSV
df_climate = pd.read_csv('climate_data.csv')
Converting to GeoDataFrame
from shapely.geometry import Point
import geopandas as gpd
# Convert climate data to GeoDataFrame
geometry = [Point(xy) for xy in zip(df_climate['longitude'], df_climate['latitude'])]
gdf_climate = gpd.GeoDataFrame(df_climate, geometry=geometry)
Analyzing Climate Trends
Visualizing Temperature Changes
import matplotlib.pyplot as plt
# Plot average temperatures over time
gdf_climate.plot(column='average_temperature', cmap='coolwarm', legend=True)
plt.title('Average Temperature Changes')
plt.show()
Correlation Analysis
Examine relationships between climate variables:
# Calculate correlation between variables
correlation = df_climate[['average_temperature', 'precipitation']].corr()
print(correlation)
Conclusion
Geospatial climate analysis using GeoPandas enables researchers to visualize and analyze climate data effectively, providing insights into trends and impacts of climate change on our planet.
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 · 63 lines · 27 tokens per session scan A a9ae1c32e347
skill-127 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 388 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…