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-135git 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-135)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-135"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-135/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-135"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-135.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 | $0.00037 | $0.01935 |
| Opus 5.5 | $0.00015 | $0.00774 |
| Sonnet 5.5 | $0.00007 | $0.00387 |
| Haiku 4.5 | $0.00004 | $0.00194 |
Grade A, and why
skill-135 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
100% identical to geospatial-analysis — 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geospatial Analysis with GeoPandas
Overview
When working with geographic data (earthquakes, plate boundaries, etc.), using geopandas with proper coordinate projections provides accurate distance calculations and efficient spatial operations. This guide covers best practices for geospatial analysis.
Key Concepts
Geographic vs Projected Coordinate Systems
| Coordinate System | Type | Units | Use Case |
|---|---|---|---|
| EPSG:4326 (WGS84) | Geographic | Degrees (lat/lon) | Data storage, display |
| EPSG:4087 (World Equidistant Cylindrical) | Projected | Meters | Distance calculations |
Critical Rule: Never calculate distances directly in geographic coordinates (EPSG:4326). Always project to a metric coordinate system first.
Why Projection Matters
# ❌ INCORRECT: Calculating distance in EPSG:4326
# This treats degrees as if they were equal distances everywhere on Earth
gdf = gpd.GeoDataFrame(..., crs="EPSG:4326")
distance = point1.distance(point2) # Wrong! Returns degrees, not meters
# ✅ CORRECT: Project to metric CRS first
gdf_projected = gdf.to_crs("EPSG:4087")
distance_meters = point1_proj.distance(point2_proj) # Correct! Returns meters
distance_km = distance_meters / 1000.0
Loading Geospatial Data
From GeoJSON Files
import geopandas as gpd
# Load GeoJSON files directly
gdf_plates = gpd.read_file("plates.json")
gdf_boundaries = gpd.read_file("boundaries.json")
From Regular Data with Coordinates
from shapely.geometry import Point
import geopandas as gpd
# Convert coordinate data to GeoDataFrame
data = [
{"id": 1, "lat": 35.0, "lon": 140.0, "value": 5.5},
{"id": 2, "lat": 36.0, "lon": 141.0, "value": 6.0},
]
geometry = [Point(row["lon"], row["lat"]) for row in data]
gdf = gpd.GeoDataFrame(data, geometry=geometry, crs="EPSG:4326")
Spatial Filtering
Finding Points Within a Polygon
# Get the polygon of interest
target_poly = gdf_plates[gdf_plates["Name"] == "Pacific"].geometry.unary_union
# Filter points that fall within the polygon
points_inside = gdf_points[gdf_points.within(target_poly)]
print(f"Found {len(points_inside)} points inside the polygon")
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 · 225 lines · 37 tokens per session scan A 9f17e85df704
skill-135 is a skill published in the GitHub repository legendtkl/agentic-skill-router (7 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 1,935 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to geospatial-analysis, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
arboreto
Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
torchdrug
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
deepspot-m
Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.
pyhealth
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer…
nemo-mbridge-perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…