Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill geomastergit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/geomaster)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/geomaster"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/geomaster/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/foryourhealth111-pixel/vibe-skills/geomaster"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/geomaster.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00132 | $0.05345 |
| Opus 5 | $0.00066 | $0.02672 |
| Sonnet 5 | $0.00026 | $0.01069 |
| Haiku 4.5 | $0.00013 | $0.00534 |
Grade A, and why
geomaster 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 9d 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 — 682 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GeoMaster
GeoMaster is a comprehensive geospatial science skill covering the full spectrum of geographic information systems, remote sensing, spatial analysis, and machine learning for Earth observation. This skill provides expert knowledge across 70+ topics with 500+ code examples in 7 programming languages.
Installation
Core Python Geospatial Stack
# Install via conda (recommended for geospatial dependencies)
conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas
# Or via uv
uv pip install geopandas rasterio fiona shapely pyproj
Remote Sensing & Image Processing
# Core remote sensing libraries
uv pip install rsgislib torchgeo eo-learn
# For Google Earth Engine
uv pip install earthengine-api
# For SNAP integration
# Download from: https://step.esa.int/main/download/
GIS Software Integration
# QGIS Python bindings (usually installed with QGIS)
# ArcPy requires ArcGIS Pro installation
# GRASS GIS
conda install -c conda-forge grassgrass
# SAGA GIS
conda install -c conda-forge saga-gis
Machine Learning for Geospatial
# Deep learning for remote sensing
uv pip install torch-geometric tensorflow-caney
# Spatial machine learning
uv pip install libpysal esda mgwr
uv pip install scikit-learn xgboost lightgbm
Point Cloud & 3D
# LiDAR processing
uv pip install laspy pylas
# Point cloud manipulation
uv pip install open3d pdal
# Photogrammetry
uv pip install opendm
Network & Routing
# Street network analysis
uv pip install osmnx networkx
# Routing engines
uv pip install osrm pyrouting
Visualization
# Static mapping
uv pip install cartopy contextily mapclassify
# Interactive web maps
uv pip install folium ipyleaflet keplergl
# 3D visualization
uv pip install pydeck pythreejs
Big Data & Cloud
# Distributed geospatial processing
uv pip install dask-geopandas
# Xarray for multidimensional arrays
uv pip install xarray rioxarray
# Planetary Computer
uv pip install pystac-client planetary-computer
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- README.md 3.1 KB
- references/advanced-gis.md 10 KB
- references/big-data.md 8.3 KB
- references/code-examples.md 12 KB
- references/core-libraries.md 6.4 KB
- references/data-sources.md 8.8 KB
- references/gis-software.md 8.7 KB
- references/industry-applications.md 13 KB
- references/machine-learning.md 12 KB
- references/programming-languages.md 9.1 KB
- references/remote-sensing.md 9.9 KB
- references/scientific-domains.md 10 KB
- references/specialized-topics.md 10 KB
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.
- 9d ago First seen · 682 lines · 132 tokens per session scan A 714f9b274da2
geomaster is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 132 tokens to every session and 5,345 once invoked, about $0.0007 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
data-scientist
Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.
statistical-analyst
Applied statistics for business and product questions — test selection, assumption checks, power planning, effect sizes with intervals, multiplicity correction. Use when interpreting an experiment, sizing a study, or vetting a claim.
jupyter-live-kernel
Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a…
neuroskill-bci
Connect to a running NeuroSkill instance and incorporate the user's real-time cognitive and emotional state (focus, relaxation, mood, cognitive load, drowsiness, heart rate, HRV, sleep staging, and 40+ derived EXG scores) into responses. Requires a BCI wearable (Muse 2/S or OpenBCI) and the NeuroSkill desktop app…
sparse-autoencoder-training
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
research-paper-writing
End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.