agentmods.dev

geo-infer-bayes

geo-infer-bayes is a skill for Claude Code, Codex from ActiveInferenceInstitute/GEO-INFER. It costs 56 tokens per session (1,602 once invoked), scanned A, original, no licence file.

A skill for Bayesian analysis of geospatial data. Bayesian inference updates the likelihood of possible explanations using observed data, while geospatial data describes places and locations.

In plain words
What is it for?
Use it to build hierarchical models, calculate posteriors, run variational inference, compare models, or create spatial Gaussian processes.
Why use it?
It provides methods for handling uncertainty, comparing models, and analyzing spatial relationships.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

not rated 15repo changed today A scan 56 tokens custom licence

Good fit Use it to build hierarchical models, calculate posteriors, run variational inference, compare models, or create spatial Gaussian processes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/activeinferenceinstitute/geo-infer/geo-infer-bayes
View source ↗ ActiveInferenceInstitute/GEO-INFER
Install

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.

Any agent
npx skills add ActiveInferenceInstitute/GEO-INFER --skill geo-infer-bayes
Clone the repo
git clone --depth 1 https://github.com/ActiveInferenceInstitute/GEO-INFER

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for geo-infer-bayes

README.md
[![agentmods](https://agentmods.dev/badge/skills/activeinferenceinstitute/geo-infer/geo-infer-bayes/github.svg)](https://agentmods.dev/skills/activeinferenceinstitute/geo-infer/geo-infer-bayes)
Your own site
<a href="https://agentmods.dev/skills/activeinferenceinstitute/geo-infer/geo-infer-bayes"><img src="https://agentmods.dev/badge/skills/activeinferenceinstitute/geo-infer/geo-infer-bayes/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.

agentmods 80×15 button for geo-infer-bayes

Your own site · 80×15
<a href="https://agentmods.dev/skills/activeinferenceinstitute/geo-infer/geo-infer-bayes"><img src="https://agentmods.dev/badge/skills/activeinferenceinstitute/geo-infer/geo-infer-bayes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,602 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.01602
Opus 5 $0.00028 $0.00801
Sonnet 5 $0.00011 $0.00320
Haiku 4.5 $0.00006 $0.00160

Measured today against content hash 26ba3076f25f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

geo-infer-bayes 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 today.

The scan reads SKILL.md. This mod also ships 33 executable files (examples/spatial_bayesian_analysis.py, examples/spatial_gp_example.py, setup.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

GEO-INFER-BAYES/SKILL.md · 164 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Files

What ships with it

60 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.

Changes

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.

  1. today Changed 26ba3076f25f
  2. 5d ago Changed 76d1d5c96924
  3. 7d ago First seen · 164 lines · 56 tokens per session scan A 0cfb900993b9

Subscribe to this mod's changes

geo-infer-bayes is a skill published in the GitHub repository ActiveInferenceInstitute/GEO-INFER (15 stars, last pushed today), with no licence file. It adds 56 tokens to every session and 1,602 once invoked, about $0.0003 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 2026-09-04.

Data and AIScientificactive-inferenceagent-workflowsbayesian-inferencedomain-modelinggeospatialh3
Related

Other skills, from other repositories

geoai-orchestrator

Route genuinely ambiguous or multi-stage geospatial work across specialist skills while enforcing shared CRS, validity, leakage, units, verification, and reproducibility rules. Use for requests spanning multiple stages such as acquisition, imagery, modeling, analysis, and map delivery, or for an explicit end-to-end…

muend/geoai-skills · 109 tokens

geo-deep-learning

Invoke before recommending, training, or auditing a neural method for geospatial imagery, including vision transformers, U-Net/DeepLab/SegFormer, object detection, pixel classification, building/road extraction, and EO foundation-model fine-tuning. Also invoke for neural chip-split validity, IoU/accuracy claims…

muend/geoai-skills · 104 tokens

ml-experiment-standards

Always invoke for training, validating, tuning, benchmarking, or claiming readiness of a predictive model. Covers leakage audits, spatial and grouped splits, metrics, reproducibility, and honest reporting. Invoke especially when spatial dependence, split design, or deployment geography is unknown; uncertainty is a…

muend/geoai-skills · 83 tokens

detect-objects

Run pre-trained AI models on geospatial imagery. Detect buildings, cars, ships, solar panels, agriculture fields, or use text-prompted segmentation with GroundedSAM. Requires GPU for best performance.

opengeos/geoai-skills · 45 tokens

agent-auto-sci-ai-ml

A research workflow for building and checking machine-learning and artificial-intelligence models, including models that work with maps, time-based data, text, or images.

Lzy599775/agent-auto-sci-skills · 113 tokens

kdense-ml-ai-selected

A selected toolkit of machine-learning and artificial-intelligence guidance for libraries and methods such as scikit-learn, PyTorch Lightning, Transformers, SHAP, time-series forecasting, graph learning, and UMAP.

Lzy599775/agent-auto-sci-skills · 88 tokens