무료GitHub에 공개

geoai

Route GeoAI geospatial data, inference, training, foundation-model, and integration workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill

설치 방법 보기

포함된 파일(34)

SKILL.md4.0 KB
references/installation-and-environment.md2.7 KB
references/repo-provenance.md1.7 KB
references/repo-routing-metadata.json482 B
references/top-level-api-map.md4.4 KB
references/troubleshooting.md4.4 KB
scripts/check_geoai_env.py6.7 KB
sub-skills/detection-segmentation-inference/references/api-reference.md14.0 KB
sub-skills/detection-segmentation-inference/references/inference-workflows.md7.9 KB
sub-skills/detection-segmentation-inference/references/troubleshooting.md5.4 KB
sub-skills/detection-segmentation-inference/scripts/inference_input_check.py18.4 KB
sub-skills/detection-segmentation-inference/SKILL.md2.4 KB
sub-skills/foundation-models-embeddings-vlms/references/api-reference.md15.3 KB
sub-skills/foundation-models-embeddings-vlms/references/model-and-embedding-workflows.md11.4 KB
sub-skills/foundation-models-embeddings-vlms/references/troubleshooting.md5.6 KB
sub-skills/foundation-models-embeddings-vlms/scripts/list_geoai_models.py9.8 KB
sub-skills/foundation-models-embeddings-vlms/SKILL.md1.9 KB
sub-skills/geospatial-data-pipelines/references/api-reference.md18.7 KB
sub-skills/geospatial-data-pipelines/references/data-and-pipeline-workflows.md10.0 KB
sub-skills/geospatial-data-pipelines/references/troubleshooting.md10.1 KB
sub-skills/geospatial-data-pipelines/scripts/geospatial_io_smoke.py9.6 KB
sub-skills/geospatial-data-pipelines/scripts/validate_pipeline_config.py9.3 KB
sub-skills/geospatial-data-pipelines/SKILL.md4.0 KB
sub-skills/integrations-agents-qgis-mcp/references/api-reference.md7.4 KB
sub-skills/integrations-agents-qgis-mcp/references/qgis-plugin-and-mcp.md5.1 KB
sub-skills/integrations-agents-qgis-mcp/references/troubleshooting.md6.2 KB
sub-skills/integrations-agents-qgis-mcp/scripts/mcp_config_check.py17.8 KB
sub-skills/integrations-agents-qgis-mcp/scripts/qgis_dependency_probe.py9.2 KB
sub-skills/integrations-agents-qgis-mcp/SKILL.md3.2 KB
sub-skills/training-and-finetuning/references/api-reference.md22.0 KB
sub-skills/training-and-finetuning/references/training-recipes.md11.7 KB
sub-skills/training-and-finetuning/references/troubleshooting.md9.9 KB
sub-skills/training-and-finetuning/scripts/check_training_layout.py25.0 KB
sub-skills/training-and-finetuning/SKILL.md2.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

GeoAI

Use this repo skill when the task is about GeoAI: geospatial data inspection and downloads, raster/vector conversion, batch pipelines, model inference, training and fine-tuning, foundation models and embeddings, or the QGIS/MCP/agent integration surfaces.

GeoAI has a broad package surface. Start with the smallest sub-skill that matches the user's intent, then read the linked references for the exact APIs, commands, and failure modes.

First steps

Read installation and environment to choose the right install path and optional extras.
Run scripts/check_geoai_env.py for a safe import/CLI/backend smoke check when you need to confirm the environment before deeper work.
Read top-level API map when you need to see which module family owns a symbol.
Read troubleshooting for cross-cutting install/import/CLI/backend issues.
Read repository provenance if you need to confirm whether this skill still matches the checkout you have.

Route by task

User request	Read next
Inspect raster/vector files, validate CRS/bounds, download STAC/NAIP/Overture data, run `geoai pipeline`, tile or vectorize data	Geospatial data pipelines
Run segmentation, detection, prompt-based segmentation, RF-DETR, water/cloud/super-resolution, ONNX, or HF-style inference	Detection and segmentation inference
Prepare training layouts, fit/fine-tune models, inspect losses or metrics, or publish checkpoints carefully	Training and finetuning
Query the foundation-model registry, work with embeddings, DINOv3, Prithvi, UniverSat, TESSERA, Moondream, vLLM, or captioning	Foundation models, embeddings, and VLMs
Configure the QGIS plugin, the GeoAI MCP server, or optional Strands agent integrations	GeoAI integrations, agents, QGIS, and MCP

Package identity

Distribution name: geoai-py
Import name: geoai
Console entry point: geoai
Public Python support: Python 3.12 or newer

Safe install pattern

For normal use, install the published package: pip install geoai-py.
When working from a local checkout, an editable install is reasonable: pip install -e ..
Install optional extras only when the task needs them. The main extras currently include agents, building, networks, onnx, osd, rfdetr, sr, terratorch, and vllm.
Do not install optional extras just because they exist; route to the sub-skill that owns the workflow first.

Minimal smoke check

A quick package smoke check should be enough before deeper work:

python -c "import geoai; print(geoai.__version__)"
python -m geoai.cli --help

If the user needs backend confirmation for GPU workflows, use the bundled smoke script rather than jumping straight to a heavy model run.

What this root skill does not do

It does not perform downstream geospatial analysis or model execution by itself.
It does not tell future agents to open files from the original repository checkout when a bundled skill reference or script exists.
It does not bundle training, inference, or integration details that belong in the sub-skills.

Follow-up references

Use repository provenance to check staleness before refresh.
Use top-level API map to trace a public symbol to the owning sub-skill.
Use troubleshooting for cross-cutting error recovery before reading the more specialized sub-skill troubleshooting file.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

같은 저장소의 스킬

3d-resnets-pytorch

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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa

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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa-v2

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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

ab3dmot

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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

accelerate

무료

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

acme

무료

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

VectorSpaceLab의 스킬 모두 보기

이 스킬의 문제 신고

설치

명령으로 설치

npx skills add https://github.com/VectorSpaceLab/AREX-Skill --skill geoai

터미널에 붙여 넣고 실행합니다(Node.js 필요). Claude Code, Codex 등 설치할 에이전트를 고를 수 있습니다.

에이전트에게 부탁하기

다음 Agent Skill을 설치해 주세요.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/geoai

먼저 SKILL.md와 첨부 파일의 내용을 확인해 수상한 처리(외부 전송, 인증 정보 읽기 등)가 없는지 확인한 뒤 Claude Code는 ~/.claude/skills/geoai/, Codex는 ~/.agents/skills/geoai/ 에 배치해 주세요.

이 문장을 그대로 Claude Code나 Codex에 보내면 내용을 확인한 뒤 설치해 줍니다.

GitHub에서 소스 보기

저장소: VectorSpaceLab/AREX-Skill
스타: 339
포크: 26
카테고리: 데이터·AI
라이선스: Apache-2.0
원문 언어: 영어
최종 업데이트: 2026년 9월 3일

스킬은 에이전트를 위한 지시와 스크립트의 묶음입니다. cccskills는 내용의 안전성을 보증하지 않습니다. 설치하기 전에 내용을 확인하세요.