PyOmniTS Usage Guide For Time Series Analysis

Provides guidance on using PyOmniTS for time series analysis, including docs, key concepts, code patterns, pitfalls, and replicating models, datasets, and lo...

Security Scan

The skill is internally consistent with its stated purpose (guidance for using and adapting PyOmniTS) but its runtime instructions assume direct filesystem modifications and copying of external code, so users should vet and sandbox any code they pull in before applying changes to their projects.

Purpose & Capability

Name/description (PyOmniTS usage and replication guidance) match the instructions: links to docs, code structure explanations, and stepwise guidance for adapting models/datasets/losses into a local PyOmniTS installation are expected and coherent.

Instruction Scope

The SKILL.md explicitly instructs the agent to clone external repositories and to copy files into user paths under ${PYOMNITS_PATH} (using cp) and to modify launch scripts. Those operations are within the stated purpose (adapting external code into PyOmniTS) but they involve network retrieval of third-party code and direct filesystem writes. The instructions do not instruct the agent to vet, sandbox, or back up files before copying — this is a safety concern to be aware of but not an incoherence with the skill's purpose.

Install Mechanism

No install spec or downloads are declared; the skill is instruction-only and relies on standard developer tools (git, cp). This is proportionate to a documentation/advice skill.

Credentials

The skill requests no environment variables, credentials, or config paths beyond a user-supplied ${PYOMNITS_PATH}. There are no disproportionate credential requests.

Persistence & Privilege

always is false and the skill is user-invocable. It does advocate modifying project files on disk, but it does not request persistent platform privileges or modify other skills or agent-wide configuration.

Assessment

This skill is a coherent how-to for adapting code into a local PyOmniTS installation, but it assumes you or the agent will clone repositories and copy files into your project. Before using it: (1) confirm the correct ${PYOMNITS_PATH} and back up the target project directories; (2) inspect any external repository code you clone (read source, check tests, review license) before copying it into your project; (3) perform operations in an isolated environment (virtualenv/conda, container, or non-production machine); (4) prefer writing adaptor wrappers rather than overwriting core files; (5) avoid running untrusted install/run scripts as root and consider scanning new code with your security tools. If you want, provide the agent a sandbox path or upload the repo so it can suggest edits without directly modifying your main project.

📊 PyOmniTS - Time Series Analysis Framework

A unified framework for time series analysis, designed following "adaptor pattern" in software engineering to achieve training any model on any dataset using any loss function. Built by Ladbaby for researchers who want to experiment quickly without fighting with boilerplate code.

🎯 What This Skill Does

When researchers ask about PyOmniTS, this skill provides:

1. Quick links to official docs
2. Key concepts (model/dataset/loss naming conventions)
3. Code structure patterns
4. Common pitfalls and best practices

📚 Documentation & Resources

Project Home

GitHub Repository: https://github.com/Ladbaby/PyOmniTS

Getting Started

Beginner's Guide: https://github.com/Ladbaby/PyOmniTS-docs/blob/main/docs/tutorial/1_get_started.md

This tutorial covers:

• Installing dependencies (Python 3.10~3.12)
• Setting up virtual environments (conda/uv)
• Dataset preparation and preprocessing
• Running your first experiment
• Folder structure explanation

API Reference

Complete API Docs: https://github.com/Ladbaby/PyOmniTS-docs/blob/main/docs/forecasting/1_API.md

Learn how to:

• Implement custom models (class Model in models/${MODEL}.py)
• Create new datasets (class Data in data/data_provider/datasets/${DATASET}.py)
• Define loss functions (class Loss in loss_fns/${LOSS}.py)
• Understand the interface and data flow

💡 Pro Tips for Agents

• Replication workflow: PyOmniTS supports quick replication for time series models, datasets, and loss functions, if their codes are publicly available. First, ensure you have already setup PyOmniTS following the beginner's guide url above. In the following descriptions, we suppose you have downloaded PyOmniTS into ${PYOMNITS_PATH}, and you may ask the user if there's an existing PyOmniTS installation. Then, download the code repository you want to adapt (e.g., via git clone). Next, identify if the repository's main contribution is "model", "dataset", or "loss function", and be careful to distinguish the proposed methods from compared baselines, where baselines are not we needed. Also, some models, datasets, and loss functions have multiple variants, and if this is the case, you may ask the user if they want only the primary variant or all variants (primary variant can usually be inferred from training launch scripts). Choose one of the following actions based on contribution type:

  1. Models: Use cp to directly copy core folders and files containing model-related codes into ${PYOMNITS_PATH}/layers/${MODEL}.py (replace ${MODEL} with the actual model name you find). Then, create an adaptor model class under ${PYOMNITS_PATH}/models/${MODEL_NAME} to adapt the copied codes into PyOmniTS, following PyOmniTS's API definition docs mentioned above. You can read ${PYOMNITS_PATH}/models/GraFITi.py as the reference.
  2. Datsets: Use cp to directly copy core folders and files containing dataset-related codes into ${PYOMNITS_PATH}/data/dependencies/${DATASET}.py (replace ${DATASET} with the actual dataset name you find). Then, create an adaptor dataset class under ${PYOMNITS_PATH}/data/data_provider/datasets/${DATASET} to adapt the copied codes into PyOmniTS, following PyOmniTS's API definition docs mentioned above. You can read ${PYOMNITS_PATH}/data/data_provider/datasets/USHCN.py as the reference.
  3. Loss functions: Rewrite the loss function to ${PYOMNITS_PATH}/loss_fns/${LOSS}.py (replace ${LOSS} with the actual loss function name you find) following PyOmniTS's API definition docs mentioned above. You can read ${PYOMNITS_PATH}/loss_fns/MSE.py as the reference.

  Finally, tell the user they need to create or modify launch scripts under ${PYOMNITS_PATH}/scripts/ in order to run the new codes. Scripts under ${PYOMNITS_PATH}/scripts/GraFITi/ can be used as examples.