PyOmniTS: A Researcher&Agent-Friendly Framework for Time Series Analysis. Train Any Model on Any Dataset

by Ladbaby · Codex Skill · ★ 106

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 58/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

benchmarkingclaude-codecodexdeep-learninggemini-cliirregular-time-seriesmissing-valuesopenclawskillstime-series

Quick Facts

Stars106
Forks15
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score58/100
Last Updated2026-10-26
Created2025-05-20
Platformsclaude-code, cli, codex, gemini, python
Est. Tokens~16k

About PyOmniTS

A Researcher&Agent-Friendly Framework for Time Series Analysis. Train Any Model on Any Dataset. 📊 Time series analysis leaderboard is now available on our 🤗 Hugging Face space. Discover the performance of different models! This is also the official repository for the following paper: Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting (ICLR 2026)...

Compatible Skills

These tools work well together with PyOmniTS for enhanced workflows:

  • agentsmithy — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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