skforecast-ai: Python time series forecasting assistant built on skforecast

by skforecast · MCP Server · ★ 52

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How to install skforecast-ai

pip install skforecast-ai
pip install "skforecast-ai[llm]"

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 68/100

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.

ai-agentsai-assistantautomlbacktesting-forecastersexplainable-aiforecastingllmmachine-learningmcpmcp-server

Quick Facts

Stars52
Forks8
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score68/100
Open Issues2
Last Updated2026-10-21
Created2026-04-29
Platformsmcp, python
Est. Tokens~18k

About skforecast-ai

[](https://ai.skforecast.org/stable/user-guides/mcp-server.html#ins

Compatible Skills

These tools work well together with skforecast-ai for enhanced workflows:

  • PyOmniTS — semantic(0.64)+complementary+rare_topics+same_lang+similar_pop+shared_platform (81%)

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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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