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Microsoft Qlib
by
sawyerieong-cpuAI
· GitHub ↗
· v1.0.0
· MIT-0
133
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0
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0
Active Installs
1
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Install in OpenClaw
/install qlib-ms
Description
Microsoft Qlib - AI-oriented Quantitative Investment Platform. Use when: (1) stock/financial data analysis, (2) quantitative trading strategy development, (3...
Usage Guidance
This SKILL appears to be a plain Qlib usage guide and pip-based install, which is coherent with its description — but two things to check before installing: (1) provenance: the registry entry has no source/homepage while the skill claims 'Microsoft Qlib' — confirm the publisher is trustworthy and that you are installing the official qlib package (compare with the official GitHub and PyPI entries). (2) pip risk: pip install will download and run code from PyPI; install into an isolated virtualenv or container and review the package/version. Finally, be aware that real data access may require separate data-provider API keys or configurations handled outside this skill.
Capability Analysis
Type: OpenClaw Skill
Name: qlib-ms
Version: 1.0.0
The skill bundle provides integration for Microsoft Qlib, a legitimate open-source quantitative investment platform. The SKILL.md file contains standard documentation, installation instructions via pip, and code snippets for financial data analysis that align perfectly with the stated purpose. No indicators of malicious intent, data exfiltration, or prompt injection were found.
Capability Assessment
Purpose & Capability
The skill is coherent with a Qlib integration: it only requires Python and shows typical qlib usage (pip install qlib, qlib.init(), data fetching, backtesting). However, the registry lists the skill source as 'unknown' and provides no homepage while the SKILL.md claims Microsoft Qlib — this provenance mismatch could indicate impersonation or an untrusted publisher.
Instruction Scope
SKILL.md instructions are scoped to installing qlib and using its API (init, data access, backtest, model training). It does not instruct reading unrelated files, harvesting env vars, or sending data to unexpected endpoints.
Install Mechanism
Installation is via pip install qlib (declared in SKILL.md metadata). Pip installation from PyPI is expected for this package but carries the usual risk of executing third-party code. Also note a minor inconsistency: registry metadata said 'No install spec' while SKILL.md contains an openclaw.install entry — this mismatch should be reconciled.
Credentials
No environment variables or credentials are requested by the skill. This is proportionate for an instruction-only qlib integration. Be aware that some data sources used with qlib may require separate API keys which are not requested here.
Persistence & Privilege
The skill is not force-enabled (always:false) and is user-invocable. It does not request elevated platform privileges or persistent presence.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install qlib-ms - After installation, invoke the skill by name or use
/qlib-ms - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release - Microsoft Qlib AI-oriented quantitative investment platform
Metadata
Frequently Asked Questions
What is Microsoft Qlib?
Microsoft Qlib - AI-oriented Quantitative Investment Platform. Use when: (1) stock/financial data analysis, (2) quantitative trading strategy development, (3... It is an AI Agent Skill for Claude Code / OpenClaw, with 133 downloads so far.
How do I install Microsoft Qlib?
Run "/install qlib-ms" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Microsoft Qlib free?
Yes, Microsoft Qlib is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Microsoft Qlib support?
Microsoft Qlib is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Microsoft Qlib?
It is built and maintained by sawyerieong-cpuAI (@sawyerieong-cpuai); the current version is v1.0.0.
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