Skill audit report

Atmquant audit report.

atmquant由公众号“堂主的ATMQuant"开发，是基于vnpy框架的AI量化交易平台，专注于AI量化投资、指标信号可视化与策略研发和回测，有完整教学和实战案例，适合量化交易初学者、金融从业者、程序员、投资爱好者

REVIEWED · REVIEWNeeds reviewGenerated Jul 3, 2026Heuristic metadata audit

Audit

Trust

Quality

Security

Maintain

Install

OpenAgentSkill Trust Score

Strong shortlist

Stars, maintenance, license, docs, install safety, permission surface, and installability.

The Trust Score is OpenAgentSkill's adoption layer. It is designed to help an agent decide whether a skill is safe enough to shortlist before installation.

GitHub adoption

WARN

96 GitHub stars

Stars/forks activity

WARN

96 stars, 44 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

3mo since push

License clarity

WARN

Unknown

README/SKILL.md completeness

PASS

Metadata includes enough usage and workflow context

Dependency/runtime risk

PASS

no major dependency risk hints in public metadata

Install availability

PASS

npx skills add seasonstar/atmquant

Install command safety

PASS

standard package or runtime install path

Permission surface

PASS

filesystem or document access

Repository evidence

PASS

https://github.com/seasonstar/atmquant

Review status

PASS

AI review data available

Agent Proven outcomes

INFO

No agent outcome data yet

Checks

Install and adoption review

8 passed · 7 review

Install path

PASS

npx skills add seasonstar/atmquant

Repository

PASS

https://github.com/seasonstar/atmquant

License

CHECK

Unknown

Maintenance

PASS

3mo since push

AI review

PASS

Approved with no listed issues

README/SKILL.md completeness

PASS

Usable description available

Dependency risk

PASS

no major dependency risk hints in public metadata

Install command safety

PASS

standard package or runtime install path

Permission surface

PASS

filesystem or document access

Stars/forks activity

FIX

96 stars, 44 forks; issue activity unavailable in current metadata

Adoption

INFO

96 GitHub stars

Warnings

License is unclear
Quality score needs review
GitHub adoption: 96 GitHub stars
Stars/forks activity: 96 stars, 44 forks; issue activity unavailable in current metadata
License clarity: Unknown

Method

This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.

Install path

Review the report before installing into production agents.

$ npx skills add seasonstar/atmquant

Agent-readable metadata

Machine decision packet

Embedded JSON exposes suited tasks, suited agents, install command, trust score, safety gate, alternatives, and do-not-use conditions for agent review.

Install policy: review
Best agents: Python + Quant
Alternatives: 3

Open agent JSON

Signals

Audit score: 77/100
Quality: 66/100
Trust: 75/100
Install safety: 92/100
Permission surface: 86/100
Maintenance: 3mo since push
Events: No events yet
GitHub stars: 96
Last push: Apr 10, 2026

Agent safety v2

61/100 · Review before install

Reviewed with permission notesreview

Usable candidate, but the agent should surface permission and audit notes before installation.

Require human approval before installing into a real workspace.

License is unclear
61/100 agent safety score

Network access

medium

Skill likely fetches remote pages, APIs, repositories, or external services.

Filesystem access

medium

Skill may read or write project files, documents, generated artifacts, or local workspace state.

License is unclear

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Atmquant audit report.

Stars, maintenance, license, docs, install safety, permission surface, and installability.

Install and adoption review

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