Claude Codepasses structure
Checker 0.1.0 · agent-skills-2026-08-13+claude-code-docs-2026-08-13 · 9/2/2026.claude/skills/xg-factor-analysisRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
Real Skill packageSource verifiedClawHub registry
小果量化因子分析系统助手
小果量化因子分析系统助手,作者(微信:xg_quant)基于 Alphalens 和自定义分析框架，提供因子数据清洗、IC分析、分层收益、多空组合、换手率、Fama-MacBeth回归、综合评分等完整因子评价功能。 触发关键词：因子分析、因子评价、Alphalens、IC分析、分层收益、多空组合、因子回测、因子评分、xg_alphalens、量化因子、因子有效性。
Identity and source
Publisher attributionli152registry owner unverified by skillvetai
Functional categoryAwaiting category reviewreview pending · 0% rule confidence
Package forminstruction with code24 recorded files
Canonical sourceClawHub registryclawhub:li152:xg-factor-analysis
Platform declarations
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OpenClawnative officialProvenance: registry distribution
Independent structural checks
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OpenAI Codexpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+codex-docs-2026-08-13 · 9/2/2026.agents/skills/xg-factor-analysisRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
OpenClawpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+openclaw-docs-2026-08-13 · 9/2/2026skills/xg-factor-analysisRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
Installation and inspection
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clawhub install @li152/xg-factor-analysisclawhub inspect @li152/xg-factor-analysis --version 1.0.0Security evidence
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- Status
- completed
- Coverage
- partial text content
- Files
- 23 / 24 inspected as text
- Checked
- 9/2/2026, 4:32:29 AM
- Scanner
- 0.1.3
- Policy
- 1.0.3
3 inferred permission indicators
- network access — automatically inferred
- filesystem read — automatically inferred
- filesystem write — automatically inferred
4 dependency and API indicators
- api: clawhub.ai
- api: github.com
- api: pandas.pydata.org
- api: www.apache.org
External clawhub result: clean
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- Evidence checked
- 9/2/2026, 2:49:11 AM
- Release binding
- Matches this record
- vt: clean
- skillspector: clean
- llm: clean
Recorded files
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Package content hash
sha256:4dace3cb27d4a7123e3bb15c641b702afb1042e4edd421b5f77c29d7372b3147Show up to 24 recorded paths
assets/alpha.txtassets/QBETF.txtassets/QBKZZ.txtassets/xg_factor_trader.txtassets/xg_factor.txtassets/xg_tdx_func.txtassets/因子表.jsonassets/因子计算测试.txtassets/数据分析模块.txtassets/测试.txtreferences/alphalens/__init__.pyreferences/alphalens/_version.pyreferences/alphalens/examples/event_study.txtreferences/alphalens/examples/intraday_factor.txtreferences/alphalens/examples/overview.txtreferences/alphalens/examples/pyfolio_integration.txtreferences/alphalens/performance.pyreferences/alphalens/plotting.pyreferences/alphalens/tears.pyreferences/alphalens/utils.pyreferences/xg_alphalens/xg_alphalens.pyskill-card.mdSKILL.md小果因子分析系统.txt
Source changelog
xg-factor-analysis 1.0.0 初始发布： - 全面集成量化因子分析各核心模块，包括IC分析、分层收益、多空组合、换手率、Fama-MacBeth回归、因子稳定性、综合评分等功能。 - 支持因子与价格数据的数据清洗、格式标准化和批量转换，涵盖数据准备全流程。 - 自动生成多种可视化图表（IC分布图、收益热力图等），并输出28项指标的综合因子评分及优化建议。 - 提供详细分步教程、依赖环境配置和常见问题解答，便于用户快速上手。 - 适用于因子研究、模型构建、策略回测和教学演示等多种量化研究场景。