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Qc Order Forensics
作者
tltby12341
· GitHub ↗
· v1.0.0
· MIT-0
158
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install qc-order-forensics
功能描述
Forensic diagnosis engine for backtest order data — trade quality, ROI attribution, monthly cashflow, drawdown root-cause analysis, and LLM-readable reports.
安全使用建议
This skill appears to be a local Python tool that analyzes QuantConnect-style order CSVs and optional result.json. Before installing/running: 1) Run it in an isolated environment (virtualenv/container) and install pandas/numpy per requirements.txt. 2) Test on non-sensitive sample CSVs to confirm outputs; the code has some non-security bugs (e.g., cashflow sign/aggregation logic) that can affect results. 3) Review the full forensics.py file yourself (the listing provided to me was truncated) to ensure there are no unexpected network calls or hidden behavior in the unseen portion. 4) If you want to limit risk, disable autonomous invocation for this skill or require explicit user invocation. 5) Do not feed production PII or secret account files into the tool until you’ve validated the full source and outputs.
功能分析
Type: OpenClaw Skill
Name: qc-order-forensics
Version: 1.0.0
The skill bundle is a legitimate data analysis tool designed to process QuantConnect backtest results (orders.csv and result.json). The Python code in forensics.py uses standard libraries like pandas and numpy to calculate trading metrics, ROI, and drawdown periods without any evidence of data exfiltration, malicious execution, or prompt injection.
能力评估
Purpose & Capability
Name/description, SKILL.md, and forensics.py align: it reads orders.csv and optional result.json and produces diagnostics. Declared runtime (python3) and requirements (pandas, numpy) are appropriate for this purpose.
Instruction Scope
Runtime instructions and examples only reference local files (orders.csv, result.json) and producing an LLM-readable report. SKILL.md does not instruct reading unrelated system files or sending data to external endpoints.
Install Mechanism
No install spec (instruction-only) and included requirements.txt is reasonable. Because there's no automated install, the user/agent must ensure pandas/numpy are available; there is no download-from-URL or extraction behavior to review.
Credentials
No environment variables, credentials, or config paths are requested. The skill appears to operate on local CSV/JSON inputs only.
Persistence & Privilege
always:false and no special privileges requested. The skill does not ask to modify other skills or system settings. Autonomous invocation is allowed by default (platform behavior) but is not combined with other red flags.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install qc-order-forensics - 安装完成后，直接呼叫该 Skill 的名称或使用
/qc-order-forensics触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release of qc-order-forensics.
- Provides a forensic diagnosis engine for QuantConnect backtest order data.
- Generates LLM-readable reports covering trade quality, ROI attribution, monthly cashflow, and drawdown root-cause analysis.
- Supports input via standard QC orders.csv and result.json files.
- Identifies metrics like zero rate, windfall trades, monthly/annual cash flow, top winners/losers, and drawdown streaks.
- Includes clear reporting rules, required input formats, and key metrics to monitor.
元数据
常见问题
Qc Order Forensics 是什么？
Forensic diagnosis engine for backtest order data — trade quality, ROI attribution, monthly cashflow, drawdown root-cause analysis, and LLM-readable reports. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 158 次。
如何安装 Qc Order Forensics？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install qc-order-forensics」即可一键安装，无需额外配置。
Qc Order Forensics 是免费的吗？
是的，Qc Order Forensics 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Qc Order Forensics 支持哪些平台？
Qc Order Forensics 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Qc Order Forensics？
由 tltby12341（@tltby12341）开发并维护，当前版本 v1.0.0。
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