Borrowing it
Nothing to install: this file belongs to wumu2013/quantcli. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/wumu2013/quantcli/main/CLAUDE.mdgit clone --depth 1 https://github.com/wumu2013/quantcliWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/wumu2013/quantcli/claude-md)<a href="https://agentmods.dev/instructions/wumu2013/quantcli/claude-md"><img src="https://agentmods.dev/badge/instructions/wumu2013/quantcli/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/wumu2013/quantcli/claude-md"><img src="https://agentmods.dev/badge/instructions/wumu2013/quantcli/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.02660 | $0.02660 |
| Opus 5.5 | $0.01064 | $0.01064 |
| Sonnet 5.5 | $0.00532 | $0.00532 |
| Haiku 4.5 | $0.00266 | $0.00266 |
Grade A, and why
quantcli CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
QuantCLI - A multi-factor stock selection CLI tool for quantitative research.
AI Agent Friendly: Designed for AI Agent integration with:
- JSON output mode (
--json) for structured data - Claude Code Skill support (
/skill multi-factor-strategy) - Idempotent APIs for safe retries
- Alpha101 factor library examples
Target Users: Individual quant researchers, students, part-time investors, AI Agents
Core Design Principles
- Let it Crash: Fail fast, fail loud. No tolerance for silent errors. Force correctness.
- YAGNI: You Aren't Gonna Need It. Resist over-engineering. Solve today's problems.
- 80/20 Rule: 80% of use cases covered by 20% of features.
- DataFrame First: All APIs return/accept
pd.DataFrame, notList[Dict].
Architecture
quantcli/
├── datasources/ # 数据源适配器 (akshare, baostock, mixed, mysql)
├── parser/ # 公式表达式解析器 (40+ 内置函数)
│ ├── formula.py # 核心解析器
│ └── constants.py # BUILTIN_FUNCTIONS, COLUMN_ALIASES
├── core/ # 核心引擎
│ ├── data.py # DataManager: 缓存、清洗
│ ├── factor.py # FactorEngine, Factor, FactorRegistry
│ └── backtest.py # BacktestEngine, YAMLBacktestEngine
├── factors/ # 因子配置 (推荐使用新 API)
│ ├── base.py # 数据类 (FactorDefinition, StrategyConfig, BacktestConfig)
│ ├── loader.py # load_strategy(), load_factor(), load_all_factors()
│ ├── screening.py # ScreeningEvaluator
│ ├── compute.py # FactorComputer (返回 DataFrame)
│ ├── ranking.py # FactorRanker, ScoringEngine
│ └── pipeline.py # FactorPipeline: 多阶段编排
├── cli.py # CLI 入口点
└── utils/ # 工具函数
Multi-Stage Filter Pipeline Flow
Stage 1: fundamental_data → fundamental_conditions → candidates
Stage 2: price_data → daily_conditions → filtered candidates
Stage 3: factors → weight fusion + conditions + bonuses → ranked results
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 312 lines · 2,660 tokens per session scan A 7849d882a50e
quantcli CLAUDE.md is an instructions file published in the GitHub repository wumu2013/quantcli (5 stars, last pushed 9mo ago), licensed MIT. It adds 2,660 tokens to every session, about $0.0106 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
quantcli CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| quantcli CLAUDE.mdwumu2013/quantcli | Instructions file | 5 | 9mo ago | MIT | A | 2,660 | Claude Code |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 9,719 | GitHub Copilot |
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| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | today | MIT | A | 4,680 | Codex, OpenCode |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 3,086 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 280k | 2d ago | MIT | A | 4,325 | Codex, OpenCode |