Borrowing it
Nothing to install: this file belongs to lululu811/zettaranc-skill. 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/lululu811/zettaranc-skill/main/AGENTS.mdgit clone --depth 1 https://github.com/lululu811/zettaranc-skillWrote 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/lululu811/zettaranc-skill/agents-md)<a href="https://agentmods.dev/instructions/lululu811/zettaranc-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/lululu811/zettaranc-skill/agents-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/lululu811/zettaranc-skill/agents-md"><img src="https://agentmods.dev/badge/instructions/lululu811/zettaranc-skill/agents-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 | $0.06493 | $0.06493 |
| Opus 5.5 | $0.02597 | $0.02597 |
| Sonnet 5.5 | $0.01299 | $0.01299 |
| Haiku 4.5 | $0.00649 | $0.00649 |
Grade A, and why
zettaranc-skill AGENTS.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 5d 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 — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Guidelines
本文件面向 AI 编程 Agent:是功能指南,不是架构全书。架构细节在
docs/,模块树在源码,这里只给入口、命令、约定、关键路径与注意事项。所有事实(版本、命令、路径)均对照仓库核实。
Project Overview
zettaranc-skill = 「Z 哥(zettaranc/万千)思维框架蒸馏包」+「A 股真实数据量化工具」双轨项目。
- 核心交付物:
SKILL.md(Skill-Schema-V2 合规的角色扮演协议,被 Claude Code/Cursor 加载)——LLM 用 Z 哥角色生成点评/话术。 - 数据层:Python 包
modules/,只负责数据准备(指标、信号、回测、评分),不做投资话术——这是刻意分层,避免「AI 味」。 - 可选层:
api/(FastAPI REST)+frontend/(React 看板)+rust/(Rust 加速计算核)。 - 当前版本:
v4.13.1(pyproject.toml:7、SKILL.mdfrontmatter + 版本表、skill.json:3、README.mdbadge、docs/CHANGELOG.md顶端、AGENTS.md本行,共 7 处必须一致;改pyproject.toml的version时同步这 7 处,docs/INDEX.md维护规则有同样的清单)。 - 许可证:MIT。
Architecture & Data Flow
双模式
| 模式 | 环境变量 | 说明 |
|---|---|---|
| JNB / 真实数据 | DATA_MODE=jnb |
走数据源取真实行情,可算指标/战法 |
| 普通小万 | DATA_MODE=websearch |
纯 LLM 对话,无事可查外部数据 |
数据源优先级(modules/datasource_resolution.py 统一决议)
「环境变量 → 用哪个 provider」只有一个定义处:modules/datasource_resolution.py::resolve_provider()。读路径(CompositeDataSource)与写路径(DataSyncer)都消费它,因此两边永远选到同一个源。
hithink(HITHINK_FINANCE_API_KEY 配置时最优先,v4.2.0)
→ Indevs(INDEVS_API_KEY,v3.8.1)
→ Tushare Pro(JNB 模式配置 TUSHARE_TOKEN)
→ a-stock-data(免费源,零配置默认,v4.1.0)
→ tushare-data-bridge(HTTP 缓存代理)
→ 本地 SQLite(data/stock_data.db,离线兜底)
读路径(CompositeDataSource)在此基础上按方法链式回退;写路径(DataSyncer)取同一个首选源(单源,保留重试与退避语义)。
改优先级只改
resolve_provider()一处。modules/data_sync/syncer.py与modules/datasource.py此前各抄了一份环境变量判断且已经漂移(写路径没有 hithink 分支),zt sync还直接钉死 tushare——在默认 websearch 模式下 tushare 无数据后端,整次同步静默同步 0 行却打印"同步完成"。v4.5.0 起统一。
约定:DB 优先读 K 线(先查 daily_kline 表,没有才调 API 并写回)。K 线读取统一走 modules/indicators/data_layer.py::get_kline_data 或 modules/strategies/core.py::get_kline_data。数据源缺 key 时绝不编造价格/信号,明确报告当前数据状态。
数据流链条
数据源 (CompositeDataSource)
→ indicators/data_layer.py # get_kline_data + indicator_cache + analyze_stock() 30步管线 → IndicatorResult
→ strategies/__init__.py # detect_all_strategies(ts_code, days) 折叠每日战法探测器 → 战法信号
→ screener/engine.py # screen_stocks(criteria, ...) 并行选股
→ backtest/ + simulator/ # 单策略/组合回测/少女少妇模拟器 → 绩效结果
# ↑ active_market_gate.py 注入闸门 port(TradingGate)控制开仓/强平
→ verify/pipeline.py # 五项硬指标验收(v1.0)
→ CLI --json / Web API # 宿主消费结构化数据
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.
- 5d ago Changed · +44 lines · +1,646 tokens per session 3fd18e8defb7
- 1mo ago Changed · +1 lines · +93 tokens per session ecab40b56d12
- 2mo ago First seen · 221 lines · 4,754 tokens per session scan A 118d25fee26c
zettaranc-skill AGENTS.md is an instructions file published in the GitHub repository lululu811/zettaranc-skill (521 stars, last pushed 6d ago), licensed MIT. It adds 6,493 tokens to every session, about $0.0260 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 2026-08-30.
zettaranc-skill AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| zettaranc-skill AGENTS.mdlululu811/zettaranc-skill | Instructions file | 521 | 6d ago | MIT | A | 6,493 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |