invest-skills: Instructions file for Claude Code

AGENTS.md

invest-skills AGENTS.md is an instructions file for Claude Code, Codex, OpenCode from Veblin/invest-skills. It costs 2,350 tokens per session, scanned A, original, MIT.

A project instruction file that sets rules for an investment-analysis agent. It requires traceable data sources, prohibits direct buy or sell recommendations, and says AI output should not be the main basis for investment decisions.

In plain words
What is it for?
Use it to guide agents that explain financial data, cite sources, compare scenarios, and produce learning material without issuing trade recommendations.
Why use it?
It reduces unsupported financial claims and makes analysis easier to verify against original sources such as reports or market data. It also sets boundaries around regulated investment advice.

Instructions file for Claude CodeCodexOpenCode

Written for Claude Code and Codex and OpenCode: SessionStart hook event, but also the file is AGENTS.md. Also seen: reads .claude/ paths; mentions Claude Code; mentions AGENTS.md.

This is Veblin/invest-skills's own configuration. It tells Claude Code, Codex and OpenCode how to work on invest-skills itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything invest-skills configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Veblin/invest-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Veblin/invest-skills/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/Veblin/invest-skills

Made for: Claude Code, Codex, OpenCode.

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Per session 2,350 This file is loaded in full into every session.
When invoked 2,350 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.02350 $0.02350
Opus 5 $0.01175 $0.01175
Sonnet 5 $0.00470 $0.00470
Haiku 4.5 $0.00235 $0.00235

Measured 5d ago against content hash 5d1c7ad31e23, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

invest-skills 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.

AGENTS.md · 194 lines

How it starts

The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — AI 协作规则

本文档定义 AI Agent 在本项目中的行为边界、设计哲学和质量标准。 所有贡献者(人类和 AI)都应遵守这些规则。


五条硬约束

约束 1:禁止荐股

不输出任何形式的"买入/卖出/持有"建议、仓位建议。允许多情景估值参考价,但必须标注假设前提、概率权重,并明确"仅供参考,不构成投资建议";不允许无假设前提的单一目标价数字。这是法律红线,也是能力边界——LLM 没有资格做投资建议。

约束 2:LLM 不可作为投资决策的主要信源

LLM 存在幻觉问题——在专业金融领域,这些幻觉极难被非专业人士识别。AI 的输出只能作为"学习材料的整理和解读",不能作为"投资决策的依据"。关键财务数据必须标注原始来源(财报 PDF / akshare / Tushare),让用户有能力追溯验证。

约束 3:所有分析解释必须依赖数据源,引用来源

这是本 Skill 最核心的质量标准。LLM 生成的分析性文本(趋势解读、行业判断、估值讨论)必须建立在可追溯的数据源之上,而非 LLM 的"知识记忆"。标准对标学术论文:每个论述要么标注数据来源,要么明确声明为"待验证的推测"。没有数据支撑的分析不输出。

约束 4:项目文档不提供社交功能入口

不建群、不设讨论区、不做用户间互动。项目是个人学习工具,开源分享。

约束 5:先服务于自己的学习需求,但以可分发标准设计

项目源于作者自身的 A 股/港股投资学习实践。功能迭代以解决自己遇到的真问题为导向,不追求覆盖所有假想需求。同时,项目按 Agent Skills 开放格式构建,面向 Claude Code / Hermes / WorkBuddy 等多平台分发——配置步骤须有文档、数据源须有 fallback、关键路径须在无 Python 环境(MCP 模式)下同样可用。WorkBuddy 分发通道(3 步安装 / token 配置 / 用户级 AGENTS 与 MEMORY 模板 / 真机验收表)见 docs/workbuddy/ 与 README「WorkBuddy 安装」节。


目标用户画像

用户画像:能够在 Claude Code 或 Hermes 中安装并使用 Skills 的用户,普遍具备较强的信息获取和自主判断能力——学习能力强、能自行验证信息、不会被营销话术左右。

这意味着:

  • 不需要"简单化":可以用专业术语,可以展示复杂逻辑
  • 需要"可验证":每个结论都要追溯到数据源头,让用户独立判断
  • 需要"方法论":用户要的是分析框架和思考工具,不是结论
  • 不存在商业变现动机:项目是开源学习工具,没有"流量变现"/"知识付费"等商业逻辑

设计哲学

用户能力模型:
  不是"需要被告知该做什么的小白"
  而是"想要理解事情如何运作的聪明人"

产品逻辑:
  不是"信任我,我帮你判断"
  而是"这是数据,这是分析方法,这是不确定性,你自己判断"

迭代逻辑:
  不是"用户想要什么功能"
  而是"我在投资学习中遇到了什么问题,需要一个工具来解决"

技术指标规范

MA5/MA10/MA20/MA60 和 MACD(DIF/DEA)等指标仅用于理解市场状态,不用于生成交易信号:

  • ✅ 描述当前价格与均线的位置关系(如"价格位于 MA60 上方""MA20 走平")
  • ✅ 描述 MACD 的 DIF/DEA 位置和方向(如"DIF 在零轴上方""DIF 向下靠近 DEA")
  • ✅ 结合均线和 MACD 理解"市场参与者的共识趋势"
  • ❌ 输出"金叉买入""死叉卖出""MACD 底背离抄底"等交易信号
  • ❌ 基于技术指标给出任何操作建议

数据源分层(A股/港股 + 全球宏观)

A 股数据源(优先级)

有 Token: Tushare ∥ akshare → 腾讯行情 → 标注不可得
无 Token: akshare → 腾讯行情 → 标注不可得
  • Tushare 与 akshare 并列并行(先到先用),非前置拦截器
  • Tushare Token 无效时静默跳过,不影响主 fallback 链
  • TickFlow ✅ 已接入(v0.1.6)— 免费免注册独立 K 线数据源,提供第四源交叉验证

港股数据源(优先级)

akshare(东方财富港股频道)→ 标注不可得

yfinance(.HK 后缀)计划在未来版本接入。

Read the full file on GitHub · 194 lines

Changes

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.

  1. 5d ago First seen · 194 lines · 2,350 tokens per session scan A 5d1c7ad31e23

Subscribe to this mod's changes

invest-skills AGENTS.md is an instructions file published in the GitHub repository Veblin/invest-skills (14 stars, last pushed 2d ago), licensed MIT. It adds 2,350 tokens to every session, about $0.0118 per session on Opus 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 2026-09-04.

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