Agent skill

Stock Research

个股深度研究（多 Agent 编排版）— 多角色分析师并行编排（技术/新闻/资金情绪/基本面/市场 5 分析师并行 → 多空辩论 → 研究经理汇总），数据全部走 QuantMind 本地（QuantDB + PG 新闻富集 + Huntly），新闻双通道（自家 FinBERT 量化情绪 + 实时搜索补充）。用户说「深度研究」「个股深度研究」「研究某只股票」「多角度分析」时使用：跑…

AGPL-3.0Auto-check passedDocuments & Office

Install Stock Research

skills CLI

$ npx skills add qusong0627/QuantMind --skill stock-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install qusong0627/QuantMind stock-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/stock-research .claude/skills/stock-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "stock-research" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-research into .claude/skills/stock-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-research", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/qusong0627/QuantMind/tree/master/skills/stock-research

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add qusong0627/QuantMind --skill stock-research -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install qusong0627/QuantMind stock-research --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/stock-research .agents/skills/stock-research && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "stock-research" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-research into .agents/skills/stock-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-research", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add qusong0627/QuantMind --skill stock-research -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install qusong0627/QuantMind stock-research --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/stock-research .cursor/skills/stock-research && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "stock-research" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-research into .cursor/skills/stock-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-research", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/qusong0627/QuantMind.git --path skills/stock-research

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add qusong0627/QuantMind --skill stock-research -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install qusong0627/QuantMind stock-research --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/stock-research .gemini/skills/stock-research && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "stock-research" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-research into .gemini/skills/stock-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-research", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install qusong0627/QuantMind stock-research

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add qusong0627/QuantMind --skill stock-research -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/stock-research .github/skills/stock-research && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "stock-research" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-research into .github/skills/stock-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-research", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add qusong0627/QuantMind --skill stock-research -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install qusong0627/QuantMind stock-research --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/stock-research .opencode/skills/stock-research && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "stock-research" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-research into .opencode/skills/stock-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-research", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: stock-research
GitHub stars: 1.7k
Token cost: ~989 tokens
SKILL.md length: 196 words
Files: 11 (incl. scripts)
Skills in repo: 27
Repo updated: Oct 10, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

Works in 6 steps: 禁止伪造数据：所有价格/PE/财务/资金流数字必须来自数据包 JSON，严禁编造 → 数据包是唯一数据源：先跑 research_data.py 拿到… → 过期标注：数据包 generated_at 距今 >5 个交易日时，报告开头标注… → …
Tasks that involve PDF
SKILL.md covers 架构, ⚠️ 数据铁律（最高优先级，所有 Agent 必须遵守）, 执行流程（每次 7 步） and 支持的分析师独立运行, plus 1 more section
Runs Python scripts from its folder; calls docker and python3

What it does

Stock Research is an agent skill from qusong0627/QuantMind. 个股深度研究（多 Agent 编排版）— 多角色分析师并行编排（技术/新闻/资金情绪/基本面/市场 5 分析师并行 → 多空辩论 → 研究经理汇总），数据全部走 QuantMind 本地（QuantDB + PG 新闻富集 + Huntly），新闻双通道（自家 FinBERT 量化情绪 + 实时搜索补充）。用户说「深度研究」「个股深度研究」「研究某只股票」「多角度分析」时使用：跑 researchdata.py 取数 → 并行分析师 → 辩论 → 汇总报告 → PDF → 落盘深度分析目录。触发词：深度研究、个股研究、研究600519、多角度分析、全面分析某股

Its SKILL.md is about 990 tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts (for example `commands/stock-research.md`, `prompts/bear.md` and `prompts/bull.md`).

It sits in Documents & Office, covering PDF. The repository describes itself as: QuantMind（量化大脑）开源版是一款面向个人开发者与投研团队的 AI 原生多市场量化交易平台。深度集成微软 Qlib、RD-Agent 因子演化与 QuantBot全能工作台，提供从 300+ 维因子挖掘、13 种机器学习与深度学习模型工场、Qlib 高性能回测、截面批量推理、7x24… The licence is AGPL-3.0.

When your agent uses it

Tasks that involve PDF

Example prompts

“/stock-research”

Requirements

Python 3
Docker

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

禁止伪造数据：所有价格/PE/财务/资金流数字必须来自数据包 JSON，严禁编造
数据包是唯一数据源：先跑 research_data.py 拿到 {symbol}_{date}.json，各分析师只读该文件对应切片；WebSearch 只允许补充新闻面的时效性（不得用于取行情/财务）
过期标注：数据包 generated_at 距今 >5 个交易日时，报告开头标注 ⚠️ 数据截止 {generated_at}；财报报告期为上季度时注明
单位口径：amount 万元、市值元（估值 JSON 已原样给出，报告统一亿元=÷1e8）、L2 资金流脚本已转亿元、技术指标 pct_change 为 %
数据缺失不跳过：某段数据为空时在报告「数据可用性」注明，不得用推断值充数
Symbol 格式：内部统一后缀格式（600036.SH）；输入任意格式（600036/SH600036/招商银行）由脚本归一化

What it can do on your machine

Read from SKILL.md and the folder at commit 2e93d9a. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- docker
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use docker, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Stock Research loads about 989 tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 196 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~75
When it runs · the whole SKILL.md, loaded when a task matches: ~989

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from qusong0627/QuantMind at commit 2e93d9a, republished under its AGPL-3.0 licence (© qusong0627). 196 words, ~989 tokens.

Download SKILL.mdSave it as .claude/skills/stock-research/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.

name: stock-research
description: 个股深度研究（多 Agent 编排版）— 多角色分析师并行编排（技术/新闻/资金情绪/基本面/市场 5 分析师并行 → 多空辩论 → 研究经理汇总），数据全部走 QuantMind 本地（QuantDB + PG 新闻富集 + Huntly），新闻双通道（自家 FinBERT 量化情绪 + 实时搜索补充）。用户说「深度研究」「个股深度研究」「研究某只股票」「多角度分析」时使用：跑 research_data.py 取数 → 并行分析师 → 辩论 → 汇总报告 → PDF → 落盘深度分析目录。触发词：深度研究、个股研究、研究600519、多角度分析、全面分析某股

⚙️ 本技能遵循公共运行环境契约（最高优先级，先于本文其余内容执行）：详见 _shared/env-contract.md，执行前先读它。

stock-research — 个股深度研究（多 Agent 框架版）

把多角色投研编排落地到 QuantMind：数据 100% 走本地（QuantDB parquet + PG 新闻富集 + Huntly），新闻双通道（自家 FinBERT 情绪量化 + WebSearch 实时补充），输出研报级 MD + PDF，落盘深度分析目录（报告管理页 → A股市场 → {股票名}）。

与 [[stock-deep-research]]（智能体自主版深度投研）不冲突，互为补充：

本 skill = 5 分析师并行编排的「多 Agent 框架版」，流程更重、结构化更强；
[[stock-deep-research]] = 轻量自主版（特征快照 → 多空子代理辩论 → 综合研判），任何大模型可跑；
两者数据同源（QuantDB + PG），本 skill 额外整合了模型推理分数（engine_signal_scores）与新闻类型分布；
结论冲突时以数据为准：每个数字都可在数据包溯源，也可读归档目录下已有报告（A股市场/{股票名}/）交叉验证。

架构

Phase 1（并行 5 分析师，各读同一份数据包的不同切片）：
  技术面 → 趋势/均线/指标/量价
  新闻面 → 自家 FinBERT 情绪（主）+ WebSearch 实时新闻（辅）
  资金情绪 → L2 主力资金流 + 换手/量能
  基本面 → 估值 + 财务三表核心科目
  市场面 → 大盘背景 + 所属行业强弱 + 板块资金流
Phase 2（并行）：多头研究员 vs 空头研究员（基于 facts 辩论，禁编数据）
Phase 3：研究经理汇总 → 结论（评级/目标区间/风险/跟踪信号）
输出 → md → PDF → 落盘

⚠️ 数据铁律（最高优先级，所有 Agent 必须遵守）

禁止伪造数据：所有价格/PE/财务/资金流数字必须来自数据包 JSON，严禁编造
数据包是唯一数据源：先跑 research_data.py 拿到 {symbol}_{date}.json，各分析师只读该文件对应切片；WebSearch 只允许补充新闻面的时效性（不得用于取行情/财务）
过期标注：数据包 generated_at 距今 >5 个交易日时，报告开头标注 ⚠️ 数据截止 {generated_at}；财报报告期为上季度时注明
单位口径：amount 万元、市值元（估值 JSON 已原样给出，报告统一亿元=÷1e8）、L2 资金流脚本已转亿元、技术指标 pct_change 为 %
数据缺失不跳过：某段数据为空时在报告「数据可用性」注明，不得用推断值充数
Symbol 格式：内部统一后缀格式（600036.SH）；输入任意格式（600036/SH600036/招商银行）由脚本归一化

执行流程（每次 7 步）

第 1 步：跑数据脚本（容器内）

bash

docker cp <repo>/skills/stock-research/scripts/research_data.py quantmind:/tmp/
docker exec quantmind python3 /tmp/research_data.py --symbol 600036.SH
# 输出: /data/reports/stock_research/600036_SH_YYYYMMDD.json（容器内）
docker cp quantmind:/data/reports/stock_research/600036_SH_YYYYMMDD.json /tmp/

数据包含：quote（120 日 K 线/60日高低/20-60日涨幅）、indicators（量纲无关指标：rsi/kdj/macd/乖离率/量能/波动率/return 系列；均线由 K 线计算）、valuation（pe_ttm/pb/ps/市值）、l2_flow（近 10 日主力净流入）、financials（三表最新报告期）、sector（CSRC 一级行业）、market_context（5 指数 + 行业涨幅榜 + 板块资金流 1/5/10 日 + 所属行业）、news（近 60 天 FinBERT 情绪事件 + 来源/标签统计 + 类型分布）、model_score（最新推理 run 的融合/轻量/TFT 分数 + 信号方向 + 预期价 + 全市场分位）。

第 2 步：创建报告目录

bash

mkdir -p /tmp/stock-research/{symbol}/reports

第 3 步：Phase 1 并行启动 5 个分析师（Agent tool，各自独立）

每个 Agent 的 prompt = prompts/{role}.md 内容 + 数据包路径 + 输出路径。并行（同一消息多个 Agent 调用）。输出各自 reports/{role}.md。

technical.md — 技术面
news.md — 新闻面（双通道：自家情绪统计为主 + WebSearch 实时为辅）
sentiment.md — 资金情绪（L2 主力资金流）
fundamentals.md — 基本面（估值 + 财务）
market.md — 市场面（大盘 + 行业 + 板块资金）

第 4 步：Phase 2 多空辩论（并行）

bull.md / bear.md：基于 5 份分析师报告 + 数据包，各自构建最强多/空逻辑链。必须引用具体数据，不得空泛。

第 5 步：Phase 3 研究经理汇总（串行）

research_manager.md：读全部 7 份报告，输出最终研究报告（结论/目标区间/风险/跟踪信号）。

第 6 步：md → PDF → 落盘深度分析目录

bash

# PDF（研报风管线，QwenPaw 本地直接执行：扩展镜像已内置 reportlab + 中文字体）
python3 /app/backend/scripts/md_to_pdf_report.py /tmp/stock-research/{symbol}/reports/final.md /tmp/ma_report.pdf

# 落盘（A股市场/{股票名}/，与深度分析报告同列表）
# /data 为 QwenPaw 与 quantmind 共享挂载，直接写入即可被「报告档案」页实时列出
mkdir -p '/data/reports/stock_reports/A股市场/{股票名}'
cp /tmp/stock-research/{symbol}/reports/final.md '/data/reports/stock_reports/A股市场/{股票名}/{股票名}{代码}_2026-08-29_深度研究分析报告.md'
cp /tmp/ma_report.pdf '/data/reports/stock_reports/A股市场/{股票名}/{股票名}{代码}_2026-08-29_深度研究分析报告.pdf'

文件名约定：{股票名}{代码}_{日期}_深度研究分析报告.pdf（与现有深度学习分析报告同格式，便于排序）。

第 7 步：聊天回复速览

3-5 行：结论评级、关键数据（现价/PE/资金/情绪）、主要风险、报告路径。

支持的分析师独立运行

用户指定单个分析师时只跑该角色（如"技术面分析 600519"）：步骤 1 + 3（单 Agent）+ 直接输出该角色报告。

已知边界

财务为最新报告期（季度），非实时；估值/技术指标为最新交易日
新闻情绪依赖 FinBERT 富集（PG），Huntly 断流时新闻段降级为空并在报告中标注
仅支持 A 股（QuantDB 主库）；港股/美股个股研究走其他 skill
报告落盘目录为容器 root，宿主机不可写，统一容器内操作

Files

SKILL.md and 10 other files (scripts) in skills/stock-research of qusong0627/QuantMind.

SKILL.md
commands/stock-research.md
prompts/bear.md
prompts/bull.md
prompts/fundamentals.md
prompts/market.md
prompts/news.md
prompts/research_manager.md
prompts/sentiment.md
prompts/technical.md
scripts/research_data.py

Open the folder on GitHubat commit 2e93d9a

Compare with similar skills

Stock Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Stock Research compared with similar skills
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Harness Book Best Practice
wquguru/harness-books
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Bookforge Korean Ebook PDF Maker
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aws-samples/amazon-bedrock-agents-healthcare-lifesciences
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Questions about Stock Research

What does Stock Research do?

When should I use Stock Research?

Stock Research fits situations like: tasks that involve PDF.

How do I install Stock Research in Claude Code?

Run `npx skills add qusong0627/QuantMind --skill stock-research -a claude-code`. Or copy the skill folder (skills/stock-research in qusong0627/QuantMind) into .claude/skills/stock-research in your project. Claude Code loads it when a task matches its description.

How do I install Stock Research in Codex?

Run `npx skills add qusong0627/QuantMind --skill stock-research -a codex`. Or copy the skill folder (skills/stock-research in qusong0627/QuantMind) into .agents/skills/stock-research in your project. Codex loads it when a task matches its description.

Can I use Stock Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add qusong0627/QuantMind --skill stock-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stock-research, .gemini/skills/stock-research, .github/skills/stock-research and .opencode/skills/stock-research in your project.

What does Stock Research need to run?

Going by SKILL.md and its folder, Stock Research needs Python for the scripts in its folder and the command-line tools its instructions call (docker and python3). Our summary lists: Python 3; Docker.

Does Stock Research access the network?

SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Stock Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Stock Research use?

Stock Research is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Stock Research use?

About 989 tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Stock Research?

Skills that share tags, products or a category with Stock Research: Markitdown (ImCa0/just-laws, 781 stars), Gzh Design (isjiamu/gzh-design-skill, 4k stars), GenOffice Document CLI (genspark-ai/genoffice, 9.2k stars) and Harness Book Best Practice (wquguru/harness-books, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Stock Research?

qusong0627 (a GitHub user) maintains it in qusong0627/QuantMind, which has 1,725 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on October 10, 2026.

Source: qusong0627/QuantMind on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Stock Research

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

stock-research — 个股深度研究（多 Agent 框架版）

架构

⚠️ 数据铁律（最高优先级，所有 Agent 必须遵守）

执行流程（每次 7 步）

第 1 步：跑数据脚本（容器内）

第 2 步：创建报告目录

第 3 步：Phase 1 并行启动 5 个分析师（Agent tool，各自独立）

第 4 步：Phase 2 多空辩论（并行）

第 5 步：Phase 3 研究经理汇总（串行）

第 6 步：md → PDF → 落盘深度分析目录

第 7 步：聊天回复速览

支持的分析师独立运行

已知边界

Files

Compare with similar skills

Similar skills

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Questions about Stock Research