Markitdown
ImCa0/just-laws
Convert files and office documents to Markdown. An agent skill from ImCa0/just-laws.
每日复盘后的股票推荐（多维度选股）— 综合 A股每日复盘（市场方向/板块/资金/新闻情绪）+ 个股深度分析（9层），用多维度筛选条件（L2微观结构为主/模型融合分/仓位信号/板块强度/新闻情绪）从全市场挑出未来几天大概率走强的股票，支持跨多日聚合（--window），输出候选榜 + 综合复盘 + Top 个股深度分析，落盘 PDF…
Install Stock Picks
$ npx skills add qusong0627/QuantMind --skill stock-picks -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install qusong0627/QuantMind stock-picks --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/stock-picks .claude/skills/stock-picks && rm -rf skills-srcUse ~/.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/
Install the "stock-picks" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-picks into .claude/skills/stock-picks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-picks", 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 install https://github.com/qusong0627/QuantMind/tree/master/skills/stock-picksType 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.
$ npx skills add qusong0627/QuantMind --skill stock-picks -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install qusong0627/QuantMind stock-picks --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/stock-picks .agents/skills/stock-picks && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "stock-picks" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-picks into .agents/skills/stock-picks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-picks", 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.
$ npx skills add qusong0627/QuantMind --skill stock-picks -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install qusong0627/QuantMind stock-picks --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/stock-picks .cursor/skills/stock-picks && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "stock-picks" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-picks into .cursor/skills/stock-picks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-picks", 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 https://github.com/qusong0627/QuantMind.git --path skills/stock-picks--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add qusong0627/QuantMind --skill stock-picks -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install qusong0627/QuantMind stock-picks --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/stock-picks .gemini/skills/stock-picks && rm -rf skills-srcUse ~/.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/
Install the "stock-picks" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-picks into .gemini/skills/stock-picks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-picks", 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.
$ gh skill install qusong0627/QuantMind stock-picksInstalls 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).
$ npx skills add qusong0627/QuantMind --skill stock-picks -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/stock-picks .github/skills/stock-picks && rm -rf skills-srcUse ~/.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/
Install the "stock-picks" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-picks into .github/skills/stock-picks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-picks", 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.
$ npx skills add qusong0627/QuantMind --skill stock-picks -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install qusong0627/QuantMind stock-picks --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/qusong0627/QuantMind.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/stock-picks .opencode/skills/stock-picks && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "stock-picks" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/stock-picks into .opencode/skills/stock-picks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stock-picks", 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-picks- GitHub stars
- 1.7k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 260 words
- Files
- 5 (incl. scripts)
- Skills in repo
- 27
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
每日复盘后的股票推荐（多维度选股）— 综合 A股每日复盘（市场方向/板块/资金/新闻情绪）+ 个股深度分析（9层），用多维度筛选条件（L2微观结构为主/模型融合分/仓位信号/板块强度/新闻情绪）从全市场挑出未来几天大概率走强的股票，支持跨多日聚合（--window），输出候选榜 + 综合复盘 + Top 个股深度分析，落盘 PDF…
- Tasks that involve PDF
- SKILL.md covers ⚠️ 铁律（先读，最高优先级）, 执行流程（固定 6 步）, 多维度筛选条件详解（写报告时逐维引用） and 报告模板, plus 1 more section
- Runs Python scripts from its folder; calls python3 and docker
What it does
Stock Picks is an agent skill from qusong0627/QuantMind. 每日复盘后的股票推荐（多维度选股）— 综合 A股每日复盘（市场方向/板块/资金/新闻情绪）+ 个股深度分析（9层），用多维度筛选条件（L2微观结构为主/模型融合分/仓位信号/板块强度/新闻情绪）从全市场挑出未来几天大概率走强的股票，支持跨多日聚合（--window），输出候选榜 + 综合复盘 + Top 个股深度分析，落盘 PDF 到股票报告目录。用户说「选股」「推荐股票」「每日推荐」「明日看好」「选股推荐」「复盘后选股」时使用：跑复盘取数 → pickcandidates 多维打分 → Top N 深分 → 综合报告 → PDF。触发词：选股、股票推荐、每日推荐、明日看好、推荐股票、今日选股
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts (for example `scripts/backtest_picks.py`, `scripts/pick_candidates.py` and `scripts/tests/test_pick_candidates.py`).
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-picks”
Requirements
- Python 3
- Docker
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 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3docker
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 Picks loads about 1.6k tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 260 words of instructions outside code blocks.
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). 260 words, ~1,598 tokens.
Download SKILL.mdSave it as
.claude/skills/stock-picks/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- stock-picks
- description
- 每日复盘后的股票推荐（多维度选股）— 综合 A股每日复盘（市场方向/板块/资金/新闻情绪）+ 个股深度分析（9层），用多维度筛选条件（L2微观结构为主/模型融合分/仓位信号/板块强度/新闻情绪）从全市场挑出未来几天大概率走强的股票，支持跨多日聚合（--window），输出候选榜 + 综合复盘 + Top 个股深度分析，落盘 PDF 到股票报告目录。用户说「选股」「推荐股票」「每日推荐」「明日看好」「选股推荐」「复盘后选股」时使用：跑复盘取数 → pick_candidates 多维打分 → Top N 深分 → 综合报告 → PDF。触发词：选股、股票推荐、每日推荐、明日看好、推荐股票、今日选股
⚙️ 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-picks — 每日复盘后的股票推荐（多维度选股）
把「每日复盘」（市场广度）和「个股深度分析」（个股深度）两张皮缝起来：先用复盘产物定市场环境（该不该进场、主线在哪、资金方向），再用多维度筛选条件从全市场挑候选，对 Top N 做 9 层深分，最后输出一份报告：综合复盘 + 候选榜 + Top 个股深分。报告 Markdown + PDF，落盘前端「股票报告」页可见目录，聊天回复速览。
定位：[[daily-review]] 是广度（今天市场发生了什么），[[stock-market-analysis]] 是深度（某只股票值不值得看），本 skill 是两者合一的推荐——先选再深挖，产出可执行的候选池，不是复盘报告。
⚠️ 铁律（先读，最高优先级）
| 陷阱 | 正确口径 |
|---|---|
| 推荐不是承诺 | 所有候选是「多维信号合成的相对强势」，不是「明天必涨」。报告必须带风险声明 + 数据滞后声明 |
| ST 股默认排除 | ST/*ST/退 有 5% 涨跌幅限制 + 退市风险，pick_candidates.py 默认排除（--keep-st 才保留） |
| 信号日取最近「全量」推理日 | 默认 = distinct symbol ≥ 1000 的那天（engine_signal_scores），避免最近只推理了几十只的残日；--date 可显式指定 |
| 分数单位 | fusion_score 是模型预测分（非涨跌幅）；position_score 0~1 是半凯利仓位；pct_industry 是行业截面百分位 |
| L2 是 T+5/T+10 信号 | VPIN 族正 IC（高分偏多）、vol_persistence 等负 IC（高分偏空）——看状态分位，别当单日信号 |
| 趋势不纳入 | 模型分数趋势维度按需求移除——打分只用 L2/融合/仓位/板块/新闻，不参与排名 |
| L2 主导 | L2 权重(40%) > 融合分(30%)：L2 是 T+5/T+10 信号，先看订单簿微结构，再看模型预测 |
执行流程（固定 6 步）
第 1 步：跑每日复盘取数（复用 [[daily-review]]）
# ① 宿主机：daily_review.py 出 指数/广度/板块/资金/L1/L2 + 模型推理信号 + 次日方向
cd <repo>/skills/daily-review/scripts
python3 daily_review.py --date 20260821 # 不带 --date 取最新交易日
# ② 容器内：news_review.py 聚合当日新闻情绪（先跑这个，新闻维度才能加权）
docker cp <repo>/skills/daily-review/scripts/news_review.py quantmind:/tmp/
docker exec quantmind python3 /tmp/news_review.py --date 20260821产出：data/reports/daily_review/{YYYY-MM-DD}_stats.json + {YYYY-MM-DD}_facts.md + {YYYY-MM-DD}_news.json。
这一步给推荐提供：市场方向（六维）、主线板块、资金流向、新闻聚焦板块、L2 微观结构状态——推荐必须和市场环境自洽（大盘空仓日不该推满仓，杀跌板块的个股即使分数高也要警惕）。
第 2 步：多维度选股（本 skill 脚本）
python3 <repo>/skills/stock-picks/scripts/pick_candidates.py --data-date 20260821 --window 3 --top 30 --json # 跨3日聚合
python3 <repo>/skills/stock-picks/scripts/pick_candidates.py --data-date 20260821 --window 1 --top 30 --json # 严格单日（无未来视觉）
python3 <repo>/skills/stock-picks/scripts/pick_candidates.py --top 30 --json # 默认最近全量推理日
python3 <repo>/skills/stock-picks/scripts/pick_candidates.py --top 30 --json --no-l2 # 跳过 L2（更快）产出：data/reports/stock_picks/{YYYYMMDD}_picks.json（全量候选 + 每维分解）+ {YYYYMMDD}_picks.md（排名表骨架）。
多维度打分 = 六维加权（满分 1.0，L2 主导）：L2 40% / 融合 25% / L1动量 15% / 仓位 10% / 板块 5% / 新闻 5%。趋势不纳入。单模型铁律：只用默认日推模型（5eea5418）的单一 run 分数，杜绝多模型融合。默认精选 5 只。
跨日聚合：--window N 从数据日起往前 N 个推理日，每股取跨日复合分均值后排名（--window 1 = 严格单日无未来视觉）。
硬过滤：① 无融合分数剔除；② 仓位门 position_score>0 或 行业百分位≥80%（避免大盘空仓日推满仓）；③ 默认排除 ST。
未含维度（如当日无 news.json、L2 分区缺失）时该维中性 0.5，picks.md 头部会标注「未含：L2, 新闻」——报告里要声明，不能假装都有。
第 3 步：Top N 深分（复用 [[stock-market-analysis]]）
对候选榜前 5~10 只跑 9 层深分，取 --json 输出供报告引用：
python3 <repo>/scripts/stock_9layer_fetch.py 001237.SZ --json # 宿主机
# 输出 /tmp/001237_9layer.json（23 因子 vs 全市场截面分位 + IC 方向）深分重点核对（和候选维度互相印证，发现矛盾要写进报告）：
- L3 技术位：候选时点 vs MA20/前高——分数高但跌破 MA20 的是矛盾项
- L4b 微观结构：正 IC 因子（VPIN 族）是否处于健康分位
- L6 模型：该股历史推理信号 vs 当日融合分，是否一致
- L7 新闻：有没有个股直接消息/相关行业消息，和三步纵深结论
第 4 步：综合报告写作（Markdown，模板见下）
报告 = facts.md 的事实 + picks 骨架的数字 + 深分的数值 + 你的解读。facts/picks/深分没有的数字禁止出现。 推荐榜数字必须照抄 picks.json，深分数字必须照抄 /tmp/{code}_9layer.json，禁止臆造。
第 5 步：Markdown → PDF + 落盘（必做，只发 /tmp = 未交付）
# Markdown → PDF（研报风，复用 md_to_pdf_report.py）
docker cp 选股推荐.md quantmind:/tmp/picks.md
docker exec quantmind bash -lc "cd /app && python3 backend/scripts/md_to_pdf_report.py /tmp/picks.md /tmp/picks.pdf"
docker cp quantmind:/tmp/picks.pdf 选股推荐.pdf
# 落盘股票报告目录（宿主机必须 docker cp，目录 owner 是容器 root）
docker cp 选股推荐.md quantmind:/data/reports/stock_reports/每日选股/每日选股推荐_2026-08-21.md
docker cp 选股推荐.pdf quantmind:/data/reports/stock_reports/每日选股/每日选股推荐_2026-08-21.pdf落盘后 ls 确认 md + pdf 都在（前端「股票报告」页 → 每日选股 文件夹）。
第 6 步：聊天回复速览
**每日选股推荐 2026-08-21（周五）**
一句话：…（市场环境 → 推荐逻辑 → 候选池特征）
市场：上证 +0.01% / 深成 +0.45%；涨停 64 / 跌停 14；主线 …；资金 …；次日方向 看多/看空（xx/11，★★★）
Top5 候选（五维综合，L2 主导）：
1. 惠康科技 001237 — L2 0.80 / 融合 0.0365 / 仓位 84% / 行业分位 100%
2. 今天国际 300532 — …（每只 1 行，标注最强维度 + 该股风险点）
3. …
深分亮点：Top3 中 X 只技术位健康、L2 VPIN 分位 >70%……（哪些候选通过了深分、哪些有矛盾）
⚠️ 以上为模型信号合成的相对强势候选，非投资建议；数据截至 2026-08-21（两融/北向滞后见数据说明）
→ 完整报告已落盘「股票报告 → 每日选股」目录多维度筛选条件详解（写报告时逐维引用）
| 维度 | 权重 | 数据源 | 怎么判读 |
|---|---|---|---|
| L2 微观结构 | 40% | l2_factors 正 IC 因子 + 负 IC 因子（负 IC 反转） | 正 IC 高=知情资金活跃；负 IC 低=毒性/波动小；健康分 = 0.5×正IC分位 + 0.5×(1−负IC分位) |
| 模型融合分数 | 30% | engine_signal_scores.fusion_score 幅度归一 | 融合分越高=模型预测收益越强；用默认单模型（日推模型） |
| 仓位信号 | 15% | quality.position.position_score | 0.8+ = 强行业地位 + 半凯利高仓位；<0.3 弱；数据缺失不拦 |
| 板块强度 | 10% | industry_top10_avg 截面分位 + 板块超级大单净额 | 行业头部强度 + 当日板块大单净流入（跌市抄底方向反推） |
| 新闻情绪 | 5% | news_review.py 产物 news.json | net_ratio>0 偏多；有直接个股新闻的优先 |
报告里每只候选必须能说清「它强在哪几个维度」+「弱在哪」，禁止只贴数字不解读。候选若和市场主线、次日方向冲突，必须明说。
报告模板
# 每日选股推荐 2026-08-21（周五）
> **报告日期**：2026-08-21
> **数据截至**：2026-08-21（信号日）
> **口径**：候选来自最近全量推理日 engine_signal_scores + l2_factors + 当日新闻情绪；五维加权（L2 40/融合 30/仓位 15/板块 10/新闻 5，趋势不纳入）
## 一、市场环境（综合复盘，从 facts.md 提炼）
指数/广度/量能/主线/资金 2-4 句 + 次日方向（六维合成）+ 一句话「该不该进场/什么风格占优」。
**这里定推荐基调**：市场偏强推进攻型，震荡降仓位，弱势只列观察不推荐。
## 二、候选榜（Top10，照抄 picks.md）
表格：# / 代码 / 名称 / 综合分 / L2 / 融合 / 仓位 / 行业分位 / 覆盖日 / 行业 / 板块大单
每只标注最强维度 + 一句话依据。
## 三、Top 个股深度分析（前 3-5 只，用 stock_9layer_fetch --json 结果）
每只分节：
### {名称}（{代码}）
- **候选维度**：最强维度 + 分数
- **9层核对**：L3 技术位（vs MA20）、L4b 微观结构（VPIN 分位）、L6 模型一致性、L7 新闻（三步纵深结论）
- **风险点**：弱维度 / 技术矛盾 / 板块杀跌风险
- **综合判断**：推荐 / 观察 / 剔除（剔除要写原因）
## 四、推荐逻辑与风险声明
- 候选池与市场主线一致性：命中/偏离
- 数据滞后声明（从 facts.md 复制）
- ⚠️ 本报告为模型信号合成的相对强势候选，非投资建议。股市有风险，投资需谨慎。
## 五、明日验证清单
2-4 条可验证预期（明天能判断对错的）：如「Top5 平均跑赢全市场」「候选集中板块继续走强」等维护
- 核心脚本：
scripts/pick_candidates.py（宿主机跑；PGengine_signal_scores+stock_aliases，QuantDBl2_factors，news_review.py产物） - 依赖技能：
[[daily-review]]（复盘取数）、[[stock-market-analysis]]（深分）、[[quantdb-fields]]（单位口径） - 权重/阈值改
pick_candidates.py顶部常量（W_*、_POSITION_GATE、_MIN_SIGNAL_COVERAGE） - 单测：
cd scripts && python3 -m pytest tests/ -q（覆盖打分/过滤/趋势/单位）
© qusong0627, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts) in skills/stock-picks of qusong0627/QuantMind.
- SKILL.md
- scripts/backtest_picks.py
- scripts/pick_candidates.py
- scripts/tests/pytest.ini
- scripts/tests/test_pick_candidates.py
Open the folder on GitHubat commit 2e93d9a
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| Gzh Designisjiamu/gzh-design-skill | 4k | — | ~2.2k | Automated safety check: Pass | AGPL-3.0 | |
| GenOffice Document CLIgenspark-ai/genoffice | 9.2k | — | ~19k | Automated safety check: Pass | Apache-2.0 | |
| Harness Book Best Practicewquguru/harness-books | 3.2k | — | ~4.1k | Automated safety check: Pass | None | |
| Bookforge Korean Ebook PDF Makergongnyang/bookforge | 316 | 1 repos | ~1.7k | Automated safety check: Pass | MIT |
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Categories
Questions about Stock Picks
What does Stock Picks do?
每日复盘后的股票推荐（多维度选股）— 综合 A股每日复盘（市场方向/板块/资金/新闻情绪）+ 个股深度分析（9层），用多维度筛选条件（L2微观结构为主/模型融合分/仓位信号/板块强度/新闻情绪）从全市场挑出未来几天大概率走强的股票，支持跨多日聚合（--window），输出候选榜 + 综合复盘 + Top 个股深度分析，落盘 PDF…. Stock Picks is an agent skill from qusong0627/QuantMind.
When should I use Stock Picks?
Stock Picks fits situations like: tasks that involve PDF.
How do I install Stock Picks in Claude Code?
Run `npx skills add qusong0627/QuantMind --skill stock-picks -a claude-code`. Or copy the skill folder (skills/stock-picks in qusong0627/QuantMind) into .claude/skills/stock-picks in your project. Claude Code loads it when a task matches its description.
How do I install Stock Picks in Codex?
Run `npx skills add qusong0627/QuantMind --skill stock-picks -a codex`. Or copy the skill folder (skills/stock-picks in qusong0627/QuantMind) into .agents/skills/stock-picks in your project. Codex loads it when a task matches its description.
Can I use Stock Picks 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-picks -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-picks, .gemini/skills/stock-picks, .github/skills/stock-picks and .opencode/skills/stock-picks in your project.
What does Stock Picks need to run?
Going by SKILL.md and its folder, Stock Picks needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and docker). Our summary lists: Python 3; Docker.
Does Stock Picks 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 Picks 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 Picks use?
Stock Picks 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 Picks use?
About 1.6k tokens (SKILL.md is roughly 6.4k 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 Picks?
Skills that share tags, products or a category with Stock Picks: 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 Picks?
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.