RD-Agent A股因子挖掘端到端流水线：环境 preflight → 启动演化 → 轮询完成 → 批量回测评估 → IC/Sharpe 排序 → explain 解读 → export 入库 → Markdown 报告。在 QuantBot / Claude Code 中挖因子时使用，一条命令跑完整流程。触发词：挖因子、因子挖掘、挖新因子、因子演化、RD-Agent、alpha…
$ npx skills add qusong0627/QuantMind --skill rd-agent-factor-mining -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install qusong0627/QuantMind rd-agent-factor-mining --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/rd-agent-factor-mining .claude/skills/rd-agent-factor-mining && 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 "rd-agent-factor-mining" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/rd-agent-factor-mining into .claude/skills/rd-agent-factor-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rd-agent-factor-mining", 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/rd-agent-factor-miningType 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 rd-agent-factor-mining -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install qusong0627/QuantMind rd-agent-factor-mining --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/rd-agent-factor-mining .agents/skills/rd-agent-factor-mining && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "rd-agent-factor-mining" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/rd-agent-factor-mining into .agents/skills/rd-agent-factor-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rd-agent-factor-mining", 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 rd-agent-factor-mining -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install qusong0627/QuantMind rd-agent-factor-mining --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/rd-agent-factor-mining .cursor/skills/rd-agent-factor-mining && 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 "rd-agent-factor-mining" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/rd-agent-factor-mining into .cursor/skills/rd-agent-factor-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rd-agent-factor-mining", 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/rd-agent-factor-mining--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 rd-agent-factor-mining -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install qusong0627/QuantMind rd-agent-factor-mining --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/rd-agent-factor-mining .gemini/skills/rd-agent-factor-mining && 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 "rd-agent-factor-mining" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/rd-agent-factor-mining into .gemini/skills/rd-agent-factor-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rd-agent-factor-mining", 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 rd-agent-factor-miningInstalls 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 rd-agent-factor-mining -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/rd-agent-factor-mining .github/skills/rd-agent-factor-mining && 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 "rd-agent-factor-mining" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/rd-agent-factor-mining into .github/skills/rd-agent-factor-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rd-agent-factor-mining", 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 rd-agent-factor-mining -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 rd-agent-factor-mining --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/rd-agent-factor-mining .opencode/skills/rd-agent-factor-mining && 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 "rd-agent-factor-mining" agent skill from https://github.com/qusong0627/QuantMind/tree/master/skills/rd-agent-factor-mining into .opencode/skills/rd-agent-factor-mining/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rd-agent-factor-mining", 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.
rd-agent-factor-miningRD-Agent A股因子挖掘端到端流水线：环境 preflight → 启动演化 → 轮询完成 → 批量回测评估 → IC/Sharpe 排序 → explain 解读 → export 入库 → Markdown 报告。在 QuantBot / Claude Code 中挖因子时使用，一条命令跑完整流程。触发词：挖因子、因子挖掘、挖新因子、因子演化、RD-Agent、alpha…
Rd Agent Factor Mining is an agent skill from qusong0627/QuantMind. RD-Agent A股因子挖掘端到端流水线：环境 preflight → 启动演化 → 轮询完成 → 批量回测评估 → IC/Sharpe 排序 → explain 解读 → export 入库 → Markdown 报告。在 QuantBot / Claude Code 中挖因子时使用，一条命令跑完整流程。触发词：挖因子、因子挖掘、挖新因子、因子演化、RD-Agent、alpha agent、自动挖因子、一键挖因子、因子回测、演化因子、启动因子任务
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud. It works with Docker and DeepSeek. 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.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 2e93d9a. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
curlpython3dockerFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use curl and docker, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
DEEPSEEK_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Rd Agent Factor Mining loads about 2k tokens when it runs. Until then it costs about 63 tokens; SKILL.md has 384 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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
nv.py` 优先级最高） | 更新 `~/projects/quantmind/.env` 的 `DEEPSEEK_API_KEY`，改后必须 `docker compose up -d quantmind` recreate |nvalid` | DEEPSEEK_API_KEY 失效 | 换 key 到 `.env`，`docker compose up -d quantmind` recreate 才生效 |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); files beside SKILL.md are not scanned.
The full file from qusong0627/QuantMind at commit 2e93d9a, republished under its AGPL-3.0 licence (© qusong0627). 384 words, ~1,987 tokens.
.claude/skills/rd-agent-factor-mining/SKILL.md (or your agent's skills folder).⚙️ 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。
调用 RD-Agent（Alpha Agent）自动挖掘 A 股 alpha 因子，覆盖完整链路： preflight → evolve（LLM 演化）→ 轮询 → 批量回测 → 排名 → explain → export → 报告。
做任何挖掘前，先跑容器内三项健康检查。任一 FAIL 都要先修再挖。
cd ~/projects/quantmind && python3 scripts/alpha_agent/factor_pipeline.py --check-env
# 期望 3 项全 PASS: conda_shim / litellm_patch / deepseek_key| 检查项 | 作用 | 失败处理 |
|---|---|---|
hardware | 最低 8 核 / 32GB。RD-Agent 演化会把 CPU/内存打满，低于此规格直接 412 失败，避免整机卡死 | 换机器或关其他重负载；测试可设 ALPHA_AGENT_SKIP_HW_LOCK=1（生产勿开） |
conda_shim | RD-Agent LocalEnv 硬编码 rdagent4qlib conda 环境，容器无 conda，靠 shim 映射到容器 python | 确认 docker/conda-shim 挂载 /usr/local/bin/conda:ro 且文件有 +x |
litellm_patch | litellm 1.97 + pydantic 2.13 冲突（Message is not fully defined） | 确认 docker/litellm_sitecustomize.py 挂载为 site-packages/sitecustomize.py:ro |
deepseek_key | 因子挖掘走 DeepSeek 通道（llm_env.py 优先级最高） | 更新 ~/projects/quantmind/.env 的 DEEPSEEK_API_KEY，改后必须 docker compose up -d quantmind recreate |
preflight 机制全在管道脚本内置；手动检修环境见文末「常见问题」。
cd ~/projects/quantmind
# 最小示例：一个方向，演化+批量回测+排名（默认报告 /tmp/rd_agent_factor_report.md）
python3 scripts/alpha_agent/factor_pipeline.py --direction "连板高度递减与涨停回封率"
# 全流程：演化 + 回测 + top5 解读 + 最高 |IC| 导出
python3 scripts/alpha_agent/factor_pipeline.py \
--direction "筹码集中度上行伴随低位换手放大" \
--universe csi300 --loops 3 \
--explain-top 5 --export --min-ic 0.03 \
--out /tmp/factor_report.md
# 只看环境
python3 scripts/alpha_agent/factor_pipeline.py --check-env参数：
| 参数 | 默认 | 说明 |
|---|---|---|
--direction | 必填 | 挖掘方向/假设，中文优先（见方向建议库） |
--universe | csi300 | csi300/csi500/csi1000/sse50/gem/star/csi800/all_a |
--loops | 3 | 演化轮数，实际以任务详情为准（可能出现归一值） |
--check-env | off | 只跑环境健康检查 |
--no-backtest | off | 演化完成即停，不批量回测 |
--backtest-start | 2025-01-01 | 回测起始日（到当天） |
--backtest-universe | =universe | 回测股票池 |
--explain-top N | 0 | 对 |
--export / --min-ic | off / 0.0 | 对 |
--out | /tmp/rd_agent_factor_report.md | 报告路径 |
--show-log | off | 轮询时打印任务日志 |
管线阶段（脚本自动执行）：
evolve 启动演化 → 拿 task_idtasks/{id} 直到 completed（打印 phase/loop/error）task_id 的因子（metadata.task_id 过滤）factor/{id}/backtest 触发 → 轮询全部 completed|IC| 排序打印排行榜--explain-top 因子 explain（LLM 解读，写入报告）--export 最高分因子--out一个方向跑完约 30–90 分钟（数据管线 + LLM 演化 + 逐因子回测）。
R&D-Agent 是「LLM 提假设 → 逐因子回测」；因子工厂是「系统性批量衍生」：拿 QuantDB 的几百个数值字段
（l1_factors / features_daily）做算子 × 窗口 × 二元组合，全量算 IC/ICIR、相关性去重，产出可训练 parquet。
适合「数据面已知、想从海量式子里筛好货」。两者互补，可都跑。
# 容器内执行（重依赖 duckdb/pandas，QwenPaw 本地 venv 跑不了）
docker exec -w /app quantmind python /app/backend/scripts/factor_factory.py \
--start-date 2025-09-01 --end-date 2026-08-31 --top-n 200
# 冒烟（小样本，先验证环境）
docker exec -w /app quantmind python /app/backend/scripts/factor_factory.py --smokedata/quantcustom/6_ml_datasets/l1_factors/dt=YYYYMMDD/data.parquet（列：symbol(后缀式) + date + OHLCV + 因子 float32）、MANIFEST.csv、PROPOSALS.json。--windows 5,10,20,60、--ops tsrank,tsstd,roc,zscore,delta,decay,slope、--cs-ops csrank,cszscore、--binary-ops csdiff,csratio,tscorr、--top-n 200、--pool-factor 3.0、--corr-threshold 0.85、--jobs 0（自动并行）、--compression zstd、--out。GET /api/v1/alpha-agent/factory-factors（读 MANIFEST，前端因子库加「工厂」徽标，仅展示不回测/训练操作）。QuantDBFactorReader(mode="CUSTOM")（QM_QUANTCUSTOM_DATA_DIR）。quantcustom 小节。BASE=http://127.0.0.1:8000
TOKEN=$(curl -s -X POST $BASE/api/v1/auth/login -H "Content-Type: application/json" \
-d '{"username":"admin","password":"admin123","tenant_id":"default"}' \
| python3 -c "import sys,json; print(json.load(sys.stdin).get('access_token',''))")
AUTH="Authorization: Bearer $TOKEN"curl -s -H "$AUTH" "$BASE/api/v1/alpha-agent/universes" # 支持的股票池
curl -s -H "$AUTH" "$BASE/api/v1/alpha-agent/markets" # 支持的市场
curl -s -H "$AUTH" "$BASE/api/v1/alpha-agent/factor-categories" # 挖掘类别参考
curl -s -H "$AUTH" "$BASE/api/v1/alpha-agent/data-summary" # 数据覆盖（日期/股票数）curl -s -X POST "$BASE/api/v1/alpha-agent/evolve" -H "$AUTH" \
--data-urlencode "market=a_share" \
--data-urlencode "universe=csi300" \
--data-urlencode "loop_n=3" \
--data-urlencode "direction=低换手率高动量" \
-w "\nHTTP %{http_code}\n"
# 返回 task_id（后续所有步骤用）curl -s -H "$AUTH" "$BASE/api/v1/alpha-agent/tasks/{task_id}" # 状态 progress/phase/loop/error
curl -s -H "$AUTH" "$BASE/api/v1/alpha-agent/tasks/{task_id}/log" # 实时日志（含失败原因）
curl -s -X POST -H "$AUTH" "$BASE/api/v1/alpha-agent/tasks/{task_id}/cancel"
# 状态机: pending → running → backtesting → completed | failedcurl -s -X POST -H "$AUTH" "$BASE/api/v1/alpha-agent/factors/{factor_id}/backtest?start_date=2025-01-01&end_date=2026-08-21&universe=csi300&data_source=qlib_bin"
# factors 列表里 ic_value/sharpe_ratio/rank_ic 回测完成后回填；status 变 completedcurl -s -X POST -H "$AUTH" "$BASE/api/v1/alpha-agent/factors/{factor_id}/explain" # LLM 解读因子逻辑
curl -s -X POST -H "$AUTH" "$BASE/api/v1/alpha-agent/factors/{factor_id}/export" # 加入生产特征库
curl -s -H "$AUTH" "$BASE/api/v1/alpha-agent/factors" # 全部因子（含本次 task 的）
curl -s -H "$AUTH" "$BASE/api/v1/alpha-agent/stats" # avg_ic/best_sharpe 等优先挖 78 核心因子集之外的空白区（筹码/微观结构/连板情绪/隔夜/资金流持续性）：
1. 连板情绪承继 连板高度递增与涨停回封率，捕捉题材情绪承接力由弱转强的启动票
2. 筹码分布 筹码集中度上行伴随低位换手放大，获利盘充分消化的突破信号
3. 隔夜/日内背离 隔夜收益与日内收益背离，捕捉大单隔夜布局意图
4. 资金流持续性 主力大单净流入的天数持续性与金额强度共振
5. 下行波动偏度 低下行风险与负偏度修正，挖掘低波动异象的非对称变体
6. 量价微观结构 开盘跳空幅度与量能共振，叠加尾盘动量延续
7. 动量质量 趋势斜率 R2 与收益动量叠加，过滤高噪音动量
8. 波动聚簇修正 波动率自相关的反转信号（高低波动切换）
9. 流动性衰减 换手率衰减速度与跌幅对比
10. 行业相对强度 个股相对行业指数 20 日超额与行业轮动方向一致每批建议跑 1–3 个方向（串行排队），避免队列过载。
completed（非 failed）ic_value 非空）explain，解读与 direction 假设一致（非噪声）export（日志有导出记录）| 现象 | 根因 | 处理 |
|---|---|---|
timeout: failed to run command 'python' + conda: not found | 容器无 conda，RD-Agent 需 rdagent4qlib env | conda shim 挂载 /usr/local/bin/conda；宿主机文件记得 chmod +x |
Message is not fully defined / ChatCompletionReasoningSummaryTextBlock is not defined | litellm 1.97 + pydantic 2.13 冲突 | docker/litellm_sitecustomize.py 挂载为 sitecustomize |
Authentication Fails ... api key is invalid | DEEPSEEK_API_KEY 失效 | 换 key 到 .env，docker compose up -d quantmind recreate 才生效 |
因子 status=pending 且 ic_value=null | 还没跑回测 | 触发 backtest（pipeline /--no-backtest 时更是如此） |
| 任务秒 failed | 先看 tasks/{id}/log 尾部具体错误 | 常见上面两类，按表修 |
engine upstream unavailable(503) | 回测并发把 engine 挤忙 | 稍等重试；少并行任务 |
传 universe=all_a 详情显示 csi300 | 后端对部分池归一 | 以任务详情 universe 为准 |
scripts/alpha_agent/factor_pipeline.pydocker/conda-shim、docker/litellm_sitecustomize.py（compose 挂载固化）scripts/alpha_agent/run_rd_agent.pybackend/scripts/factor_factory.py（+ backend/scripts/alpha_library_factors.py 复用算子/写盘）© 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
Just SKILL.md in skills/rd-agent-factor-mining of qusong0627/QuantMind.
Open the folder on GitHubat commit 2e93d9a
Rd Agent Factor Mining 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Rd Agent Factor Mining this skillqusong0627/QuantMind | 1.7k | — | ~2k | Automated safety check: Notes | AGPL-3.0 | |
| Deepseek Harness Dockerrunzhliu/deepseek-harness-docker | 110 | — | ~2.7k | Automated safety check: Notes | MIT | |
| Operate Pi Dispatchedgehero/pi-dispatch | 178 | — | ~7.6k | Automated safety check: Notes | MIT | |
| Cloud TestFreakStudioCN/mpy-hardware-extension | 132 | — | ~1.7k | Automated safety check: Pass | Custom licence | |
| Weave Router Local Testingweave-os/router | 5.6k | — | ~3.1k | Automated safety check: Notes | Apache-2.0 | |
| GreptimeDB Dev Docker ImageGreptimeTeam/greptimedb | 6.7k | — | ~4k | Automated safety check: Notes | Apache-2.0 |
runzhliu/deepseek-harness-docker
Deploy, configure, verify, upgrade, and troubleshoot DeepSeek Harness with the community Docker, Docker Compose, rootless Podman, and Helm runtime, including the built-in Chromium/noVNC browser…
edgehero/pi-dispatch
How to operate a pi-dispatch deployment through its tools, and how to use the operator-confirm gates on the write tools (changing limits, adding/editing/deleting triggers).
FreakStudioCN/mpy-hardware-extension
用本地前端插件连云端后端做端到端测试 / test the local VS Code extension against the deployed cloud backend.
weave-os/router
Stands up the Weave model router in Docker Compose and drives it with claude -p against a real or mocked upstream to reproduce and verify routing and streaming behavior.
GreptimeTeam/greptimedb
Packages a locally built GreptimeDB debug binary into a development-only Docker image for local-cluster testing, with an optional push to a dev registry.
AMAP-ML/LongHorizon-Harness
Reproduce CUA-Harness experiments on WeaveBench from a GitHub checkout.
qusong0627/QuantMind
Produces a post-market review report for the China A-share market from local QuantDB data, news sentiment and model signals, ending in a next-day direction call.
qusong0627/QuantMind
Queries Futu quotes, options, fundamentals and accounts and places orders through the Futu OpenAPI Python SDK, defaulting to simulated trading.
qusong0627/QuantMind
Turns a plain-language model training request into a validated QuantMind training config file that can be imported from the Model Training page.
qusong0627/QuantMind
Covers the Tiger Brokers OpenAPI Python SDK for market data, stock, futures and options trading, push subscriptions, a CLI and an MCP server, defaulting to paper trading.
qusong0627/QuantMind
Guides an agent through the Tiger Brokers OpenAPI C++ SDK for build setup, market data, orders and real-time push, defaulting to paper trading.
qusong0627/QuantMind
Guides building C# and .NET apps on the Tiger Brokers OpenAPI SDK: setup, market data, orders, accounts, options and real-time push, defaulting to paper trading.
Categories
RD-Agent A股因子挖掘端到端流水线：环境 preflight → 启动演化 → 轮询完成 → 批量回测评估 → IC/Sharpe 排序 → explain 解读 → export 入库 → Markdown 报告。在 QuantBot / Claude Code 中挖因子时使用，一条命令跑完整流程。触发词：挖因子、因子挖掘、挖新因子、因子演化、RD-Agent、alpha…. Rd Agent Factor Mining is an agent skill from qusong0627/QuantMind.
Rd Agent Factor Mining fits situations like: devOps & Cloud work in your project.
Run `npx skills add qusong0627/QuantMind --skill rd-agent-factor-mining -a claude-code`. Or copy the skill folder (skills/rd-agent-factor-mining in qusong0627/QuantMind) into .claude/skills/rd-agent-factor-mining in your project. Claude Code loads it when a task matches its description.
Run `npx skills add qusong0627/QuantMind --skill rd-agent-factor-mining -a codex`. Or copy the skill folder (skills/rd-agent-factor-mining in qusong0627/QuantMind) into .agents/skills/rd-agent-factor-mining in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add qusong0627/QuantMind --skill rd-agent-factor-mining -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rd-agent-factor-mining, .gemini/skills/rd-agent-factor-mining, .github/skills/rd-agent-factor-mining and .opencode/skills/rd-agent-factor-mining in your project.
Going by SKILL.md and its folder, Rd Agent Factor Mining needs the command-line tools its instructions call (curl, python3 and docker) and credentials named DEEPSEEK_API_KEY. Our summary lists: Python 3; Docker; A credential in DEEPSEEK_API_KEY.
SKILL.md contains no URLs. Its commands use curl and docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Rd Agent Factor Mining 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.
About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Rd Agent Factor Mining: Deepseek Harness Docker (runzhliu/deepseek-harness-docker, 110 stars), Operate Pi Dispatch (edgehero/pi-dispatch, 178 stars), Cloud Test (FreakStudioCN/mpy-hardware-extension, 132 stars) and Weave Router Local Testing (weave-os/router, 5.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
qusong0627 (a GitHub user) maintains it in qusong0627/QuantMind, which has 1,717 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on October 8, 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.
