메뉴
多智能体A股深度研究框架。当用户要求分析A股上市公司、产业链研究、个股深度报告、行业比较、投资价值评估时激活。支持：单只股票全面分析（财务/新闻/情绪/技术/风险）、多智能体辩论（看涨vs看跌）、产业链上下游拆解、事实核查、Dashboard部署。数据来源：akshare（A股/港股）、yfinance（美股）、网络搜索（研报/新闻）。适合A股投资者、量化研究员、产业链分析师。
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/knownasnaffy/prompthound --skill astock-multiagent-research명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Install and operate local NVIDIA Parakeet ASR for OpenClaw with an OpenAI-compatible transcription API on Ubuntu/Linux and macOS (Intel/Apple Silicon). Use when the user wants private/local speech-to-text, voice transcription setup, ASR troubleshooting, or OpenClaw voice stack configuration with Parakeet (and optional Whisper fallback).
Collect, refresh, normalize, and analyze the user's own Douban history for taste analysis and recommendation reasoning. Use when the task involves the user's own Douban shelves, ratings, tags, comments, reviews, or recent activity, especially when you need to decide whether local cache is fresh enough, re-crawl logged-in data with cookies, store refreshed results locally, and then analyze them by category.
LoRA fine-tuning pipeline for Stable Diffusion on Apple Silicon — dataset prep, training, evaluation with LLM-as-judge scoring. Use when fine-tuning image generation models for consistent style, custom characters, or domain-specific visuals. Requires Python with torch and diffusers.
SOC 직업 분류 기준
SKILL.md 표시 중
| AIGC | {"ContentProducer":"Minimax Agent AI","ContentPropagator":"Minimax Agent AI","Label":"AIGC","ProduceID":"5f99537637ec8a7284fc6db453befbf1","PropagateID":"5f99537637ec8a7284fc6db453befbf1","ReservedCode1":"30450220459a060a3cd402aa1efd0cedaa919091eac38669a1a684f23a3d11e0bbeefab40221009d2ccb1970d6f6ae56965fa89c64a5ce991462be3af54aa3d944f723b80579d9","ReservedCode2":"3045022072c8a10b604befe2a48fdea3a89439bc7ff3b660ddfa39531dcf9376f3bf2691022100e64cbce383db99fd25a2ea80b7484bbb9e579a3627ad9fc3d5daab163557d93e"} |
| description | 多智能体A股深度研究框架。当用户要求分析A股上市公司、产业链研究、个股深度报告、行业比较、投资价值评估时激活。支持：单只股票全面分析（财务/新闻/情绪/技术/风险）、多智能体辩论（看涨vs看跌）、产业链上下游拆解、事实核查、Dashboard部署。数据来源：akshare（A股/港股）、yfinance（美股）、网络搜索（研报/新闻）。适合A股投资者、量化研究员、产业链分析师。 |
| name | astock-multiagent-research |
单只股票分析：
用户: "帮我研究 600519 茅台" / "分析润泽科技300442" / "中际旭创值得买吗"
→ 触发完整6步工作流（见下）
产业链分析：
用户: "分析AI算力产业链" / "商业航天产业链哪个环节最值得关注"
→ 读取 references/industry-chain.md，触发产业链专项框架
快速概览（仅基本面+新闻）：
用户: "给我简单看下000977浪潮信息最近怎样"
→ 仅启动 fundamentals_analyst + news_analyst 两个子智能体
同时启动4个子智能体（使用 sessions_spawn）：
| 子智能体 | 任务 | 参考提示词 |
|---|---|---|
fundamentals_analyst | 财务报表深度分析、估值、机构预测 | references/agent-prompts.md#fundamentals |
news_analyst | 公司动态、行业新闻、政策 | references/agent-prompts.md#news |
sentiment_analyst | 市场情绪、研报评级变化 | references/agent-prompts.md#sentiment |
technical_analyst | 价格走势、成交量、关键位 | references/agent-prompts.md#technical |
基于Step1报告，并行启动：
bullish_researcher：挖掘增长潜力、竞争优势、价值低估bearish_researcher：识别风险、业绩隐忧、估值泡沫risk_manager：综合投资风险评估（行业/财务/流动性/政策）fact_checker：对关键数据和说法二次验证按 references/report-template.md 格式输出完整研究报告
将报告渲染为交互式HTML Dashboard，使用 deploy 工具部署，返回可访问链接
运行 scripts/fetch_stock_data.py <股票代码> 获取基础数据包。
# 手动调用示例
import akshare as ak
# 公司基本信息（A股）
info = ak.stock_individual_info_em(symbol="300442")
# 三大财务报表
balance = ak.stock_balance_sheet_by_report_em(symbol="300442")
income = ak.stock_profit_sheet_by_report_em(symbol="300442")
cashflow = ak.stock_cash_flow_sheet_by_report_em(symbol="300442")
# 财务指标（ROE/毛利率等）
indicator = ak.stock_financial_analysis_indicator(symbol="300442")
# 机构持仓
fund_hold = ak.stock_report_fund_hold(symbol="300442")
# 美股
import yfinance as yf
ticker = yf.Ticker("AAPL")
info = ticker.info
子代码格式：沪市6开头用"6xxxxx"，深市/创业板用"0xxxxx"/"3xxxxx"，北交所用"8xxxxx"。
详见 references/report-template.md。核心结构：
references/industry-chain.md