Real-time and historical A-stock analysis with natural language routing across market, fundamental, sector, and derivatives data.
Works with
Supports 11 analysis intents: real-time indices, K-line patterns, intraday moves, limit-up/down stats, money flow, fundamentals, margin/dragon-tiger boards, sector rotation, derivatives, funds, and cross-border markets
Four-layer architecture (Router → Service → Analyzer → Formatter) isolates intent parsing, data fetching, metric calculation, and platform-spe
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionakshare-stockExecute the skills CLI command in your project's root directory to begin installation:
Fetches akshare-stock from molezzz/openclaw-stock-skill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate akshare-stock. Access via /akshare-stock in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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目标:在 OpenClaw 中通过自然语言触发 A 股和相关市场分析,输出适配 QQ/Telegram 的紧凑文本。
/Users/molezz/Library/Python/3.9/lib/python3.9/site-packagespython3 skills/akshare-stock/main.py --query "${USER_QUERY}"采用 Router -> Service -> Analyzer -> Formatter 四层结构,便于扩展和维护。
skills/akshare-stock/
SKILL.md
main.py # OpenClaw 调用入口
router.py # 意图识别 + 参数解析
schemas.py # 数据结构定义(dataclass)
formatter.py # QQ/Telegram 输出模板
services/
market_service.py # 大盘/个股行情、K线、分时、涨跌停、资金流
fundamental_service.py# 财务指标、财报、融资融券、龙虎榜
sector_service.py # 行业/概念板块、轮动、板块资金流
cross_service.py # 期货/期权、基金、可转债、港股/美股
analyzers/
kline_analyzer.py # 均线、振幅、涨跌幅、量比等
flow_analyzer.py # 主力净流入、连续性、强弱排序
rotation_analyzer.py # 板块轮动强度、持续性
adapters/
akshare_adapter.py # 封装 akshare 接口,隔离 API 变化
utils/
trading_calendar.py # 交易日判断
symbols.py # 指数/股票/板块别名映射
cache.py # 短缓存(30~120 秒)
main.py 接收自然语言 query。router.py 输出结构化意图:intent + symbols + timeframe + metric + top_n。services/* 拉取原始数据(只做数据获取和轻清洗)。analyzers/* 做指标计算和结论生成。formatter.py 按聊天平台压缩输出(短句、分段、emoji、重点数值)。adapters/akshare_adapter.py。建议采用“关键词 + 正则 + 别名词典”混合方式。
INDEX_REALTIME:实时大盘KLINE_ANALYSIS:历史 K 线INTRADAY_ANALYSIS:分时分析LIMIT_STATS:涨跌停统计MONEY_FLOW:资金流向FUNDAMENTAL:财务指标 / 财报MARGIN_LHB:融资融券 / 龙虎榜SECTOR_ANALYSIS:行业/概念/轮动/板块资金DERIVATIVES:期货/期权FUND_BOND:基金净值 / 可转债HK_US_MARKET:港股 / 美股A股大盘 上证现在多少 沪深300实时贵州茅台近60日K线 宁德时代周线 比亚迪月线复权看下000001分时 平安银行今天分时走势今日涨停统计 跌停家数 连板梯队主力资金流入前十 北向资金 行业资金净流入茅台财务指标 宁德时代最新季报 ROE和毛利率中兴通讯融资融券 今日龙虎榜行业板块涨幅榜 概念轮动 AI板块资金流IF主力合约 300ETF期权 基金净值 可转债行情 腾讯港股 英伟达美股\b\d{6}\b(如 600519)YYYYMMDD / YYYY-MM-DD / 今天/昨日/近N日1m/5m/15m/30m/60m/day/week/month前N(默认 10)前复权/后复权/不复权下面是“功能 -> 推荐数据 -> 分析输出”的落地框架(接口以 akshare 当前版本为准,实际以 adapter 层统一封装)。
指数点位 + 涨跌幅 + 市场情绪一句话。核心指标摘要 + 同比/环比 + 风险提示。强势板块Top3 + 轮动结论 + 次日观察点。说明:以下为可直接落地的最小框架,不含完整业务细节。
main.py#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
from router import parse_query
from services.market_service import MarketService
from services.fundamental_service import FundamentalService
from services.sector_service import SectorService
from services.cross_service import CrossService
from formatter import render_output
def dispatch(intent_obj):
intent = intent_obj.intent
if intent in {"INDEX_REALTIME", "KLINE_ANALYSIS", "INTRADAY_ANALYSIS", "LIMIT_STATS", "MONEY_FLOW"}:
data = MarketService().handle(intent_obj)
elif intent in {"FUNDAMENTAL", "MARGIN_LHB"}:
data = FundamentalService().handle(intent_obj)
elif intent == "SECTOR_ANALYSIS":
data = SectorService().handle(intent_obj)
elif intent in {"DERIVATIVES", "FUND_BOND", "HK_US_MARKET"}:
data = CrossService().handle(intent_obj)
else:
data = {"ok": False, "error": "未识别请求,请补充标的或时间范围"}
return data
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--query", required=True, help="自然语言请求")
parser.add_argument("--platform", default="qq", choices=["qq", "telegram"])
args = parser.parse_args()
intent_obj = parse_query(args.query)
result = dispatch(intent_obj)
text = render_output(intent_obj, result, platform=args.platform)
print(text)
if __name__ == "__main__":
main()
router.pyfrom dataclasses import dataclass, field
import re
@dataclass
class IntentObj:
intent: str
symbols: list = field(default_factory=list)
timeframe: str = "day"
days: int = 60
top_n: int = 10
date: str = ""
raw_query: str = ""
def parse_query(query: str) -> IntentObj:
q = query.strip()
obj = IntentObj(intent="INDEX_REALTIME", raw_query=q)
# 1) intent
if any(k in q for k in ["K线", "日线", "周线", "月线"]):
obj.intent = "KLINE_ANALYSIS"
elif "分时" in q:
obj.intent = "INTRADAY_ANALYSIS"
elif any(k in q for k in ["涨停", "跌停", "连板"]):
obj.intent = "LIMIT_STATS"
elif "资金" in q:
obj.intent = "MONEY_FLOW"
elif any(k in q for k in ["财务", "财报", "ROE", "毛利率"]):
obj.intent = "FUNDAMENTAL"
elif any(k in q for k in ["融资融券", "龙虎榜"]):
obj.intent = "MARGIN_LHB"
elif any(k in q for k in ["板块", "行业", "概念", "轮动"]):
obj.intent = "SECTOR_ANALYSIS"
elif any(k in q for k in ["期货", "期权"]):
obj.intent = "DERIVATIVES"
elif any(k in q for k in ["基金", "净值",Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Keeps context tight: akshare-stock is the kind of skill you can hand to a new teammate without a long onboarding doc.
akshare-stock is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
akshare-stock fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend akshare-stock for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added akshare-stock from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
akshare-stock reduced setup friction for our internal harness; good balance of opinion and flexibility.
akshare-stock has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: akshare-stock is focused, and the summary matches what you get after install.
akshare-stock fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added akshare-stock from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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