akshare▌
succ985/openclaw-akshare-skill · updated Apr 30, 2026
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Real-time and historical financial data for Chinese and Asian markets via AkShare library.
- ›Covers A-shares, Hong Kong stocks, US stocks, futures, funds, and macroeconomic indicators with real-time quotes and historical daily/weekly/monthly data
- ›Supports multiple adjustment modes (forward, backward, or unadjusted) and returns pandas DataFrames for easy processing
- ›Includes macroeconomic data such as GDP, CPI, and PMI for market analysis
- ›Requires implementing custom caching and retry
AkShare - Chinese Financial Data
Overview
AkShare is a free, open-source Python library for accessing Chinese financial market data. This skill provides guidance for fetching data from Chinese exchanges including Shanghai Stock Exchange, Shenzhen Stock Exchange, Hong Kong Exchange, and more.
Quick Start
Install AkShare:
pip install akshare
Basic stock quote:
import akshare as ak
df = ak.stock_zh_a_spot_em() # Real-time A-share data
Stock Data
A-Shares (A股)
Real-time quotes:
# All A-shares real-time data
df = ak.stock_zh_a_spot_em()
# Single stock real-time quote
df = ak.stock_zh_a_spot()
Historical data:
# Historical daily data
df = ak.stock_zh_a_hist(symbol="000001", period="daily", start_date="20240101", end_date="20241231", adjust="qfq")
Stock list:
# Get all A-share stock list
df = ak.stock_info_a_code_name()
Hong Kong Stocks (港股)
Real-time quotes:
df = ak.stock_hk_spot_em()
Historical data:
df = ak.stock_hk_hist(symbol="00700", period="daily", adjust="qfq")
US Stocks (美股)
Real-time data:
df = ak.stock_us_spot_em()
Futures Data (期货)
Real-time futures:
# Commodity futures
df = ak.futures_zh_spot()
Historical futures:
df = ak.futures_zh_hist_sina(symbol="IF0")
Fund Data (基金)
Fund list:
df = ak.fund_open_fund_info_em()
Fund historical data:
df = ak.fund_open_fund_info_em(fund="000001", indicator="单位净值走势")
Macroeconomic Indicators (宏观)
GDP data:
df = ak.macro_china_gdp()
CPI data:
df = ak.macro_china_cpi()
PMI data:
df = ak.macro_china_pmi()
Common Parameters
Period (周期):
daily- 日线weekly- 周线monthly- 月线
Adjustment (复权):
qfq- 前复权hfq- 后复权""- 不复权
Tips
- Data caching: AkShare doesn't cache data, implement your own caching if needed
- Rate limiting: Be mindful of request frequency to avoid being blocked
- Data format: Returns pandas DataFrame, can be easily processed
- Error handling: Network errors may occur, implement retry logic
References
For complete API documentation and advanced usage, see:
- references/akshare_api.md - Detailed API reference
- references/common_functions.md - Commonly used functions
- https://akshare.akfamily.xyz/ - Official documentation
How to use akshare on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add akshare
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches akshare from GitHub repository succ985/openclaw-akshare-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate akshare. Access the skill through slash commands (e.g., /akshare) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★43 reviews- ★★★★★Pratham Ware· Dec 28, 2024
Useful defaults in akshare — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mei Yang· Dec 28, 2024
Solid pick for teams standardizing on skills: akshare is focused, and the summary matches what you get after install.
- ★★★★★Emma Sanchez· Dec 24, 2024
I recommend akshare for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hassan Thompson· Dec 12, 2024
We added akshare from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Zara Malhotra· Nov 23, 2024
akshare reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yash Thakker· Nov 19, 2024
akshare is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zara Brown· Nov 19, 2024
We added akshare from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Emma Desai· Nov 15, 2024
Keeps context tight: akshare is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Li Farah· Nov 3, 2024
Solid pick for teams standardizing on skills: akshare is focused, and the summary matches what you get after install.
- ★★★★★Diya Brown· Oct 22, 2024
akshare has been reliable in day-to-day use. Documentation quality is above average for community skills.
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