market-data▌
eng0ai/eng0-template-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Access US stock market data through eng0's data proxy service.
Market Data API
Access US stock market data through eng0's data proxy service.
Base URL
https://api.eng0.ai/api/data
Data Coverage
- All US stock tickers
- 5 years of historical data
- 100% market coverage
- 15-minute delayed quotes
Available Endpoints
| Endpoint | Purpose |
|---|---|
POST /stocks/bars |
OHLCV price bars (1min to 1week intervals) |
POST /stocks/news |
News articles with sentiment analysis |
POST /stocks/details |
Company information and market cap |
GET /schema |
API schema discovery |
Get Price Bars
Retrieve OHLCV (Open, High, Low, Close, Volume) bars for a stock.
curl -X POST https://api.eng0.ai/api/data/stocks/bars \
-H "Content-Type: application/json" \
-d '{
"ticker": "AAPL",
"interval": "1day",
"from": "2024-12-01",
"to": "2024-12-31"
}'
Parameters:
| Name | Type | Required | Description |
|---|---|---|---|
ticker |
string | Yes | Stock symbol (e.g., AAPL, MSFT) |
interval |
string | Yes | 1min, 5min, 15min, 30min, 1hour, 4hour, 1day, 1week |
from |
date | Yes | Start date (YYYY-MM-DD) |
to |
date | Yes | End date (YYYY-MM-DD) |
Response:
{
"ticker": "AAPL",
"count": 21,
"bars": [
{
"t": "2024-12-02T05:00:00.000Z",
"o": 237.27,
"h": 240.79,
"l": 237.16,
"c": 239.59,
"v": 48137103,
"vw": 239.4992,
"n": 469685
}
]
}
Response Fields:
| Field | Description |
|---|---|
t |
Timestamp (ISO 8601 UTC) |
o |
Open price |
h |
High price |
l |
Low price |
c |
Close price |
v |
Volume |
vw |
Volume-weighted average price |
n |
Number of transactions |
Get News
Retrieve financial news articles with sentiment analysis.
curl -X POST https://api.eng0.ai/api/data/stocks/news \
-H "Content-Type: application/json" \
-d '{
"ticker": "TSLA",
"limit": 5
}'
Parameters:
| Name | Type | Required | Description |
|---|---|---|---|
ticker |
string | Yes | Stock symbol |
limit |
number | No | Max articles (default: 10, max: 100) |
Response:
{
"count": 5,
"articles": [
{
"title": "Tesla Stock Rises on Strong Delivery Numbers",
"description": "Tesla reported better-than-expected Q4 deliveries...",
"author": "John Smith",
"publisher": "Reuters",
"publishedAt": "2025-01-06T14:30:00Z",
"url": "https://...",
"tickers": ["TSLA"],
"keywords": ["electric vehicles", "deliveries"],
"sentiment": "positive",
"sentimentReasoning": "Article discusses strong delivery numbers and positive market reaction."
}
]
}
Sentiment Values: positive, negative, neutral
Get Company Details
Retrieve company information for a stock ticker.
curl -X POST https://api.eng0.ai/api/data/stocks/details \
-H "Content-Type: application/json" \
-d '{"ticker": "AAPL"}'
Parameters:
| Name | Type | Required | Description |
|---|---|---|---|
ticker |
string | Yes | Stock symbol |
Response:
{
"ticker": "AAPL",
"name": "Apple Inc.",
"description": "Apple Inc. designs, manufactures, and markets smartphones...",
"market": "stocks",
"primaryExchange": "XNAS",
"type": "CS",
"currencyName": "usd",
"marketCap": 3949128102780,
"listDate": "1980-12-12",
"sicDescription": "ELECTRONIC COMPUTERS",
"homepage": "https://www.apple.com",
"totalEmployees": 164000
}
Common Workflows
Get Last 30 Days of Daily Prices
curl -X POST https://api.eng0.ai/api/data/stocks/bars \
-H "Content-Type: application/json" \
-d '{
"ticker": "NVDA",
"interval": "1day",
"from": "2024-12-07",
"to": "2025-01-07"
}'
Get Intraday Data (1-minute bars)
curl -X POST https://api.eng0.ai/api/data/stocks/bars \
-H "Content-Type: application/json" \
-d '{
"ticker": "AAPL",
"interval": "1min",
"from": "2025-01-06",
"to": "2025-01-06"
}'
Get Weekly Bars for 1 Year
curl -X POST https://api.eng0.ai/api/data/stocks/bars \
-H "Content-Type: application/json" \
-d '{
"ticker": "MSFT",
"interval": "1week",
"from": "2024-01-01",
"to": "2025-01-01"
}'
Get Recent News with Sentiment
curl -X POST https://api.eng0.ai/api/data/stocks/news \
-H "Content-Type: application/json" \
-d '{
"ticker": "GOOGL",
"limit": 20
}'
Using with Python
import requests
BASE_URL = "https://api.eng0.ai/api/data"
def get_price_bars(ticker: str, interval: str, from_date: str, to_date: str):
"""Get OHLCV price bars for a stock."""
response = requests.post(
f"{BASE_URL}/stocks/bars",
json={
"ticker": ticker,
"interval": interval,
"from": from_date,
"to": to_date
}
)
return response.json()
def get_news(ticker: str, limit: int = 10):
"""Get news articles with sentiment for a stock."""
response = requests.post(
f"{BASE_URL}/stocks/news",
json={"ticker": ticker, "limit": limit}
)
return response.json()
def get_company_details(ticker: str):
"""Get company information."""
response = requests.post(
How to use market-data 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 market-data
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches market-data from GitHub repository eng0ai/eng0-template-skills 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 market-data. Access the skill through slash commands (e.g., /market-data) 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
4.8★★★★★60 reviews- ★★★★★Diya Tandon· Dec 24, 2024
Useful defaults in market-data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Xiao Farah· Dec 12, 2024
Registry listing for market-data matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Isabella Brown· Dec 4, 2024
market-data has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Alexander Liu· Nov 27, 2024
Keeps context tight: market-data is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Alexander Anderson· Nov 23, 2024
market-data fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Diya Jackson· Nov 15, 2024
Registry listing for market-data matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Xiao Abebe· Nov 3, 2024
Useful defaults in market-data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Xiao Taylor· Oct 22, 2024
I recommend market-data for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aisha Harris· Oct 18, 2024
market-data is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Alexander Zhang· Oct 14, 2024
We added market-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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