Access US stock market data through eng0's data proxy service.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmarket-dataExecute the skills CLI command in your project's root directory to begin installation:
Fetches market-data from eng0ai/eng0-template-skills 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 market-data. Access via /market-data 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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Access US stock market data through eng0's data proxy service.
https://api.eng0.ai/api/data
| 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 |
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 |
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
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
}
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"
}'
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"
}'
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"
}'
curl -X POST https://api.eng0.ai/api/data/stocks/news \
-H "Content-Type: application/json" \
-d '{
"ticker": "GOOGL",
"limit": 20
}'
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(
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
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in market-data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for market-data matched our evaluation — installs cleanly and behaves as described in the markdown.
market-data has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: market-data is the kind of skill you can hand to a new teammate without a long onboarding doc.
market-data fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for market-data matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in market-data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend market-data for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
market-data is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added market-data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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