intellectia-stock-screener
Get stock screener list data from Intellectia API (no auth) and summarize results.
Best use case
intellectia-stock-screener is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Get stock screener list data from Intellectia API (no auth) and summarize results.
Teams using intellectia-stock-screener should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/intellectia-stock-screener/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How intellectia-stock-screener Compares
| Feature / Agent | intellectia-stock-screener | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Get stock screener list data from Intellectia API (no auth) and summarize results.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Intellectia Stock Screener
Base URL: `https://api.intellectia.ai`
## Endpoint
- `GET /gateway/v1/stock/screener-list`
Full URL:
- `https://api.intellectia.ai/gateway/v1/stock/screener-list`
## Query parameters
- `symbol_type` (int): Asset type `0=stock 1=etf 2=crypto`
- `period_type` (int): Period `0=day 1=week 2=month`
- `trend_type` (int): Trend `0=bullish 1=bearish`
- `profit_asc` (bool): Sort by profit ascending (`true` = small → large)
- `market_cap` (int): Market cap filter
- `0=any`
- `1=micro <300M`
- `2=small 300M-2B`
- `3=mid 2B-10B`
- `4=large 10B-200B`
- `5=mega >200B`
- `price` (int): Price filter
- `0=any`
- `1=<5`
- `2=<50`
- `3=>5`
- `4=>50`
- `5=5-50`
- `page` (int): Page number (example: 1)
- `size` (int): Page size (example: 20)
## Response (200)
Example response (shape):
```json
{
"ret": 0,
"msg": "",
"data": {
"list": [
{
"code": "BKD.N",
"symbol": "BKD",
"symbol_type": 0,
"name": "Brookdale Senior Living Inc",
"logo": "https://intellectia-public-documents.s3.amazonaws.com/image/logo/BKD_logo.png",
"pre_close": 14.5,
"price": 15,
"change_ratio": 3.45,
"timestamp": "1769749200",
"simiar_num": 10,
"probability": 80,
"profit": 5.27,
"klines": [{ "close": 15, "timestamp": "1769749200" }],
"trend_list": [
{
"symbol": "BKD",
"symbol_type": 0,
"is_main": true,
"list": [{ "change_ratio": 5.27, "timestamp": "1730260800", "close": 16 }]
}
],
"update_time": "1769806800"
}
],
"total": 3,
"detail": {
"cover_url": "https://d159e3ysga2l0q.cloudfront.net/image/cover_image/stock-1.png",
"name": "Stocks Bullish Tomorrow",
"screener_type": 1011,
"params": "{}",
"desc": "..."
}
}
}
```
### Field reference
Top-level:
- `ret` (int): Status code (typically `0` means success)
- `msg` (string): Message (empty string when OK)
- `data` (object): Payload
`data`:
- `data.list` (array): Result rows
- `data.total` (int): Total number of rows
- `data.detail` (object): Screener metadata
Each item in `data.list`:
- `code` (string): Full instrument code (may include exchange suffix, e.g. `BKD.N`)
- `symbol` (string): Ticker symbol (e.g. `BKD`)
- `symbol_type` (int): Asset type (`0=stock 1=etf 2=crypto`)
- `name` (string): Display name
- `logo` (string): Logo URL
- `pre_close` (number): Previous close price
- `price` (number): Current price
- `change_ratio` (number): Percent change vs previous close
- `timestamp` (string): Quote timestamp (Unix seconds)
- `simiar_num` (int): Similarity count (as returned by API; spelling kept as-is)
- `probability` (int): Model confidence (0-100)
- `profit` (number): Predicted/expected return (as returned by API)
- `klines` (array): Price series
- `klines[].close` (number): Close price
- `klines[].timestamp` (string): Unix seconds
- `trend_list` (array): Trend comparison series
- `trend_list[].symbol` (string): Symbol for the series (may be empty for non-main series)
- `trend_list[].symbol_type` (int): Asset type
- `trend_list[].is_main` (bool): Whether this is the main series
- `trend_list[].list` (array): Time points
- `trend_list[].list[].change_ratio` (number): Percent change at that point
- `trend_list[].list[].timestamp` (string): Unix seconds
- `trend_list[].list[].close` (number): Close price at that point
- `update_time` (string): Last update time (Unix seconds)
`data.detail`:
- `cover_url` (string): Cover image URL
- `name` (string): Screener title
- `screener_type` (int): Screener type ID
- `params` (string): Serialized params (often JSON string)
- `desc` (string): Screener description
- `num` (int, optional): As returned by API (may be absent)
## Examples
### cURL
```bash
curl -sS "https://api.intellectia.ai/gateway/v1/stock/screener-list?symbol_type=0&period_type=0&trend_type=0&profit_asc=false&market_cap=0&price=0&page=1&size=20"
```
### Python (requests)
```bash
python3 - <<'PY'
import requests
base_url = "https://api.intellectia.ai"
params = {
"symbol_type": 0,
"period_type": 0,
"trend_type": 0,
"profit_asc": False,
"market_cap": 0,
"price": 0,
"page": 1,
"size": 20,
}
r = requests.get(f"{base_url}/gateway/v1/stock/screener-list", params=params, timeout=30)
r.raise_for_status()
payload = r.json()
print("ret:", payload.get("ret"))
print("msg:", payload.get("msg"))
data = payload.get("data") or {}
rows = data.get("list") or []
print("total:", data.get("total"))
for row in rows[:10]:
print(row.get("symbol"), row.get("price"), row.get("change_ratio"), row.get("probability"), row.get("profit"))
PY
```
## Notes
- No authentication required.
- If you see rate limits, reduce `size` and add backoff/retry in client code.Related Skills
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