LangAlpha is an agent workspace for researching financial markets and supporting investment decisions through persistent research, news analysis, and parallel subagents. It is for investors who want to develop and update trading theses over time, including generating long-short pair-trade ideas. The catalogue entries provide the skills, instructions, MCP servers, and plugin that make up its agent workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ginlix-ai/LangAlpha --skill web-scrapinggit clone --depth 1 https://github.com/ginlix-ai/LangAlphaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ginlix-ai/langalpha/web-scraping)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/web-scraping"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/web-scraping.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.02191 |
| Opus 5 | $0.00026 | $0.01095 |
| Sonnet 5 | $0.00010 | $0.00438 |
| Haiku 4.5 | $0.00005 | $0.00219 |
Grade A, and why
web-scraping scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Web Scraping
Overview
Two ways to scrape in the sandbox:
- MCP tools (
scrape_page,scrape_pages) — recommended for straight "give me this page's content". Synchronous, return dicts. - Direct Scrapling Python API — for CSS/XPath selectors, sessions, logins, and multi-page spiders. Async, returns Page objects with
.css()/.xpath().
Quick fetches can run inline via ExecuteCode. For spiders, multi-URL crawls, or anything you'll iterate on, write the scraper to work/<task_name>/scraper.py and run it via Bash — edit-and-rerun beats resubmitting code.
MCP Tools
Import from tools.scrape. Synchronous — no await.
from tools.scrape import scrape_page, scrape_pages
Signatures
scrape_page(url: str, mode: str = "fast", extraction: str = "markdown",
timeout: float = 30.0, solve_cloudflare: bool = False) -> dict
scrape_pages(urls: list[str], mode: str = "fast", extraction: str = "markdown",
timeout: float = 30.0, solve_cloudflare: bool = False) -> dict
Parameters
| Param | Default | Notes |
|---|---|---|
mode |
"fast" |
"fast" plain HTTP · "browser" JS rendering · "stealth" bot-protected sites |
extraction |
"markdown" |
"markdown" (article text, cleaned) · "html" (raw) · "text" (plain) |
timeout |
30.0 |
Per-fetch seconds, 1–60 — seconds in every mode, not ms |
solve_cloudflare |
False |
Only meaningful with mode="stealth" |
urls |
— | scrape_pages only; max 10 per call |
Escalate modes only as needed: start fast, go to browser when the page needs JavaScript, stealth when you're getting blocked, and add solve_cloudflare=True only if stealth still returns a challenge page.
Return shape
scrape_page returns a flat dict:
{
"url": "https://example.com",
"status": 200,
"title": "Example Domain",
"content": "# Example Domain\n\nThis domain is for use in...", # str
"extraction": "markdown",
"mode": "fast",
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago Changed 9b3863eab657
- 8d ago First seen · 258 lines · 51 tokens per session scan A 237c6009b5e0
web-scraping is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,726 stars, last pushed yesterday), licensed Apache-2.0. It adds 51 tokens to every session and 2,191 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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