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 automationgit 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/automation)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/automation"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/automation.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 | $0.00013 | $0.02207 |
| Opus 5 | $0.00006 | $0.01104 |
| Sonnet 5 | $0.00003 | $0.00441 |
| Haiku 4.5 | $0.00001 | $0.00221 |
Grade A, and why
automation 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 8d 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automation Skill
This skill provides 3 tools for creating and managing scheduled automations:
check_automations- List all or inspect a specific automationcreate_automation- Create a new scheduled automationmanage_automation- Update, pause, resume, trigger, or delete automations
You should call these tools directly instead of using ExecuteCode tool.
Before Creating an Automation
Always confirm with the user before calling create_automation. Automations run autonomously on a schedule, so getting the details right matters. If the user's request is unclear or underspecified, ask to clarify:
- Schedule — "Every morning" is ambiguous. Confirm the exact time and days (e.g. "Weekdays at 9 AM in your timezone?").
- Thread strategy — If the task involves ongoing analysis or follow-ups, ask whether they want results in a fresh thread each time, a single persistent thread, or the current conversation.
- Instruction — The instruction runs without further user input. If the user gives a vague prompt like "check my portfolio", refine it: what tickers? what metrics? what format?
- Delivery — If the user hasn't mentioned how they want to receive results, ask if they want delivery (e.g. Slack) or just in-app.
Summarize what you're about to create and get a "yes" before calling the tool.
Tool 1: check_automations
List all automations or inspect a specific one with execution history.
| Parameter | Type | Required | Description |
|---|---|---|---|
automation_id |
str | No | Automation ID to inspect. Omit to list all. |
Examples
# List all automations
check_automations()
# Inspect a specific automation (includes last 5 executions)
check_automations(automation_id="abc-123")
Tool 2: create_automation
Create a new scheduled automation.
| Parameter | Type | Required | Description |
|---|---|---|---|
name |
str | Yes | Short name for the automation |
instruction |
str | Yes | The prompt the agent will execute on each run |
schedule |
str | Yes | Cron expression or ISO datetime (see below) |
description |
str | No | Optional description |
thread |
str | No | "new" (default), "persistent", or "current" (see Thread Strategy) |
delivery |
str | No | Comma-separated delivery methods (e.g. "slack") |
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.
- 8d ago First seen · 254 lines · 13 tokens per session scan A f26239b40eeb
automation is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,726 stars, last pushed yesterday), licensed Apache-2.0. It adds 13 tokens to every session and 2,207 once invoked, about $0.0001 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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