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 langalpha-docgit 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/langalpha-doc)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/langalpha-doc"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/langalpha-doc/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/langalpha-doc"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/langalpha-doc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00089 | $0.00950 |
| Opus 5.5 | $0.00036 | $0.00380 |
| Sonnet 5.5 | $0.00018 | $0.00190 |
| Haiku 4.5 | $0.00009 | $0.00095 |
Grade A, and why
langalpha-doc 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 3d 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
How LangAlpha Works
You run on a computer: one sandbox machine that belongs to the user. Each of the user's workspaces on it is a folder, side by side at the computer root; yours is your working directory, so the root is ... A thread is one conversation in one workspace, and a turn is one user message and everything done to answer it. Workspaces on one computer share its disk, OS user, CPUs, /tmp and installed packages, and can read each other's folders.
Reference files
- The user points back at an earlier conversation or at work done in another thread or workspace; a compaction summary; a background task's steps; a tool result saved to a file:
.agents/skills/langalpha-doc/references/history.md. - Where things live, how a path resolves, why Grep or Glob missed a file, memory and memo storage, what the user sees in the file panel:
.agents/skills/langalpha-doc/references/files.md. - Processes, output and timeouts, CPUs and disk, installed packages, what survives a stop or a rebuild, data-server tools from code, secrets:
.agents/skills/langalpha-doc/references/computer.md. - Installing, writing or changing a skill, here or for every workspace:
.agents/skills/langalpha-doc/references/skills.md. - The user wants a new data source, MCP server, brokerage connection or plugin, a server needs a key, or a server you expected is missing:
.agents/skills/langalpha-doc/references/plugins.md. - Talking with the user in a chat app, or sending a message or file to Slack, Discord, Telegram, iMessage or Feishu:
.agents/skills/langalpha-doc/references/chat.md.
Traps
- Every ExecuteCode and Bash call is a fresh process started in your workspace folder: Python variables,
cdandexportdo not carry to the next call. - When ExecuteCode fails you get its stderr, not what it printed before the crash.
- The file tools read a leading slash as your workspace folder;
open()and Bash read it as the real filesystem root. Glob, Grep and Write print workspace files with a leading slash (/task/file.md), so drop it before pasting a path into code. - Grep skips hidden and git-ignored files and folders,
.agents/included (.agents/memory/aside), unlesspathpoints inside one. - Only workspace folders are backed up, and not their virtual environments. Installed packages, the computer root and
/tmpsurvive a stop but not a rebuild of the computer. - A turn is written to its thread's transcript when it completes, so do not look there for the turn in progress.
- Memory, memos, the profile, workflows and automations are held by the server. Bash and code reach them only through the file mount, where a save the server refuses shows under NOT SAVED in the result, not in the exit status. Without the mount, a command naming a memory, memo or automations path is refused before it runs. The chat-app settings in
.agents/user/channels/are file tools only: a command naming them is always refused. - In a markdown file, an image path resolves from the workspace folder, not from the file's own folder.
plt.show()output goes nowhere. Save charts withsavefiginto<task>/charts/and link the file.
What ships with it
7 files 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.
- 3d ago Changed 0ad9833d79c8
- 4d ago Changed c5add3cba915
- 5d ago Changed · +7 lines · +16 tokens per session 6c7a4302f45d
- 14d ago First seen · 29 lines · 73 tokens per session scan A 82ff23adfc5a
langalpha-doc is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,820 stars, last pushed today), licensed Apache-2.0. It adds 89 tokens to every session and 950 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-03.
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