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.
Borrowing it
Nothing to install: this file belongs to ginlix-ai/LangAlpha. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ginlix-ai/LangAlpha/main/AGENTS.mdgit 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/instructions/ginlix-ai/langalpha/agents-md)<a href="https://agentmods.dev/instructions/ginlix-ai/langalpha/agents-md"><img src="https://agentmods.dev/badge/instructions/ginlix-ai/langalpha/agents-md/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/instructions/ginlix-ai/langalpha/agents-md"><img src="https://agentmods.dev/badge/instructions/ginlix-ai/langalpha/agents-md.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 | $0.05348 | $0.05348 |
| Opus 5.5 | $0.02139 | $0.02139 |
| Sonnet 5.5 | $0.01070 | $0.01070 |
| Haiku 4.5 | $0.00535 | $0.00535 |
Grade A, and why
LangAlpha AGENTS.md 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
langalpha
Core AI agent service of the Ginlix financial research platform. One agent, the PTC agent, wired with a full Daytona, E2B or Docker sandbox, or the user's own machine over SSH, and the complete toolset (code execution, MCP financial-data tools, charts, subagent orchestration), in one of two roles per turn. PTC = Programmatic Tool Calling (see PTC pattern); it names the technique, not a product.
- Analyst (default): the agent inside a workspace. Does the R&D and produces the deliverables, which stay in that workspace.
- Chief of Staff: the agent for work in no particular workspace (named "Chief of Staff" in the web UI; the chat apps still offer it as "All workspaces"). Runs in a
Homefolder on the user's computer beside the workspace folders, where everything it produces is kept. It answers quick questions and anything the workspaces already hold itself, reading their folders and past threads, and hands new work to the workspace's analyst, which reports back when done.
Single source of truth for AI coding agents.
CLAUDE.mdimports this via@AGENTS.md; Codex/Cursor/Copilot read it directly. Edit here, not there.
Common Commands
make up # full stack (postgres, redis, backend, frontend); auto-detects sandbox
make config # interactive setup wizard (LLM, data, sandbox, search)
make help # all targets (deploy, prod-up, test-sandbox, …)
uv run python server.py --reload # backend only, port 8000 (needs DB + Redis)
cd web && pnpm dev # frontend dev server, port 5173
make lint # Ruff (backend) + ESLint (frontend)
# Backend tests — default run is unit only (integration/slow/regression deselected in pyproject)
uv run pytest tests/unit/ -v --tb=short
uv run pytest -m integration # hits real APIs — needs DB + Redis + API keys
uv run pytest -m regression # locks live market-data behavior — needs a running server + live providers
cd web && pnpm test # Vitest; pnpm test:e2e = Playwright; pnpm typecheck = tsc -b
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 · +47 tokens per session cd53c71322d9
- 7d ago Changed · +243 tokens per session 0139c8077bcf
- 12d ago Changed · +1 lines · +352 tokens per session 0383943d91ca
- 13d ago Changed · +15 tokens per session 3a0514da6543
- 14d ago Changed · +4 lines · +141 tokens per session 028602a11d57
- 15d ago Changed · +43 tokens per session 03683ed71b40
- 16d ago Changed · +1 lines · +542 tokens per session d5d817ec4303
- 19d ago Changed · +10 tokens per session ed43fb25a37f
- 20d ago Changed · -3 lines · -106 tokens per session f2d076e6f624
- 25d ago First seen · 102 lines · 4,061 tokens per session scan A b914294821d3
LangAlpha AGENTS.md is an instructions file published in the GitHub repository ginlix-ai/LangAlpha (2,244 stars, last pushed yesterday), licensed Apache-2.0. It adds 5,348 tokens to every session, about $0.0214 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 2026-08-30.
LangAlpha AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| LangAlpha AGENTS.mdginlix-ai/LangAlpha | Instructions file | 2.2k | yesterday | Apache-2.0 | A | 5,348 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |