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 secretarygit 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/secretary)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/secretary"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/secretary/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/secretary"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/secretary.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 21 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00021 | $0.00934 |
| Opus 5 | $0.00010 | $0.00467 |
| Sonnet 5 | $0.00004 | $0.00187 |
| Haiku 4.5 | $0.00002 | $0.00093 |
Grade A, and why
secretary 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 10d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Secretary Skill
Workflow patterns and operational details for the secretary tools. Basic tool signatures are in the tool descriptions — this covers what they don't.
Operational Details
HITL approval
These actions pause for user confirmation before executing:
manage_workspaces(action="create"|"delete"|"stop")ptc_agent(...)— always, before dispatchmanage_threads(action="delete")
These run immediately (no approval):
manage_workspaces(action="list")manage_threads(action="list"|"get_output")agent_output(...)
ptc_agent dispatch
ptc_agent is asynchronous — it dispatches the question and returns immediately. The PTC agent runs in the background with full code execution, charts, and financial data tools.
Return: { success, workspace_id, thread_id, status: "dispatched", report_back }
- Omit
workspace_id→ auto-creates a new workspace (blocks ~8-10s for sandbox init) - Pass
workspace_id→ dispatches to existing workspace (new thread) - Pass
thread_id→ continues an existing conversation (overridesworkspace_id) report_back=True(default) → when PTC completes, you'll automatically receive the results and should summarize them for the userreport_back=False→ fire-and-forget; the user will check results in the workspace themselves- The returned
report_backfield is authoritative, not an echo of your request: it can come backfalseeven when you asked fortrue(degraded backend). If it does, results will NOT arrive automatically — poll withagent_output. - Concurrency caps (report-back dispatches only): at most 5 pending analyses per conversation and 10 per user. Over the cap the dispatch fails with an error starting "too many concurrent analyses" — wait for one to finish, or dispatch with
report_back=False.
Use the returned thread_id with agent_output to check progress later (only needed when the returned report_back is false).
agent_output
Return: { text, status, thread_id, workspace_id }
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.
- 10d ago First seen · 82 lines · 21 tokens per session scan A a39e0ce406e8
secretary is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,730 stars, last pushed yesterday), licensed Apache-2.0. It adds 21 tokens to every session and 934 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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