secretary

secretary is a skill for Claude Code, Codex from ginlix-ai/LangAlpha. It costs 21 tokens per session (934 once invoked), scanned A, original, Apache-2.0.

A workspace and research-management skill for sending analyses to background agents, checking their progress, and managing workspaces and conversation threads.

In plain words
What is it for?
Dispatching analysis tasks, monitoring agents, viewing their outputs, and listing or managing workspaces and threads.
Why use it?
It helps coordinate longer or parallel research work without losing track of running analyses and their results.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Dispatching analysis tasks, monitoring agents, viewing their outputs, and listing or managing workspaces and threads.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ginlix-ai/langalpha/secretary
About the project

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.

ginlix-ai/LangAlpha · 1,730 stars · on GitHub · langalpha.ai

Install

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.

Any agent
npx skills add ginlix-ai/LangAlpha --skill secretary
Clone the repo
git clone --depth 1 https://github.com/ginlix-ai/LangAlpha

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for secretary

README.md
[![agentmods](https://agentmods.dev/badge/skills/ginlix-ai/langalpha/secretary/github.svg)](https://agentmods.dev/skills/ginlix-ai/langalpha/secretary)
Your own site
<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.

agentmods 80×15 button for secretary

Your own site · 80×15
<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>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 934 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash a39e0ce406e8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

plugins/langalpha_service/skills/secretary/SKILL.md · 82 lines

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 dispatch
  • manage_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 (overrides workspace_id)
  • report_back=True (default) → when PTC completes, you'll automatically receive the results and should summarize them for the user
  • report_back=False → fire-and-forget; the user will check results in the workspace themselves
  • The returned report_back field is authoritative, not an echo of your request: it can come back false even when you asked for true (degraded backend). If it does, results will NOT arrive automatically — poll with agent_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 }

Read the full file on GitHub · 82 lines

Changes

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.

  1. 10d ago First seen · 82 lines · 21 tokens per session scan A a39e0ce406e8

Subscribe to this mod's changes

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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