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 agentmods add skills/ginlix-ai/langalpha/onboardingnpx skills add ginlix-ai/LangAlpha --skill onboardinggit 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/onboarding)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/onboarding"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/onboarding.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.02674 |
| Opus 5 | $0.00010 | $0.01337 |
| Sonnet 5 | $0.00004 | $0.00535 |
| Haiku 4.5 | $0.00002 | $0.00267 |
Grade A, and why
onboarding 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 6d 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboarding Skill
Purpose
Help new users set up their investment profile through a natural, conversational flow. The agent gathers preferences and stores them as rich, descriptive text that future conversations can reference for personalized advice.
This skill provides 5 tools:
get_user_data- Read user dataupdate_user_data- Create or update user dataremove_user_data- Delete user datamanage_workspaces- Create workspaces (via action="create")ptc_agent- Dispatch a research question to a workspace
You should call these tools directly instead of using ExecuteCode tool.
Tool Reference
Tool 1: get_user_data
Retrieve user data by entity type.
| Entity | Description | entity_id |
|---|---|---|
all |
Complete user data (profile, preferences, watchlists with items, portfolio) | Not used |
profile |
User info (name, timezone, locale) | Not used |
preferences |
All preferences (risk, investment, agent) | Not used |
watchlists |
List of all watchlists | Not used |
watchlist_items |
Items in a specific watchlist | Optional watchlist_id |
portfolio |
All portfolio holdings | Not used |
# Get complete user data (recommended at start of onboarding)
get_user_data(entity="all")
Tool 2: update_user_data
Create or update user data (upsert semantics). Preference entities merge by default.
| Entity | Description |
|---|---|
profile |
User info (name, timezone, locale, onboarding_completed) |
risk_preference |
Risk tolerance settings |
investment_preference |
Investment style settings |
agent_preference |
Agent behavior settings |
watchlist |
Create or update a watchlist |
watchlist_item |
Add or update item in watchlist |
portfolio_holding |
Add or update a portfolio holding |
All preference fields accept any descriptive string. Extra fields are allowed and persisted.
# Good - rich context that helps future conversations
update_user_data(entity="risk_preference", data={
"risk_tolerance": "Moderate - comfortable with market swings but avoids concentrated bets",
"notes": "Lost money in 2022 tech crash, now prefers diversification"
})
# Bad - keyword with no context
update_user_data(entity="risk_preference", data={"risk_tolerance": "medium"})
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.
- 6d ago First seen · 291 lines · 21 tokens per session scan A 101b31428d69
onboarding is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,722 stars, last pushed today), licensed Apache-2.0. It adds 21 tokens to every session and 2,674 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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