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/user-profilenpx skills add ginlix-ai/LangAlpha --skill user-profilegit 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/user-profile)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/user-profile"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/user-profile.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 | $0.00015 | $0.02729 |
| Opus 5 | $0.00008 | $0.01365 |
| Sonnet 5 | $0.00003 | $0.00546 |
| Haiku 4.5 | $0.00002 | $0.00273 |
Grade A, and why
user-profile 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 7d 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 — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Profile Skill
This skill provides 3 unified tools for managing user data:
get_user_data- Read user dataupdate_user_data- Create or update user dataremove_user_data- Delete user data
You should call these tools directly instead of using ExecuteCode tool.
Tool 1: get_user_data
Retrieve user data by entity type.
Entities
| 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 |
Examples
# Get complete user data (recommended for initial context)
get_user_data(entity="all")
# Returns: {
# "profile": {"name": "John", "timezone": "America/New_York", "locale": "en-US"},
# "preferences": {"risk_preference": {...}, "investment_preference": {...}, ...},
# "watchlists": [{"name": "Tech Stocks", "items": [...], ...}],
# "portfolio": [{"symbol": "AAPL", "quantity": 50, ...}]
# }
# Get user profile
get_user_data(entity="profile")
# Returns: {"name": "John", "timezone": "America/New_York", "locale": "en-US"}
# Get all preferences
get_user_data(entity="preferences")
# Returns: {"risk_preference": {...}, "investment_preference": {...}, "agent_preference": {...}}
# Get all watchlists
get_user_data(entity="watchlists")
# Returns: [{"watchlist_id": "abc", "name": "Tech Stocks", "is_default": true}, ...]
# Get items from default watchlist
get_user_data(entity="watchlist_items")
# Returns: [{"symbol": "AAPL", "notes": "..."}, {"symbol": "NVDA", ...}]
# Get items from specific watchlist
get_user_data(entity="watchlist_items", entity_id="abc-123")
# Get portfolio holdings
get_user_data(entity="portfolio")
# Returns: [{"symbol": "AAPL", "quantity": 50, "average_cost": 175.0}, ...]
What ships with it
1 file 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.
- 7d ago First seen · 373 lines · 15 tokens per session scan A 7222e376e594
user-profile is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,724 stars, last pushed today), licensed Apache-2.0. It adds 15 tokens to every session and 2,729 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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