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 chart-annotationgit 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/chart-annotation)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/chart-annotation"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/chart-annotation/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/chart-annotation"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/chart-annotation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.02448 |
| Opus 5.5 | $0.00017 | $0.00979 |
| Sonnet 5.5 | $0.00009 | $0.00490 |
| Haiku 4.5 | $0.00004 | $0.00245 |
Grade A, and why
chart-annotation 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 14d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chart Annotation Skill
When to use
You want to call out a technical level, a pattern, or an event on a stock's
price chart. Drawing directly on the chart is almost always clearer than
describing it in prose. Reach for draw_chart_annotation whenever you would
otherwise say "look at the level around 205" or "notice the downtrend from
October to December".
MarketView is the app's live, trading-chart style price chart page (pan, zoom, switch timeframes). You do not need the user to be on it to annotate. If they are, the drawing appears on their live chart immediately. Anywhere else in the app, the same drawing renders as a chart card in the chat that opens the live chart beside the conversation, so annotate freely whenever it helps, then mention the user can click the card to see it.
Both tools are always available; this guide is how to use them well:
draw_chart_annotation— add a single annotation to a chart.manage_chart_annotations— list, remove, or clear annotations.
Interactive chart vs. a Python chart (deliverable)
There are two ways to show price information visually — pick by what the user needs:
- The annotation tools (interactive). Annotations land on a live, pannable chart: MarketView's, or the one a chart card in the chat opens. Best when the user just wants to see and explore a level, pattern, or event themselves — quick, in-the-moment, nothing to hand off.
- A Python chart (deliverable). A static image you render with code and embed in a report or document. Best when the output is a deliverable the user keeps, shares, or exports — a research note, PDF, or deck.
The two aren't exclusive: draw on the live chart for a quick look, render a Python chart when it belongs in a written artifact, or do both.
Charts are identified by SYMBOL:timeframe
Every annotation belongs to a chart identified by its ticker + timeframe
(e.g. NVDA:1day) — that pair is the chart's id:
- Pass the same
symbol+timeframeagain to add to / edit that same chart (annotations accumulate on it). - Use a different ticker or timeframe to start a separate chart — so
you can draw several charts in one turn (e.g.
AAPL:1dayandAAPL:1hour, orAAPL:1dayandMSFT:1day), each rendered as its own card.
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.
- 14d ago Changed · -15 tokens per session 6c1e03e3615d
- 1mo ago Changed 74517892cec0
- 1mo ago First seen · 255 lines · 58 tokens per session scan A a20a0ebf9b0a
chart-annotation is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,826 stars, last pushed today), licensed Apache-2.0. It adds 43 tokens to every session and 2,448 once invoked, about $0.0002 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.
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