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/chart-annotationnpx 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.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 | $0.00058 | $0.02444 |
| Opus 5 | $0.00029 | $0.01222 |
| Sonnet 5 | $0.00012 | $0.00489 |
| Haiku 4.5 | $0.00006 | $0.00244 |
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 5d 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 this skill 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, TradingView-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. If they are in any other chat, the same drawing renders as a clickable preview card that expands into MarketView — so annotate freely whenever it helps, then mention the user can click it to open the full chart.
This skill provides two tools:
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:
- This skill (interactive). Annotations land on the live, pannable MarketView chart (or a preview card that opens it). 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 preview.
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.
- 5d 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 (1,722 stars, last pushed today), licensed Apache-2.0. It adds 58 tokens to every session and 2,444 once invoked, about $0.0003 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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