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 idea-generationgit 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/idea-generation)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/idea-generation"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/idea-generation/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/idea-generation"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/idea-generation.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 | $0.00067 | $0.03521 |
| Opus 5.5 | $0.00027 | $0.01408 |
| Sonnet 5.5 | $0.00013 | $0.00704 |
| Haiku 4.5 | $0.00007 | $0.00352 |
Grade A, and why
idea-generation 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 1mo 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Idea Generation
A funnel, not a screen dump. A name that clears a filter is a candidate: it has earned the next hour of work, nothing more. The output is a short ranked list where each name says why it surfaced, what would have to be true, and what would kill it, plus the names that screened well and were rejected, which is where most of the learning sits.
Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first deliverable.
Step 1: Mandate and universe
Read the investor-mandate memory first per .agents/skills/research-conventions/references/intake.md, then ask only about the forks it leaves open. Record every slot, with a disclosed default where nothing fixes it:
| Slot | Default when unstated |
|---|---|
| Mandate and vehicle | long-only fundamental |
| Objective | absolute return over a multi-quarter horizon |
| Direction | long, plus shorts only where the mandate allows them |
| Horizon | two to eight quarters, which is what the screens below are calibrated for |
| Geography and listing | the user's home market, primary listings |
| Market-cap range | above the liquidity floor, otherwise unrestricted |
| Liquidity floor | a minimum daily traded value the position size could clear in a few days |
| Benchmark | the mandate's index, or none stated |
| Instruments and constraints | common equity; restricted lists, borrow availability and position limits taken from the user when they exist |
| Theme | none, unless the request names one |
Done when every slot carries a value or a default that the delivery message discloses.
Step 2: Validate the universe before anything is ranked
Screen metrics lie on a universe nobody cleaned:
- Resolve identifiers to one traded line per company, choosing between an ADR, the local line and multiple share classes deliberately, and note which line the figures use.
- Drop delisted and stale listings; confirm the benchmark membership the mandate cares about; mark overlap with existing holdings so a "new idea" is not a position the user already owns.
- Flag names whose recent history breaks a screen: an IPO inside a year (no comparable base), a spin-off (pro-forma financials, no like-for-like history), a restatement, a fiscal-year change, and a transformative acquisition that resets every growth rate.
What ships with it
2 files 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.
- 1mo ago Changed 11df6d206dde
- 1mo ago Changed · +37 lines · +51 tokens per session d55e4507bdaa
- 1mo ago Changed 6b69311dc92a
- 1mo ago First seen · 131 lines · 16 tokens per session scan A 709ce5ba481b
idea-generation is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,869 stars, last pushed today), licensed Apache-2.0. It adds 67 tokens to every session and 3,521 once invoked, about $0.0003 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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