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 research-conventionsgit 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/research-conventions)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/research-conventions"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/research-conventions/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/research-conventions"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/research-conventions.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00066 | $0.00757 |
| Opus 5.5 | $0.00026 | $0.00303 |
| Sonnet 5.5 | $0.00013 | $0.00151 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
research-conventions 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 — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Conventions
The shared layer beneath every skill in this plugin. Each research skill says what to build; this one says what a figure has to carry, how far the evidence lets the language go, what is worth asking before starting, and how periods, windows and returns are computed. It holds those rules so no skill re-derives them: on evidence, judgement, intake and market-data mechanics this layer is the source of truth, and the calling skill owns what its deliverable contains.
Reference files
- Any deliverable that prints a figure, a number with no source, two sources disagreeing, a figure that may be stale, or how densely to cite:
.agents/skills/research-conventions/references/evidence.md. - A valuation, a thesis, a recommendation, a scenario set, or any section that ends in what to do:
.agents/skills/research-conventions/references/judgment.md. - Before asking the user anything at the start of a task, and when deciding whether to ask at all:
.agents/skills/research-conventions/references/intake.md. - Before the first market-data pull of anything that prints a period label, a multiple, a growth rate or a return:
.agents/skills/research-conventions/references/market-data-rules.md. - Choosing how long a deliverable runs, a draft over its length band, or a request for a short version of something already built:
.agents/skills/research-conventions/references/depth.md.
Readiness posture
Every deliverable states one posture, once, near the top, in the reader's language. Posture is about inputs, not conviction: a high-conviction view resting on stale inputs is not decision-grade, and saying so is what makes the rest of the artifact usable.
| Posture | Holds when | Forced down by |
|---|---|---|
decision-grade |
every load-bearing figure is sourced and inside its freshness threshold, the downside is mechanical, and the inputs the recommended action needs are in hand | one load-bearing figure that is stale, unsourced or contested |
review-ready |
the analysis is complete and sourced, and one check or one input is still open | an unresolved conflict on a load-bearing claim, or a check that fails |
screen-grade |
coverage or depth is deliberately thin and the artifact says so | a headline conclusion that outruns the coverage behind it |
not-ready |
a load-bearing claim is unsupported, stale or contested with nothing settling it, and the conclusion rests on it | already the floor for a claim problem; state what would lift it |
blocked |
an input cannot be obtained at all (a tool failure, an undisclosed metric, a document we do not have) | already the floor; name the missing input and what would unblock it |
What ships with it
5 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 First seen · 31 lines · 66 tokens per session scan A f14ef18ea58e
research-conventions is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,820 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 757 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 2026-09-16.
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