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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ginlix-ai/LangAlphanpx agentmods add skills/ginlix-ai/langalpha/check-deckWrote 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/check-deck)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/check-deck"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/check-deck/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/check-deck"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/check-deck.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 | $0.00063 | $0.03355 |
| Opus 5.5 | $0.00025 | $0.01342 |
| Sonnet 5.5 | $0.00013 | $0.00671 |
| Haiku 4.5 | $0.00006 | $0.00335 |
Grade A, and why
check-deck 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deck Check
QC that ends in a decision. Finding a discrepancy is the easy half; the work is saying what it proves, how confident we are, and whether the deck can go out. Every consequential finding carries a confidence label, and the pass ends in one readiness posture for the whole document.
Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first finding.
Step 1: Establish the artifact set and the evidence rules
Name the controlling artifact (the deck under review) and list every supporting artifact with its role: the model the numbers tie to, the filings behind the market claims, the prior version, the data pulls. A finding's confidence depends on which artifact it came from, so this classification happens before any checking.
The controlling model is the subject under test, and the deck is checked against it: a deck figure that does not match the model is a finding about the deck. Where the model and a source disagree, the sources resolve down the tiers in .agents/skills/research-conventions/references/evidence.md, the filing first, then the data tools, then company materials labelled as such, then third-party summaries. Two conflicting sources are never averaged and never silently resolved in favour of the flattering one.
Read the deck out with the pptx skill's extractor, which keeps the slide structure the
checks depend on:
python .agents/skills/pptx/scripts/extract.py presentation.pptx > deck.json
The result is one JSON object with file, slide_size_in and slides. Each slide carries
its index, title, layout, its text lines in shape order, a shapes inventory of
names, positions and font sizes, tables as cells (a list of rows of strings), charts
as series values against categories (or x_values, y_values and bubble_sizes for a
scatter or bubble chart), and speaker notes. Keeping that structure is the
point: a figure can then be reported as slide 4, Table 3 r2c2, and a table cell can be read
through the row label and column header that say what it is, neither of which survives a
prose export.
What ships with it
4 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 · +104 lines · +41 tokens per session a16eec70ecd6
- 1mo ago First seen · 95 lines · 22 tokens per session scan A 2482090eddf6
check-deck is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,826 stars, last pushed today), licensed Apache-2.0. It adds 63 tokens to every session and 3,355 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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