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-modelWrote 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-model)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/check-model"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/check-model/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-model"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/check-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 145 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Excessive Agency · line 142 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00072 | $0.03780 |
| Opus 5.5 | $0.00029 | $0.01512 |
| Sonnet 5.5 | $0.00014 | $0.00756 |
| Haiku 4.5 | $0.00007 | $0.00378 |
Grade A, and why
check-model 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 29d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- check-model — 88% identical, 292 lines differ
- check-model — 88% identical, 292 lines differ
How it starts
The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Checker
Two different things can be wrong with a model: the arithmetic, and the underwriting. A model whose formulas are flawless can still be built on a figure that contradicts the filing it cites, or on an estimate nobody refreshed after the last print. This skill tests both, and ends in a report a reader can act on rather than a list of cells.
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: Ingest and scope the audit
- Accept the user's model (
.xlsxor.xlsm) from{task}/ - Identify the model type: DCF, LBO, merger, 3-statement, comps, returns, or custom
- Map the structure: which tabs exist, how they link, where inputs live against outputs
Scope discipline. Judge the model against the job it was built for. A standalone operating model with no valuation layer is not missing a DCF, and a quick screen is not a failed initiation. Where the model's purpose is unclear, state the scope you audited against at the top of the report and let the user correct it. A finding that amounts to "this is not the model I would have built" belongs in a scope note, not in the issue log, where it dilutes the findings that matter.
Missing files are surfaced first. An absent workbook, a source document the model cites and does not include, or a tab referenced by formulas and not present goes at the top of the report, not into a source appendix at the bottom. The reader's first question is whether you audited what they think you audited.
Done when the model type, the tab map, and the scope you are auditing against are written down, and any missing file is named.
Step 2: Recalculate, then read the Checks sheet
Recalculation honesty governs the whole audit. Cached values pass checks on broken models. Before reading a single number, recalculate on a copy:
python .agents/skills/xlsx/scripts/recalc.py <copy of file> 60
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.
- 29d ago Changed 449566bacde4
- 1mo ago Changed · +101 lines · +57 tokens per session b173778916c2
- 1mo ago First seen · 146 lines · 15 tokens per session scan A ca4daf4d1077
check-model is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,829 stars, last pushed today), licensed Apache-2.0. It adds 72 tokens to every session and 3,780 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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