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/check-modelnpx skills add ginlix-ai/LangAlpha --skill check-modelgit 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/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.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 5.1 | $0.00015 | $0.01433 |
| Opus 5 | $0.00008 | $0.00717 |
| Sonnet 5 | $0.00003 | $0.00287 |
| Haiku 4.5 | $0.00002 | $0.00143 |
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 6d 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, 9 lines differ
- check-model — 88% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Checker
description: Debug and audit financial models for errors — circular references, broken formulas, hardcoded overrides, balance sheet imbalances, cash flow mismatches, and logic gaps. Use when a model isn't tying, producing unexpected results, or before sending to a client or IC. Triggers on "debug model", "model check", "audit model", "model won't balance", "something's off in my model", "check my model", "QA model", or "model review".
Workflow
Step 1: Ingest the Model
- Accept the user's Excel model (.xlsx or .xlsm) from
$WORK_DIR/work/{task}/ - Identify model type: DCF, LBO, merger, 3-statement, comps, returns, or custom
- Map the structure: which tabs exist, how they're linked, where inputs vs. outputs live
Step 2: Structural Checks
Tab & Layout Review:
- Are inputs clearly separated from calculations?
- Is there a consistent color-coding convention? (blue = input, black = formula, green = link)
- Are there hidden tabs or rows that could contain overrides?
- Is the model flow logical? (assumptions → IS → BS → CF → valuation)
Formula Consistency:
- Check for hardcoded numbers inside formulas (partial hardcodes)
- Check for inconsistent formulas across row/column ranges (should be the same formula dragged across)
- Identify any #REF!, #VALUE!, #N/A, #DIV/0! errors
- Flag cells that are formatted as formulas but contain hardcoded values
Step 3: Integrity Checks
Balance Sheet:
- Total Assets = Total Liabilities + Equity (every period)
- If imbalanced, quantify the gap and trace where it breaks
- Check that retained earnings rolls forward correctly: Prior RE + Net Income - Dividends = Current RE
- Verify goodwill and intangibles flow from acquisition assumptions (if M&A model)
Cash Flow Statement:
- Ending cash from CF = Cash on BS (every period)
- Operating CF + Investing CF + Financing CF = Change in Cash
- D&A on CF matches D&A on IS
- Capex on CF matches PP&E rollforward on BS
- Working capital changes on CF match BS movements (AR, AP, inventory)
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.
- 6d 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 (1,722 stars, last pushed today), licensed Apache-2.0. It adds 15 tokens to every session and 1,433 once invoked, about $0.0001 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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