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/model-updatenpx skills add ginlix-ai/LangAlpha --skill model-updategit 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/model-update)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/model-update"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/model-update.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 | $0.00019 | $0.00746 |
| Opus 5 | $0.00010 | $0.00373 |
| Sonnet 5 | $0.00004 | $0.00149 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
model-update 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 5d 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.
This is a copy
86% identical to model-update — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Update
Workflow
Step 1: Identify What Changed
Determine the update trigger:
- Earnings release: New quarterly actuals to plug in
- Guidance change: Company updated forward outlook
- Estimate revision: Analyst changing assumptions based on new data
- Macro update: Interest rates, FX, commodity prices changed
- Event-driven: M&A, restructuring, new product, management change
Step 2: Plug New Data
After Earnings
Update the model with reported actuals:
| Line Item | Prior Estimate | Actual | Delta | Notes |
|---|---|---|---|---|
| Revenue | ||||
| Gross Margin | ||||
| Operating Expenses | ||||
| EBITDA | ||||
| EPS | ||||
| [Key metric 1] | ||||
| [Key metric 2] |
Segment Detail (if applicable):
- Update each segment's revenue and margin
- Note any segment mix shifts
Balance Sheet / Cash Flow Updates:
- Cash and debt balances
- Share count (buybacks, dilution)
- Capex actual vs. estimate
- Working capital changes
Step 3: Revise Forward Estimates
Based on the new data, adjust forward estimates:
| Old FY Est | New FY Est | Change | Old Next FY | New Next FY | Change | |
|---|---|---|---|---|---|---|
| Revenue | ||||||
| EBITDA | ||||||
| EPS |
Key Assumption Changes:
- What assumptions are you changing and why?
- Revenue growth rate: old → new (reason)
- Margin assumption: old → new (reason)
- Any new items (restructuring charges, one-time gains, etc.)
Step 4: Valuation Impact
Recalculate valuation with updated estimates:
| Valuation Method | Prior | Updated | Change |
|---|---|---|---|
| DCF fair value | |||
| P/E (NTM EPS × target multiple) | |||
| EV/EBITDA (NTM EBITDA × target multiple) | |||
| Price Target |
Step 5: Summary & Action
Estimate Change Summary:
- One paragraph: what changed, why, and what it means for the stock
- Is this a thesis-changing event or noise?
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.
- 5d ago First seen · 97 lines · 19 tokens per session scan A dd7c56a21001
model-update is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,720 stars, last pushed today), licensed Apache-2.0. It adds 19 tokens to every session and 746 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to model-update, differing in 3 lines, and is treated as a copy.
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