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/model-updateWrote 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/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/model-update"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/model-update.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 | $0.00058 | $0.02740 |
| Opus 5.5 | $0.00023 | $0.01096 |
| Sonnet 5.5 | $0.00012 | $0.00548 |
| Haiku 4.5 | $0.00006 | $0.00274 |
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 28d 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 — 208 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Update
Updating is not building. You know what changed in the world and you do not know what the file will do when you write into it, so the order below maps every data point to a treatment before a single cell moves, keeps the file the user gave you byte-identical, and leaves a reader able to see what changed and on whose authority.
Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first write.
Routing before you start:
- The workbook's own
Checkssheet fails, or the model looks wrong before you touch it: audit it first with.agents/skills/check-model/SKILL.md. - The refresh has to move operating metrics rather than the three statements:
.agents/skills/dcf-model/references/sector-drivers.mdlists what drives each sector.
Step 1: Name the trigger and list the fields it touches
One update has one trigger. Name it, hold the document or the tool call that carries it, and write down the fields it reaches before opening the workbook.
| Trigger | Arrives as | Touches |
|---|---|---|
| Results | a reported quarter or year | historical actuals, the LTM roll, and the forward periods the print reprices |
| Guidance change | a range, a raise, a cut, a withdrawal | the driver rows for the guided periods, and the basis note beside them |
| Consensus move | a revised mean estimate with its vintage | the comparison column, never the model's own drivers |
| Transcript disclosure | a number said on a call | the operating metrics it re-anchors, and any driver built on them |
| Filing | 10-K, 10-Q, 8-K, proxy | restatements, segment re-cuts, share count, the debt schedule, contingencies |
| KPI release | a monthly or quarterly operating metric | the sector KPI rows, and the revenue build above them |
| Capital structure change | a buyback, an issuance, a raise, a repayment | diluted share count, net debt, the equity bridge |
| Market data move | price, rates, FX | the spot anchor, the WACC inputs, and the implied return |
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.
- 28d ago Changed aaa01ed8b777
- 1mo ago Changed · +71 lines · +39 tokens per session 2cf89364a195
- 1mo 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 (1,826 stars, last pushed today), licensed Apache-2.0. It adds 58 tokens to every session and 2,740 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to model-update, differing in 208 lines, and is treated as a copy.
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