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 skills add ginlix-ai/LangAlpha --skill dcf-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/dcf-model)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/dcf-model"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/dcf-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/dcf-model"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/dcf-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 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 analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Agent Snooping · line 721 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 Agent Snooping · line 770 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 Agent Snooping · line 774 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.
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 | $0.00056 | $0.07037 |
| Opus 5.5 | $0.00022 | $0.02815 |
| Sonnet 5.5 | $0.00011 | $0.01407 |
| Haiku 4.5 | $0.00006 | $0.00704 |
Grade A, and why
dcf-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.
How it starts
The opening of the file, as written. The whole thing — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DCF Model Builder
Builds an institutional-quality DCF as a live Excel workbook: four sheets, three scenarios, three sensitivity grids, and a valuation a portfolio manager can argue with. A model that computes correctly and says nothing about the stock is half the job, so the construction rules and the judgement rules below are one document.
Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first figure enters the workbook.
recalc.pyreports errors, the case selector does not move the model, or the valuation looks wrong: read.agents/skills/dcf-model/TROUBLESHOOTING.md.- The revenue build needs the drivers of a particular sector, or the company is a bank, an insurer, a miner or a REIT: read
.agents/skills/dcf-model/references/sector-drivers.md.
Tools
- fundamentals MCP:
get_financial_statements,get_financial_ratios,get_growth_metrics,get_historical_valuation; diluted shares come from here - macro MCP:
get_treasury_rates,get_market_risk_premium get_company_overview: share price, analyst consensus, earnings surprises, revenue segmentationyf_fundamentalsMCP:compare_valuations, whose per-symbol record carries thebetafieldyf_analysisMCP:get_growth_estimatesfor consensus growth, one record perperiod(0q,+1q,0y,+1y,+5y,-5y)- Web search and fetch, and user-provided data, as supplements
Step 0: Before you build
The question budget, the shape of each question and what to do when no answer comes back are in .agents/skills/research-conventions/references/intake.md.
The forks that usually matter here: whether the user has an existing model to extend rather than a new build, the horizon, and whether the base case should follow guidance, consensus, or your own view.
Three anchors are required, not optional. A DCF without them produces a number nobody can act on:
| Anchor | Why the model cannot proceed without it |
|---|---|
| Current share price, with its as-of | The output is a fair value; without spot there is no implied return and no stance |
| Diluted share count | Equity value divided by the wrong share count is wrong by exactly the dilution |
| Net debt, with the balance-sheet date | It is the whole bridge from enterprise value to equity value |
What ships with it
3 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.
- 29d ago Changed 1edb2e1ecdef
- 1mo ago Changed · -805 lines · +36 tokens per session 872ba4ae2fca
- 1mo ago First seen · 1,134 lines · 20 tokens per session scan A 455de2a12ac8
dcf-model is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,829 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 7,037 once invoked, about $0.0002 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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