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/dcf-modelnpx 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.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 | $0.00020 | $0.11075 |
| Opus 5 | $0.00010 | $0.05538 |
| Sonnet 5 | $0.00004 | $0.02215 |
| Haiku 4.5 | $0.00002 | $0.01107 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 1,134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DCF Model Builder
Overview
This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet).
Tools
- fundamentals MCP:
get_financial_statements,get_financial_ratios,get_growth_metrics,get_historical_valuation - macro MCP:
get_treasury_rates,get_market_risk_premium get_company_overviewtool: analyst consensus, growth estimates, company profile- User-provided data and web search/fetch as supplements
Critical Constraints - Read These First
These constraints apply throughout all DCF model building. Review before starting:
Sensitivity Tables:
- Populate ALL 75 cells (3 tables × 25 cells) with full DCF recalculation formulas
- Use openpyxl loops to write formulas programmatically
- NO placeholder text, NO linear approximations, NO manual steps required
- Each cell must recalculate full DCF for that assumption combination
Cell Comments:
- Add cell comments AS each hardcoded value is created
- Format: "Source: [System/Document], [Date], [Reference], [URL if applicable]"
- Every blue input must have a comment before moving to next section
- Do not defer to end or write "TODO: add source"
Model Layout Planning:
- Define ALL section row positions BEFORE writing any formulas
- Write ALL headers and labels first
- Write ALL section dividers and blank rows second
- THEN write formulas using the locked row positions
- Test formulas immediately after creation
Formula Recalculation:
- Run
python .agents/skills/xlsx/scripts/recalc.py model.xlsx 30before delivery - Fix ALL errors until status is "success"
- Zero formula errors required (#REF!, #DIV/0!, #VALUE!, etc.)
Scenario Blocks:
- Create separate blocks for Bear/Base/Bull cases
- Show assumptions horizontally across projection years within each block
- Use IF formulas:
=IF($B$6=1,[Bear cell],IF($B$6=2,[Base cell],[Bull cell])) - Verify formulas reference correct scenario block cells
What ships with it
1 file 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.
- 6d 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 (1,722 stars, last pushed today), licensed Apache-2.0. It adds 20 tokens to every session and 11,075 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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