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/comps-analysisnpx skills add ginlix-ai/LangAlpha --skill comps-analysisgit 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/comps-analysis)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/comps-analysis"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/comps-analysis.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 | $0.00016 | $0.05797 |
| Opus 5 | $0.00008 | $0.02899 |
| Sonnet 5 | $0.00003 | $0.01159 |
| Haiku 4.5 | $0.00002 | $0.00580 |
Grade A, and why
comps-analysis 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 4d 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 — 551 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comparable Company Analysis
Overview
This skill teaches Claude to build institutional-grade comparable company analyses that combine operating metrics, valuation multiples, and statistical benchmarking. The output is a structured Excel/spreadsheet that enables informed investment decisions through peer comparison.
ALWAYS ask yourself first:
- "Do you have a preferred format or should I adapt the template style?"
- "Who is the audience?" (Investment committee, board presentation, quick reference, detailed memo)
- "What's the key question?" (Valuation, growth analysis, competitive positioning, efficiency)
- "What's the context?" (M&A evaluation, investment decision, sector benchmarking, performance review)
Adapt based on specifics:
- Industry context: Big tech mega-caps need different metrics than emerging SaaS startups
- Sector-specific needs: Add relevant metrics early (e.g., cloud ARR, enterprise customers, developer ecosystem for tech)
- Company familiarity: Well-known companies may need less background, more focus on delta analysis
- Decision type: M&A requires different emphasis than ongoing portfolio monitoring
Core principle: Use template principles (clear structure, statistical rigor, transparent formulas) but vary execution based on context. The goal is institutional-quality analysis, not institutional-looking templates.
User-provided examples and explicit preferences always take precedence over defaults.
Core Philosophy
"Build the right structure first, then let the data tell the story."
Start with headers that force strategic thinking about what matters, input clean data, build transparent formulas, and let statistics emerge automatically. A good comp should be immediately readable by someone who didn't build it.
Section 1: Document Structure & Setup
Header Block (Rows 1-3)
Row 1: [ANALYSIS TITLE] - COMPARABLE COMPANY ANALYSIS
Row 2: [List of Companies with Tickers] • [Company 1 (TICK1)] • [Company 2 (TICK2)] • [Company 3 (TICK3)]
Row 3: As of [Period] | All figures in [USD Millions/Billions] except per-share amounts and ratios
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.
- 4d ago First seen · 551 lines · 16 tokens per session scan A 039a0d871c82
comps-analysis is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,720 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 5,797 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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