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 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/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/comps-analysis"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/comps-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 Agent Snooping · line 48 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 372 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 | $0.00048 | $0.05849 |
| Opus 5.5 | $0.00019 | $0.02340 |
| Sonnet 5.5 | $0.00010 | $0.01170 |
| Haiku 4.5 | $0.00005 | $0.00585 |
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 1mo 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comparable Company Analysis
A comps table is arithmetic anyone can do and data discipline almost nobody does. The multiples are the easy half: the work is in which companies belong in the set, whether each number measures the same thing over the same period, and how old it is. The steps below build the table in the order those questions have to be answered.
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 table.
Period labels, per-company LTM windows, NTM as four quarterly estimates, and one common base date for every comparative return are binding here: .agents/skills/research-conventions/references/market-data-rules.md, read before the first pull. Step 3 adds no rule of its own and says only which of those period cases a comps table selects.
Step 1: Frame the table
Four questions decide what gets built. How many to ask, in what shape, and what to do when no answer comes back: .agents/skills/research-conventions/references/intake.md.
- Format: their template, or ours
- Audience: investment committee, board, quick reference, detailed memo
- The key question: valuation, growth, competitive position, efficiency
- The context: M&A, an investment decision, sector benchmarking, performance review
The answers change the table, not just its wrapper. A relative-value question needs multiples and quartiles; an efficiency question needs margins and turns and can drop the multiples entirely. Big-cap incumbents and emerging names in the same sector do not take the same metrics. User-provided examples and stated preferences outrank every default in this file.
Done when the question the table answers is written down in one sentence and the metric list follows from it.
Step 2: Build the peer set, with roles
Every peer carries a role, and the roles are tiered so the headline statistic is not diluted by a name that was only ever context.
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.
- 1mo ago Changed · -264 lines · +32 tokens per session 05fef10b744c
- 1mo 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 (1,826 stars, last pushed today), licensed Apache-2.0. It adds 48 tokens to every session and 5,849 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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