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 competitive-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/competitive-analysis)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/competitive-analysis"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/competitive-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/competitive-analysis"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/competitive-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.02908 |
| Opus 5.5 | $0.00017 | $0.01163 |
| Sonnet 5.5 | $0.00008 | $0.00582 |
| Haiku 4.5 | $0.00004 | $0.00291 |
Grade A, and why
competitive-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.
Copies of this mod
2 near-identical copies found in the catalogue:
- competitive-analysis — 89% identical, 410 lines differ
- competitive-analysis — 89% identical, 410 lines differ
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Landscape Mapping
Who competes, on what, and which advantages survive. The product is a comparison, so the comparison has to be real: same period, same metric definition, same basis, with each figure's provenance visible. A table of numbers that were measured differently looks like analysis and is not one.
Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first deliverable.
Reference files
- The deliverable is a deck or a formatted document, or the request specifies titles, chart types or exact figures:
.agents/skills/competitive-analysis/references/presentation.md. - The set is judged on customer economics rather than units shipped or stores opened:
.agents/skills/competitive-analysis/references/unit-economics.md, which carries the benchmark bands (Rule of 40 on EBITDA margin, NDR, LTV:CAC, CAC payback), the cohort matrix and the traps that make two companies look comparable when they are not. - Building an M&A transaction table, a scenario table, or a slide skeleton:
.agents/skills/competitive-analysis/references/schemas.md. - Choosing the two axes for a positioning matrix:
.agents/skills/competitive-analysis/references/frameworks.md.
Data standards
These bind every run, whatever the deliverable is.
Provenance on every competitor metric
Each cell in a competitor table carries the figure, its as-of, and one label from the closed set in .agents/skills/research-conventions/references/evidence.md, which also holds the freshness thresholds and the six staleness states. Three of the seven carry most of a competitor table: fact for the company's own filed statements, company claim for a management figure that is not in them (market share, customer count, addressable market), and street estimate for a consensus or single-analyst number, with its vintage and analyst count.
A third-party sizing of a private company is a street estimate: fact is reserved for a primary document or a data-tool figure for a period that has closed. A research house's published sizing carries the house as its estimator and the publication date as its vintage, and anything with no traceable publisher stays needs-source until one is found.
What ships with it
4 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.
- 1mo ago Changed · -155 lines · +24 tokens per session d9bcbd916ec4
- 1mo ago First seen · 361 lines · 18 tokens per session scan A 9b245076ccb8
competitive-analysis is a skill published in the GitHub repository ginlix-ai/LangAlpha (2,155 stars, last pushed today), licensed Apache-2.0. It adds 42 tokens to every session and 2,908 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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