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 initiating-coveragegit 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/initiating-coverage)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/initiating-coverage"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/initiating-coverage/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/initiating-coverage"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/initiating-coverage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Excessive Agency · line 643 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00072 | $0.03441 |
| Opus 5.5 | $0.00029 | $0.01376 |
| Sonnet 5.5 | $0.00014 | $0.00688 |
| Haiku 4.5 | $0.00007 | $0.00344 |
Grade A, and why
initiating-coverage 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.
This is a copy
100% identical to initiating-coverage — 794 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Initiating Coverage
A first-time coverage report: the argument for or against owning a company, built from original research, a full model and a defended valuation. Five tasks, one per request by default, each verifying its inputs before it starts. The deliverables are a 30 to 50 page DOCX and an XLSX model, with typography set by .agents/skills/docx/SKILL.md.
Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first deliverable.
One task per request
This skill runs one task per request by default: execute the task, deliver its file, and stop, so the user can review before the next.
That stop is an intake exception and yields under .agents/skills/research-conventions/references/intake.md. When the user asked for the whole pipeline in one request ("do all five tasks", "run the whole initiation") or this skill is running as an input to another workflow, run the tasks in order without stopping between them, naming at each handoff the task just finished.
When the request names no task and does not ask for the whole run ("write an initiation report for X"), reply with the task list and ask which one to begin, in roughly this shape:
An initiation for [Company] runs as five tasks, one per request:
1. Company research 4. Chart generation
2. Financial modelling 5. Report assembly
3. Valuation analysis
Each one delivers its file and stops, so you can review before the next.
Which would you like to start with?
Either way, never assume a task the user did not ask for, and never start a task whose prerequisites are unverified.
Task map
| Task | Prerequisites | Deliverable |
|---|---|---|
| 1 Company research | company name or ticker | [Company]_Research_Document_[Date].md, 6,000 to 8,000 words |
| 2 Financial modelling | financial statements or 10-K access | [Company]_Financial_Model_[Date].xlsx, the manifest's Task 2 sheets |
| 3 Valuation analysis | Task 2 | [Company]_Valuation_Analysis_[Date].md plus the manifest's Task 3 sheets, added to the Task 2 workbook |
| 4 Chart generation | Tasks 1, 2, 3 | [Company]_Charts_[Date].zip, 25 to 35 charts at 300 DPI |
| 5 Report assembly | Tasks 1, 2, 3, 4 | [Company]_Initiation_Report_[Date].docx |
What ships with it
11 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.
- assets/quality-checklist.md 15 KB
- assets/report-template.md 30 KB
- references/argument-standards.md 5.6 KB
- references/report-modes.md 3.8 KB
- references/task1-company-research.md 12 KB
- references/task2-financial-modeling.md 27 KB
- references/task3-valuation.md 23 KB
- references/task4-chart-generation.md 36 KB
- references/task5-assembly-phases.md 25 KB
- references/task5-report-assembly.md 23 KB
- references/valuation-methodologies.md 19 KB
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 · -627 lines · +46 tokens per session fdaeebc8e464
- 1mo ago First seen · 785 lines · 26 tokens per session scan A cdd9a305b0a1
initiating-coverage is a skill published in the GitHub repository ginlix-ai/LangAlpha (2,155 stars, last pushed today), licensed Apache-2.0. It adds 72 tokens to every session and 3,441 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to initiating-coverage, differing in 794 lines, and is treated as a copy.
Other skills, from other repositories
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This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions.
dividend-analysis
Comprehensive capital allocation analysis: dividends, buybacks, M&A, debt management, and FCF deployment.
financial-report-analyst
Analyze company financial reports (10-K, 10-Q, annual reports) and extract actionable investment insights — statement quality, red flags, and a signal. For a structured section-by-section summary document of a 10-K rather than an investment call, use 10k-digest.
report-generator
Generate professional HTML/PDF investment reports with interactive visualizations.
stock-eval
Evaluate US stocks with comprehensive fundamental and valuation analysis.
stock-valuation
Multi-method stock valuation using DCF, comparable company analysis, EV multiples, and residual income models.