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/check-decknpx skills add ginlix-ai/LangAlpha --skill check-deckgit 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/check-deck)<a href="https://agentmods.dev/skills/ginlix-ai/langalpha/check-deck"><img src="https://agentmods.dev/badge/skills/ginlix-ai/langalpha/check-deck.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 | $0.00022 | $0.00629 |
| Opus 5 | $0.00011 | $0.00315 |
| Sonnet 5 | $0.00004 | $0.00126 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
check-deck 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 5d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IB Deck Checker
Perform comprehensive QC on investment banking presentations across four dimensions.
Prerequisites
Extract presentation content before checking:
python -m markitdown presentation.pptx > content.md
For visual inspection, convert to images using the pptx skill workflow.
Check Workflow
1. Number Consistency
Extract numbers with slide references:
python scripts/extract_numbers.py content.md --check
Verify:
- Key metrics match across all slides (revenue, EBITDA, multiples)
- Calculations are correct (totals, percentages, growth rates)
- Units consistent (same scale used: millions vs billions, % vs bps)
- Unit formatting consistent (e.g., $M vs $MM, $B vs $Bn - pick one style throughout)
- Time periods aligned (FY vs LTM vs quarterly)
Flag pattern:
ISSUE: Revenue mismatch
- $500M on Slides 3, 8
- $485M on Slide 15 (DCF input)
ACTION: Reconcile figures
2. Data-Narrative Alignment
Map claims to supporting data:
- Trend statements → chart directions
- Market position claims → revenue/share data
- Factual assertions → verify accuracy
Flag contradictions:
ISSUE: Narrative contradicts data
- Slide 4: "declining margins"
- Slide 7 chart: margins 18% → 22%
ACTION: Update narrative or verify data
Check plausibility (e.g., "#1 player in $100B market" with $200M revenue = 0.2% share).
3. Language Polish
Scan for:
- Casual phrasing ("pretty good", "a lot of")
- Vague quantifiers without specifics
- Contractions, exclamation points
- Inconsistent terminology
See references/ib-terminology.md for replacement patterns.
Flag pattern:
ISSUE: Casual language (Slide 12)
- "This deal is a no-brainer"
→ "The transaction presents a compelling value proposition"
4. Formatting QC
Audit each slide for:
- Charts: Source citations, axis labels, legends
- Typography: Consistent fonts, size hierarchy
- Numbers: Consistent formatting (1,000 vs 1K)
- Dates: Consistent format throughout
- Footnotes: Proper sourcing and disclaimers
What ships with it
3 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.
- 5d ago First seen · 95 lines · 22 tokens per session scan A 2482090eddf6
check-deck is a skill published in the GitHub repository ginlix-ai/LangAlpha (1,722 stars, last pushed today), licensed Apache-2.0. It adds 22 tokens to every session and 629 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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