XVARY Stock Research
March 23, 2026 · View on GitHub
Type /analyze NVDA in Claude Code and get a thesis-driven equity report with conviction scoring, kill criteria, and an EDGAR-backed financial snapshot -- in under two minutes, from public data, for free.
This is the open skill layer of XVARY Research. We run a 21-stage pipeline to produce institutional-depth stock analysis. This repo gives you the methodology framework, the data tools, and the scoring models. The full 22-section deep dives live at xvary.com.
We recognize Linux Do community
From the live site (NVDA deep dive)
Captured from xvary.com/stock/nvda/deep-dive/ — same product surface the skill is designed to complement.
Regenerate these assets: npm install && npm run screenshots:nvda (see scripts/screenshot_xvary_nvda.mjs).
What you get that raw data tools don't
- A verdict, not a spreadsheet -- "Constructive at 74/100 conviction"
- Named kill criteria -- exactly what would break the thesis
- Composite scores across four dimensions, not just price ratios
- Analysis that reads like a research desk, not a terminal dump
Quick Start
Clone and verify
git clone git@github.com:xvary-research/claude-code-stock-analysis-skill.git
cd claude-code-stock-analysis-skill
python3 tools/edgar.py AAPL # pulls SEC XBRL data
python3 tools/market.py AAPL # pulls price + ratios
XVARY monorepo: if you already have the full workspace, this skill lives at 9. Marketing/xvary skill/.
Install as a Claude Code skill
mkdir -p ~/.claude/skills/xvary-stock-research
cp SKILL.md ~/.claude/skills/xvary-stock-research/SKILL.md
cp -R references tools examples ~/.claude/skills/xvary-stock-research/
Or skip the install entirely -- open Claude Code in this repo and say:
Read SKILL.md and run /analyze AAPL
Plugin marketplace (same folder): open this directory as the marketplace root (it contains .claude-plugin/marketplace.json), then in Claude Code run /plugin marketplace add . and /plugin install xvary-stock-research@xvary-research. Validate with claude plugin validate . before you tag a release.
Public GitHub checkout: /plugin marketplace add xvary-research/claude-code-stock-analysis-skill then /plugin install xvary-stock-research.
Commands
| Command | What it does |
|---|---|
/analyze {ticker} | 1-page thesis + scorecard + risks + EDGAR-backed financial snapshot |
/score {ticker} | Momentum, Stability, Financial Health, and Upside Estimate |
/compare {A} vs {B} | Side-by-side score, thesis, and risk differential |
Example: /analyze NVDA
Full example: examples/nvda-analysis.md
Verdict: CONSTRUCTIVE (Conviction 74/100)
┌─────────────────┬───────┬──────────────────────────────────────────────┐
│ Score │ Value │ Read │
├─────────────────┼───────┼──────────────────────────────────────────────┤
│ Momentum │ 88 │ Demand + operating leverage remain strong │
│ Stability │ 70 │ Strong execution, non-zero cyclicality risk │
│ Financial Health│ 84 │ Robust balance sheet vs obligations │
│ Upside Estimate │ 64 │ Positive setup, expectations already high │
└─────────────────┴───────┴──────────────────────────────────────────────┘
Thesis pillars:
1. AI infrastructure spend durability
2. CUDA ecosystem lock-in + pricing power
3. Operating leverage on incremental revenue
4. Balance-sheet capacity through cycle volatility
Kill criteria: hyperscaler capex pullback + export control
escalation + gross-margin break with rising capex intensity
Financial snapshot (public, 10-K 2026-01-25):
Revenue \$215.9B · Net income \$120.1B · OCF \$102.7B
Assets \$206.8B / Liabilities \$49.5B
Price \$172.70 · Market cap ~\$4.20T · P/E 35.23 · Beta 2.34
This is the free layer. The full pipeline produces 22-section reports with DCF models, competitive matrices, risk scenarios, and adversarial challenge gates.
Open the live NVDA report: xvary.com/stock/nvda/deep-dive/ (free preview; full tabs with subscription)
How this compares
| Raw data MCPs | Screener APIs | This repo | |
|---|---|---|---|
| Free | Varies | Usually no | Yes |
| Thesis with verdict | No | No | Yes |
| Named kill criteria | No | No | Yes |
| Composite scoring (4 dimensions) | No | Partial | Yes |
| Works locally, no API key | N/A | No | Yes |
| Methodology published | N/A | No | Yes |
Architecture
Skill layer (this repo): public data in → methodology + scoring → structured output → link out to full deep dives on xvary.com.
Claude Code plugin bundle (ships in this folder)
| Path | Role |
|---|---|
.claude-plugin/marketplace.json | Marketplace catalog xvary-research — users run /plugin marketplace add from this directory |
plugins/xvary-stock-research/ | Plugin wrapper; skills/xvary-stock-research/ symlinks to root SKILL.md, references/, tools/, examples/ so there is a single source tree |
Monorepo checkout: open a terminal in 9. Marketing/xvary skill/ (this folder), then run /plugin marketplace add . in Claude Code — same as a standalone claude-code-stock-analysis-skill clone where this folder is the repo root.
flowchart LR
A["/analyze ticker"] --> B["tools/edgar.py\nSEC XBRL + filings"]
A --> C["tools/market.py\nYahoo → Finviz → Stooq"]
B --> D["Methodology spine\n+ scoring refs"]
C --> D
D --> E["Structured analysis\n+ kill criteria"]
E --> F["xvary.com deep dive"]
21-stage research spine + finalize (operational DAG)
Same DAG as references/methodology.md: 22 nodes in code (research spine + finalize). Edges show real control flow—parallel paths merge at phase_b, quality_gate, and completion_loop.
flowchart TB
subgraph P1["① Intake & evidence integrity"]
s1[directive_selection] --> s2[phase_a] --> s3[data_quality_gate] --> s4[evidence_gap_analysis]
end
subgraph P2["② Hypothesis & quant scaffolding"]
s5[kvd_hypothesis]
s6[pane_selection] --> s7[quant_foundation] --> s8[model_quality_gate]
end
subgraph P3["③ Deep enrichment & triangulation"]
s9[phase_b] --> s10[triangulation] --> s11[pillar_discovery]
end
subgraph P4["④ Parallel synthesis & QA"]
s12[phase_c]
s13[why_tree]
s14[quality_gate]
end
subgraph P5["⑤ Adversarial challenge & conviction"]
s15[challenge] --> s16[synthesis]
end
subgraph P6["⑥ Audit, packaging & release control"]
s17[audit] --> s18[report_json]
s19[audience_calibration]
s20[compliance_audit]
s21[completion_loop] --> s22[finalize]
end
s4 --> s5
s4 --> s6
s5 --> s9
s6 --> s9
s11 --> s12
s11 --> s13
s12 --> s14
s13 --> s14
s14 --> s15
s16 --> s17
s18 --> s19
s18 --> s20
s19 --> s21
s20 --> s21
Stage index (one-line intent) — click to expand
| # | Stage | Intent |
|---|---|---|
| 1 | directive_selection | Choose sector/style evidence directives |
| 2 | phase_a | Baseline facts, filings, market context |
| 3 | data_quality_gate | Block low-integrity factual inputs |
| 4 | evidence_gap_analysis | Find gaps; open targeted searches |
| 5 | kvd_hypothesis | Candidate key value drivers |
| 6 | pane_selection | Choose report panes for company profile |
| 7 | quant_foundation | Valuation / risk scaffolding |
| 8 | model_quality_gate | Sanity-check model outputs |
| 9 | phase_b | Enrichment + deeper context |
| 10 | triangulation | Cross-check independent reasoning vectors |
| 11 | pillar_discovery | Weighted thesis pillars |
| 12 | phase_c | Module-level synthesis (parallel) |
| 13 | why_tree | Causal claims + dependency chains |
| 14 | quality_gate | Consistency + evidence sufficiency |
| 15 | challenge | Adversarial test of pillars |
| 16 | synthesis | Conviction, variant view, scenarios |
| 17 | audit | Multi-role verification + follow-ups |
| 18 | report_json | Structured report payload |
| 19 | audience_calibration | Readability + decision speed |
| 20 | compliance_audit | Methodology + policy checks |
| 21 | completion_loop | Repair sparse / inconsistent sections |
| 22 | finalize | Release gating + artifact finalization |
XVARY Scores
Definitions: references/scoring.md
| Score | What it measures |
|---|---|
| Momentum | Direction and persistence of operating + market trajectory |
| Stability | Earnings durability, cyclicality resilience, variance control |
| Financial Health | Balance-sheet strength and cash-flow solvency |
| Upside Estimate | Asymmetry vs. current implied expectations |
Methodology (Published Framework)
Full framework: references/methodology.md
What's published:
- 21-stage research DAG with stage purposes
- 23 module map and what each module produces
- Quality gate names and validation criteria
- Conviction scoring and variant-perception philosophy
- Kill-file risk discipline
What stays proprietary:
- LLM prompts and chain-of-thought templates
- Threshold tables and scoring formulas
- Triangulation and convergence algorithms
- Sector-specific prompt libraries
Data Sources
| Source | Access | Used for |
|---|---|---|
| SEC EDGAR | Public, free | Company facts (XBRL) + filing metadata |
| Yahoo Finance | No API key | Quote, valuation, ratio fields |
| Finviz / Stooq | Fallback | Resilience when Yahoo is unavailable |
EDGAR patterns: references/edgar-guide.md
Full Deep Dives
| Ticker | Link |
|---|---|
| NVDA | xvary.com/stock/nvda/deep-dive/ |
| All coverage (3,325 names) | xvary.com/discover |
| Methodology narrative | xvary.com/methodology |
Roadmap
- MCP server for on-demand full deep dives
- Earnings-season auto-refresh triggers
- Additional scoring models (earnings quality, capital allocation)
- Cursor / Windsurf / Codex skill mirrors (Claude Code marketplace ships from this folder)
Contributing
PRs welcome for:
- EDGAR taxonomy coverage and normalization
- Market-data fallback robustness
- Documentation clarity and examples
License
MIT. See LICENSE.