https://github.com/xvary-research/claude-code-stock-analysis-skill

Claude Code stock analysis skill: SEC EDGAR + market data, /analyze /score /compare — free, local Python tools. By XVARY Research.
https://github.com/xvary-research/claude-code-stock-analysis-skill

ai-skills anthropic claude-code claude-plugin edgar equity-research investing python sec-edgar stock-analysis xvary

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Claude Code stock analysis skill: SEC EDGAR + market data, /analyze /score /compare — free, local Python tools. By XVARY Research.

Host: GitHub
URL: https://github.com/xvary-research/claude-code-stock-analysis-skill
Owner: xvary-research
License: mit
Created: 2026-03-22T23:46:02.000Z (3 months ago)
Default Branch: main
Last Pushed: 2026-03-23T18:00:31.000Z (3 months ago)
Last Synced: 2026-03-23T18:29:00.254Z (3 months ago)
Topics: ai-skills, anthropic, claude-code, claude-plugin, edgar, equity-research, investing, python, sec-edgar, stock-analysis, xvary
Language: Python
Homepage: https://xvary.com
Size: 1.32 MB
Stars: 0
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

          # XVARY Stock Research

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](./LICENSE)

[![Python](https://img.shields.io/badge/Python-3.10%2B-blue)](https://www.python.org/)

[![Claude Code Skill](https://img.shields.io/badge/Claude%20Code-Skill-orange)](./SKILL.md)

[![xvary.com](https://img.shields.io/badge/Full%20Deep%20Dives-xvary.com-black)](https://xvary.com)

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](https://xvary.com). 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](https://xvary.com).

*We recognize Linux Do community*

## From the live site (NVDA deep dive)

Captured from **[xvary.com/stock/nvda/deep-dive/](https://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

```bash

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

```bash

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](./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/](https://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](https://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.

```mermaid

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](./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**.

```mermaid

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](./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](./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](./references/edgar-guide.md)

## Full Deep Dives

| Ticker | Link |

|---|---|

| NVDA | [xvary.com/stock/nvda/deep-dive/](https://xvary.com/stock/nvda/deep-dive/) |

| All coverage (3,325 names) | [xvary.com/discover](https://xvary.com/discover) |

| Methodology narrative | [xvary.com/methodology](https://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](./LICENSE).

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