xvary-stock-research
Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
Best use case
xvary-stock-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "xvary-stock-research" skill to help with this workflow task. Context: Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/xvary-stock-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How xvary-stock-research Compares
| Feature / Agent | xvary-stock-research | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# XVARY Stock Research Skill
Use this skill to produce institutional-depth stock analysis in Claude Code using public EDGAR + market data.
## When to Use
- Use when you need a **verdict-style equity memo** (constructive / neutral / cautious) grounded in **public** filings and quotes.
- Use when you want **named kill criteria** and a **four-pillar scorecard** (Momentum, Stability, Financial Health, Upside) without a paid data terminal.
- Use when comparing two tickers with `/compare` and need a structured differential, not a prose-only chat answer.
## Commands
### `/analyze {ticker}`
Run full skill workflow:
1. Pull SEC fundamentals and filing metadata from `tools/edgar.py`.
2. Pull quote and valuation context from `tools/market.py`.
3. Apply framework from `references/methodology.md`.
4. Compute scorecard using `references/scoring.md`.
5. Output structured analysis with verdict, pillars, risks, and kill criteria.
### `/score {ticker}`
Run score-only workflow:
1. Pull minimum required EDGAR and market fields.
2. Compute Momentum, Stability, Financial Health, and Upside Estimate.
3. Return score table + short interpretation + top sensitivity checks.
### `/compare {ticker1} vs {ticker2}`
Run side-by-side workflow:
1. Execute `/score` logic for both tickers.
2. Compare conviction drivers, key risks, and valuation asymmetry.
3. Return winner by setup quality, plus conditions that would flip the view.
## Execution Rules
- Normalize all tickers to uppercase.
- Prefer latest annual + quarterly EDGAR datapoints.
- Cite filing form/date whenever stating a hard financial figure.
- Keep analysis concise but decision-oriented.
- Use plain English, avoid generic finance fluff.
- Never claim certainty; surface assumptions and kill criteria.
## Output Format
For `/analyze {ticker}` use this shape:
1. `Verdict` (Constructive / Neutral / Cautious)
2. `Conviction Rationale` (3-5 bullets)
3. `XVARY Scores` (Momentum, Stability, Financial Health, Upside)
4. `Thesis Pillars` (3-5 pillars)
5. `Top Risks` (3 items)
6. `Kill Criteria` (thesis-invalidating conditions)
7. `Financial Snapshot` (revenue, margin proxy, cash flow, leverage snapshot)
8. `Next Checks` (what to watch over next 1-2 quarters)
For `/score {ticker}` use this shape:
1. Score table
2. Factor highlights by score
3. Confidence note
For `/compare {ticker1} vs {ticker2}` use this shape:
1. Score comparison table
2. Where ticker A is stronger
3. Where ticker B is stronger
4. What would change the ranking
## Scoring + Methodology References
- Methodology: `references/methodology.md`
- Score definitions: `references/scoring.md`
- EDGAR usage guide: `references/edgar-guide.md`
## Data Tooling
- EDGAR tool: `tools/edgar.py`
- Market tool: `tools/market.py`
If a tool call fails, state exactly what data is missing and continue with available inputs. Do not hallucinate missing figures.
## Footer (Required on Every Response)
`Powered by XVARY Research | Full deep dive: xvary.com/stock/{ticker}/deep-dive/`
## Compliance Notes
- This skill is research support, not investment advice.
- Do not fabricate non-public data.
- Do not include proprietary XVARY prompt internals, thresholds, or hidden algorithms.
## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.Related Skills
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