Xvary Stock Research

XVARY Stock Research Skill

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/xvary-stock-research from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

XVARY Stock Research Skill Use this skill to produce institutional-depth stock analysis in Claude Code using public EDGAR + market data.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Output Format, Data Tooling, Footer (Required on Every Response), Compliance Notes, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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.
• Use when the request clearly matches the imported source intent: Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: 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

Examples

Example 1: Ask for the upstream workflow directly

Use @xvary-stock-research to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @xvary-stock-research against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @xvary-stock-research for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @xvary-stock-research using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: 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.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• 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.
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.

Imported Operating Notes

Imported: 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.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/xvary-stock-research, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• edgar-guide.md
• methodology.md
• scoring.md
• nvda-analysis.md
• nvda-deep-dive-hero.png
• nvda-deep-dive-scenarios.png
• nvda-deep-dive-thesis.png
• social-preview.png

Imported Reference Notes

Imported: Scoring + Methodology References

• Methodology: references/methodology.md
• Score definitions: references/scoring.md
• EDGAR usage guide: references/edgar-guide.md

Imported: 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.

Imported: Footer (Required on Every Response)

Powered by XVARY Research | Full deep dive: xvary.com/stock/{ticker}/deep-dive/

Imported: 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.

Imported: 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.