From antigravity-awesome-skills
Generates thesis-driven stock analysis from SEC EDGAR filings and market data via /analyze, /score, /compare commands with Python tools.
How this skill is triggered — by the user, by Claude, or both
Slash command
/antigravity-awesome-skills:xvary-stock-researchThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this skill to produce institutional-depth stock analysis in Claude Code using public EDGAR + market data.
Use this skill to produce institutional-depth stock analysis in Claude Code using public EDGAR + market data.
/compare and need a structured differential, not a prose-only chat answer./analyze {ticker}Run full skill workflow:
tools/edgar.py.tools/market.py.references/methodology.md.references/scoring.md./score {ticker}Run score-only workflow:
/compare {ticker1} vs {ticker2}Run side-by-side workflow:
/score logic for both tickers.For /analyze {ticker} use this shape:
Verdict (Constructive / Neutral / Cautious)Conviction Rationale (3-5 bullets)XVARY Scores (Momentum, Stability, Financial Health, Upside)Thesis Pillars (3-5 pillars)Top Risks (3 items)Kill Criteria (thesis-invalidating conditions)Financial Snapshot (revenue, margin proxy, cash flow, leverage snapshot)Next Checks (what to watch over next 1-2 quarters)For /score {ticker} use this shape:
For /compare {ticker1} vs {ticker2} use this shape:
references/methodology.mdreferences/scoring.mdreferences/edgar-guide.mdtools/edgar.pytools/market.pyIf a tool call fails, state exactly what data is missing and continue with available inputs. Do not hallucinate missing figures.
Powered by XVARY Research | Full deep dive: xvary.com/stock/{ticker}/deep-dive/
npx claudepluginhub sickn33/antigravity-awesome-skills --plugin antigravity-awesome-skillsThesis-driven equity analysis using public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools.
Routes equity research workflows including stock analysis, multi-stock comparison, research memos, merger-arb memos, and profit-taking analysis. Uses LLMQuant Data for fundamentals, filings, ownership, and events.
Chains InvestSkill modules for structured stock research: business analysis, valuation, market signals, technicals into unified investment thesis with composite score. Use for due diligence.