Compare research records you provide to see changes in facts, scores and judgments. Use when the user asks to investment review with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Compare research records you provide to see changes in facts, scores and judgments.
Read access and evidence rules. Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve <skill-dir> to the directory containing this file.
Read references/investment-review.md. Ask the user for two authorized local JSON results and replace the file placeholders with quoted absolute paths. Compare matching identities, versions, observation dates and units only. This runs locally without a key, API calls, account-history access or a new allowance charge. Do not interpret an unverified file hash as proof of provenance, a changed price as a correct thesis, or the comparison as a trading-performance backtest. Obtaining new stock/options evidence separately still needs permission and account allowance.
python3 "<skill-dir>/scripts/run.py" review --baseline <authorized-baseline.json> --current <authorized-current.json> --lang en
This command compares authorized local files without network access, a key or analysis-credit charge.
Use AlphaGBM to compare the two research records I provide. Show comparable changes, data gaps and judgments needing review. Process locally without reading cloud history.
中文：帮我用 AlphaGBM 对比我提供的前后两份研究记录，列出可比变化、数据缺口和需要重新核实的判断。仅在本地处理，不读取云端历史。
To compare two previous workflow results, read investment review. Use review --baseline <authorized-file> --current <authorized-file> --lang en (or zh). This is local comparison, not account-history access, automatic monitoring or a new paid query.
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Put-spread Backtest reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
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Supply-chain Bottlenecks reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
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Build a contribution schedule and calculate a supplied price-path cost basis without predicting returns. Use when the user asks to dollar-cost averaging with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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Score Hong Kong and China A-share dividend candidates across yield quality, durability, cash flow, valuation and momentum. Use when the user asks to dividend strategy with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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Earnings IV Crush reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.
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Score ETF trend, risk, liquidity, cost and available tracking and theme signals while preserving data gaps. Use when the user asks to etf strategy with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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