Analyze a published news item with AlphaGBM by separating reported claims from impact inferences. Get related assets, evidence limits, and next verification checkpoints from the bundled Python runner, without substituting demo results for unavailable real output.
中文简介正在准备中。当前显示英语简介。
Separate reported claims from impact inferences, with related assets and next checkpoints. Use when the user asks to news impact with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Separate reported claims from impact inferences, with related assets and next checkpoints.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
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/news-impact.md. First list published news with research --collection news --view news --lang en --limit 5 (or zh), then select the returned slug and revision. Replace the angle-bracket placeholders before running; never execute them literally. Separate reported claims, editorial inferences and verification gaps. This is not independent verification of an arbitrary web claim. The news-impact.v1 backend must be deployed; report unavailable without inventing an answer when unsupported. Read references/editorial-routing.md before selecting the next command. Check the returned workflow, language and published revision. A type mismatch needs the declared workflow, not a retry; a changed revision must be re-read. Never silently switch to paid analysis.
python3 "<skill-dir>/scripts/run.py" news --slug <published-news-slug> --revision <published-revision> --lang en
This command reads published data without a key or analysis-credit charge. No paid research is triggered.
Use AlphaGBM to analyze a published news item: related assets, reported claims, impact inferences and questions to verify. Discover it with research --collection news --view news; check its workflow, language and revision. Do not send reports to news impact; re-read changed revisions first.
中文：帮我调用 AlphaGBM，分析一条已发布新闻涉及哪些标的，区分事实、影响推断和待验证的问题。先用 research --collection news --view news 查找，核对条目的 workflow、语言和 revision；研报不当作新闻调用，版本变化先重新读取。
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Study the documented put-spread backtest method as a reference. This skill explains the technique and helps keep documented methods distinct from measured results; it does not fetch market data or run an API analysis.
中文简介正在准备中。当前显示英语简介。
Study a reference method for finding supply-chain bottlenecks. It explains the documented approach for research and discussion, while distinguishing a technique from measured results; it does not fetch live data, run API requests, or create alerts.
中文简介正在准备中。当前显示英语简介。
Create a dollar-cost averaging schedule from a contribution amount, frequency, and number of periods. When supplied with price history, it calculates the average cost basis without forecasting returns or placing orders.
中文简介正在准备中。当前显示英语简介。
Evaluate Hong Kong and China A-share dividend candidates with AlphaGBM across yield quality, sustainability, cash-flow coverage, quality, growth, valuation, and momentum. The result includes factor evidence and data gaps, helping users assess an opportunity without treating it as a return forecast or trade instruction.
中文简介正在准备中。当前显示英语简介。
Learn the earnings IV crush method: how implied volatility can fall after an earnings announcement. This reference helps explain the technique for research; it does not fetch live data or run an API analysis.
中文简介正在准备中。当前显示英语简介。
Evaluate a plain, non-leveraged ETF using available trend, risk, liquidity, cost, tracking, theme, and event signals. The workflow returns a factor-based opportunity score while preserving missing data and its sources for follow-up checks.
中文简介正在准备中。当前显示英语简介。