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Explains where data comes from, how metrics are calculated, and traces data lineage through the pipeline. Use when the user asks "where does this data come from?", "how is X calculated?", or "what does this metric mean?".
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use this skill when the user asks to "prepare an RFP response", "draft an RFP", "respond to an RFP", or needs to compile firm capabilities, track record, investment process, and compliance content for a proposal. Also use when the user mentions "Request for Proposal", "mandate proposal", "tender response", or "client proposal".
Use this skill when the user asks about a borrower's default probability, credit risk drivers, PD scores, risk ratings, or wants to run what-if scenarios on credit metrics like "what if revenue drops 20%" or "recalculate PD with adjusted inputs". Also use when explaining why a borrower received a particular risk rating, SHAP analysis, or comparing risk across borrowers.
Use this skill when the user asks which factors are driving returns, wants to compare factor performance across periods, or requests ad-hoc factor exploration like "which factors are working this quarter", "compare momentum vs value", or "factor correlations". Also use when the user needs analysis beyond what pre-computed factor views provide.
Use this skill when the user wants to optimize portfolio weights, find the efficient frontier, construct a maximum Sharpe or minimum variance portfolio, or rebalance with specific constraints like position caps, sector limits, or ESG minimums. Also use when the user says "what's the optimal allocation" or "rebalance for minimum risk".
Use this skill when the user asks about anomalies, risk flags, style drift, attribution drift, concentration alerts, or anything unusual in attribution patterns. Also use proactively when generating reports to check for HIGH severity items before presenting findings.
Use this skill when the user asks to "generate an attribution report", "prepare attribution for the board", "write up quarterly performance", or wants a comprehensive multi-section attribution narrative. Orchestrates multiple tools into a complete report with audience-appropriate depth.
| name | data-lineage-explanation |
| description | Explains where data comes from, how metrics are calculated, and traces data lineage through the pipeline. Use when the user asks "where does this data come from?", "how is X calculated?", or "what does this metric mean?". |
Trigger when the user asks:
Identify the semantic field name from the previous query (e.g., TOTAL_MARKET_VALUE, FIRM_AUM, AVG_RATING)
Map the Cortex Analyst tool that produced the data to its semantic view:
| Tool Name | Semantic View |
|---|---|
| quantitative_analyzer | SAM_DEMO.AI.SAM_ANALYST_VIEW |
| financial_analyzer | SAM_DEMO.AI.SAM_SEC_FINANCIALS_VIEW |
| implementation_analyzer | SAM_DEMO.AI.SAM_IMPLEMENTATION_VIEW |
| supply_chain_analyzer | SAM_DEMO.AI.SAM_SUPPLY_CHAIN_VIEW |
| stock_prices | SAM_DEMO.AI.SAM_STOCK_PRICES_VIEW |
| sec_financials | SAM_DEMO.AI.SAM_SEC_FINANCIALS_VIEW |
| executive_kpi_analyzer | SAM_DEMO.AI.SAM_EXECUTIVE_VIEW |
| factor_model_analyzer | SAM_DEMO.AI.SAM_FACTOR_MODEL_VIEW |
| regime_analyzer | SAM_DEMO.AI.SAM_REGIME_VIEW |
Call explain_data_origin with the semantic view name and field name
Your response MUST use ONLY information from the tool output:
[FIELD_NAME] is calculated as [plain language description of semantic_expression], drawn from [business description of physical_table] ([fully qualified table name]).
Data lineage ([N] levels upstream):
A structured lineage explanation following the Response Template above, using ONLY data from explain_data_origin tool output.
