data-analyst-sz

Builds and maintains a domain knowledge pack for the data-analysis Skill the way a human analyst would — studying the raw material page by page, synthesising one Understanding Handbook, then deriving the full set of pack files (triggers, README, framework, glossary, computation-reference, data-spec, context-notes, insight-extensions, business-rules, plus an optional design-system slot) from that handbook plus the data profile. It is a research workflow, not an extraction pipeline — the agent reads decks and data visually and thoroughly, writes a handbook, then derives. Use when the user wants to create a domain pack for a new analysis area, refresh an existing pack after new material, new data, or an SME correction, or turn training decks and a sample analysis and a data file into pack files. Triggers include "build/refresh a domain pack", "把这些材料研读成 domain pack", "先写理解手册再派生 pack", and "刷新一下 domain pack".

Runs structured AI-native data analysis on tabular datasets (Excel / CSV / Parquet / DataFrame). Pluggable domain architecture — Step 0 reads `references/resolver.md` (a domain-neutral scoring mechanism) plus each registered domain's own `domains/<slug>/triggers.md` to detect which analytical domain applies; all domain knowledge and trigger words live in the domain packs, never in this Skill or the resolver. Drives every analysis through a deterministic 0+9-step workflow (Domain Detect → Profile → Signal Scan → Clarify → Frame → Hypothesize → Verify → Decompose → Synthesize → Follow-up). Hard rules against hallucination — LLM never computes numbers, every claim cites a verdict_id, state persists to JSON. Auto-produces default insights without asking the user what to analyze. Use whenever the user uploads structured data, asks for "analysis" / "insights" / "default insights" / "what does this say" / "默认洞察" / "分析这份数据", asks "why is X declining / growing / changing", or any structured-data analytical query that