Skill: openlark/full-link-data-analysis (version 1.0.0)

```markdown --- name: full-link-data-analysis description: Full-Link Data Analysis Engine: From business议题 to analytical report with complete seven-layer architecture. Built-in 15 analysis methods, supports data-aware routing (议题 semantics + data structure + problem type three dimensions), built-in quality assurance, outputs Feishu doc format analytical reports. --- # Full-Link Data Analysis ## Overview The seven-layer architecture automatically transforms business议题 into structured analytical reports: ``` L1 Persona Understanding → L2 Data Scope Acquisition → L3 Analysis Data Scoping → L4 Problem Decomposition → L5 Method Selection → L6 Execution & Computation → L7 Result Output ``` The Agent writes Python analysis code as needed, no pre-packaged code library required. Each layer acts as a quality gate for the next. ## Trigger Scenarios - User requests data/business metric analysis - User wants to understand causes/trends/patterns in data - User needs a data analysis report - User mentions "analyze", "attribute", "predict", "cluster", "trend", "data report" - User presents business议题 requiring data-driven insights. ## Core Principles - **Code on Demand**: Use `pandas`/`numpy`/`scipy`/`sklearn`/`statsmodels` to write analysis scripts tailored to the actual data. - **Data-Aware Routing**: Method selection综合考虑议题 semantics, data structure, and problem type. - **Quality Gate**: Every conclusion output must include confidence annotation and cross-validation description. ## Seven-Layer Process ### L1 — Persona Understanding Identify the questioner's role, decision-making scenario, and success criteria. Key Questions: - **Role**: Executive, analyst, product manager, operations personnel? - **Decision Scenario**: Strategic planning, operational review, special investigation? - **Success Criteria**: What metrics/goals define "good"? - **Data Literacy**: How technical should the report be? **Output**: Structured persona summary (role, decision scenario, success criteria). ### L2 — Data Scope Acquisition Discover and validate available data sources. Steps: 1. Ask user for provided data (files, databases, APIs) 2. Upon receiving files, check schema, sample data, inspect types and missing values 3. Identify relevant fields/tables 4. Confirm data timeliness (update frequency) **Output**: Data inventory (source, schema, quality notes). ### L3 — Analysis Data Scoping Narrow down from "all available data" to "data relevant to the议题". Steps: 1. Map business problem keywords to required data dimensions 2. Filter only necessary fields/records 3. Define time window, aggregation granularity, filter conditions 4. Identify potential confounding factors **Output**: Data scope specification (dimensions, time window, filters, aggregation granularity). ### L4 — Problem Decomposition Break down the business problem into analyzable sub-problems. Framework Selection: - **MECE** (Mutually Exclusive, Collectively Exhaustive): Revenue = Transaction Value × Customer Flow - **Drill-down**: Decompose by region → channel → product layer by layer - **Before/After**: Pre-change vs. post-change - **Cohort**: Group by time/attributes, compare trajectories - **Funnel**: Step-by-step conversion analysis - **Hypothesis Tree**: Structured hypothesis testing **Output**: Problem tree (decomposition structure with clear hypothesis statements). ### L5 — Method Selection Select analysis method based on data-aware routing. See `references/routing.md`. Three-Dimensional Routing: 1. **议题 Semantics**: Growth, churn, conversion, risk, attribution... 2. **Data Structure**: Time series, cross-sectional, panel, hierarchical... 3. **Problem Type**: Descriptive, diagnostic, predictive, prescriptive See `references/methods.md` (details on 15 methods). **Output**: Method plan (primary method + alternative cross-validation method). ### L6 — Execution & Computation Execute analysis using Python. Process: 1. **Environment Check**: `pip list` to confirm `pandas`, `numpy` availability; install missing packages 2. **Data Loading**: Load data according to L3 scope specification 3. **Data Cleaning**: Handle missing values, outliers, type conversions 4. **Analysis Execution**: Write and execute Python script for selected method(s) 5. **Cross-Validation**: Run comparison using alternative method (see `references/quality.md`) 6. **Result Interpretation**: Extract key figures, statistics, effect sizes Coding Standards: - Use `pandas` for data manipulation, `scipy.stats` for statistical tests, `statsmodels`/`sklearn` for modeling - Print results with clear labels; script output directly constitutes report content - Handle edge cases (empty data, all-null columns, single-category variables) - Output structured text rather than raw numbers ### L7 — Result Output Output analytical report in Feishu document format. See `references/feishu-report.md`. Report Structure: 1. **Analysis Overview** — Executive summary (1 paragraph) 2. **Key Findings** — Data-backed key insights 3. **Analysis Process** — Methods, data scope, key assumptions 4. **Detailed Results** — Chart descriptions, statistical results, effect sizes 5. **Conclusions & Recommendations** — Actionable recommendations with confidence annotations 6. **Appendix** — Method details, caveats, data quality notes ## Quick Reference | Layer | Action | Reference | |-------|--------|-----------| | L1 Persona | Identify role and decision context | Built-in question set | | L2 Data | Discover and validate data sources | Built-in check process | | L3 Scope | Narrow to relevant data | Built-in mapping logic | | L4 Decompose | Break down into sub-problems | Built-in frameworks | | L5 Method | Select analysis method | `references/methods.md` + `references/routing.md` | | L6 Execute | Write Python analysis | Built-in coding standards | | L7 Output | Generate report | `references/feishu-report.md` + `references/quality.md` | ## Checklist - [ ] L1: Confirm persona before accessing data - [ ] L2: Always check schema and sample before analysis - [ ] L3: Clearly state data scope (time, filters, dimensions) - [ ] L4: Must decompose first, do not jump directly to methods - [ ] L5: Consult routing table and document method selection rationale - [ ] L6: Run at least one cross-validation - [ ] L7: Each conclusion must include confidence annotation ```


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