An advanced seven-layer analytical engine that converts business agendas into structured, cross-validated reports using dynamically generated Python code.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install full-link-data-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install full-link-data-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Full-Link Data Analysis Engine?
The Full-Link Data Analysis engine is an advanced, seven-layer system designed to dynamically convert raw business agendas and datasets into ready-to-present analytical reports. Rather than using pre-defined analytical routines, this engine writes Python analysis scripts on demand, tailoring calculations precisely to the active dataset using standard libraries like pandas, numpy, scipy, and scikit-learn.
By integrating this engine via Openclaw Skills, developers and enterprises can ensure reliable data parsing and validation. Each layer of the system operates as a strict quality gate, verifying user intent, data scope, and analytical assumptions before running automated cross-validations, yielding highly dependable strategic decisions.
Full-Link Data Analysis Engine Use Cases
- Inquiring on Business Metrics: Instantly investigate revenue drops, marketing conversion trends, or customer retention changes without writing custom Python scripts manually.
- Generating Polished Business Reports: Generate structured Feishu documents with clear executive summaries, detailed statistical charts, and predictive analysis.
- Interpreting High-Dimensional Datasets: Safely profile and cleanse multi-source files, databases, and API structures to identify relevant business variables and filter out noise.
- Scientific Trend and Cohort Analysis: Execute rigorous mathematical models such as MECE decompositions, hypothesis trees, conversion funnels, and customer cohort studies.
How Full-Link Data Analysis Engine Works
- L1 - Persona Understanding: Establishes target reader roles (e.g., manager, analyst), business decision situations, and primary success criteria to guide tone and technical depth.
- L2 - Data Scope Acquisition: Identifies available data inputs, dynamically inspecting schemas, evaluating data health, and flagging missing values.
- L3 - Analysis Data Scoping: Maps out exact data boundaries, isolating specific timeframes, filtering variables, and setting proper aggregation frequencies.
- L4 - Problem Decomposition: Breaks complex business objectives into solvable sub-problems using logical frameworks like MECE, Drill-downs, or Hypothesis Trees.
- L5 - Method Selection: Determines optimal statistical pathways (from 15 available methods) using three-dimensional data routing logic (agenda semantics, data structure, and analysis type).
- L6 - Execution & Computation: Generates custom Python workflows using statsmodels, scipy, and pandas to clean inputs, perform mathematical operations, and perform secondary cross-validation.
- L7 - Result Output: Packages findings, annotated confidence marks, and tactical recommendations into standard-compliant Feishu reports.
Full-Link Data Analysis Engine Setup
To integrate this engine into your automated Openclaw Skills workflows, make sure your agent environment is configured with Python 3.8+ and essential scientific libraries.
Install the required analysis tools using your command line interface:
pip install pandas numpy scipy scikit-learn statsmodels
Configure your local agent to load routing tables, method specifications, and Feishu templates by adding the reference configuration schemas to your project directory. This allows the agent to construct error-free Python execution scripts dynamically.
Full-Link Data Analysis Engine Data Schema & Taxonomy
The Full-Link Data Analysis engine outputs structured records, schemas, and configurations at every step of its execution lifecycle:
| Layer | Key Deliverable | Documentation Standard |
|---|---|---|
| L1 | Persona Profile | JSON-formatted summary containing role, context, and KPIs |
| L2 | Data Catalog | Schema specifications, null value indicators, and field types |
| L3 | Filter Boundary | Explicitly defined dimensions, time windows, and target bounds |
| L4 | Hypothesis Tree | MECE-based structured markdown breakdown of sub-issues |
| L5 | Route Strategy | Documented methodology choice (Primary and cross-validation plans) |
| L6 | Execution Log | Dynamic Python script, printed calculations, and error-catch flags |
| L7 | Feishu Report | Standard markdown layout complete with statistical confidence annotations |
Full-Link Data Analysis Engine Advanced Features
- Code-on-Demand Pipeline: Generates clean, robust Python manipulation scripts on the fly rather than using generic, rigid templates.
- Multi-Dimensional Routing Engine: Uses advanced routing algorithms to auto-select from 15 core analysis methods depending on query semantics and target structural constraints.
- Automatic Cross-Validation Quality Gates: Employs secondary methods to double-check statistical hypotheses, ensuring all recommendations remain scientifically valid.
- Designed for Openclaw Skills Integration: Easily hooks into enterprise workflow automations, translating complex statistical logic directly into clean Feishu markdown files.
SKILL.md
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