A specialized tool for constructing hierarchical business tagging systems to optimize AI knowledge retrieval through precise intent mapping and recall.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install label-system
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 label-system 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 Domain Labeling System Architect?
The Domain Labeling System Architect is designed to transform raw domain knowledge into a structured, searchable taxonomy. By analyzing source documents and QA pairs, this skill helps developers build a multi-level business tagging hierarchy that characterizes specific business stages and scenarios.
This systematic approach is a core component of the Openclaw Skills ecosystem, enabling a sophisticated retrieval pipeline: Tag Matching -> Narrowed Recall -> Precise Reranking. It ensures that AI agents can navigate complex datasets with high accuracy by aligning knowledge granularity with business logic.
Domain Labeling System Architect Use Cases
- Enhancing RAG (Retrieval-Augmented Generation) performance by implementing a metadata-driven recall strategy.
- Organizing large-scale corporate or government knowledge bases into logical business stages and scenarios.
- Automating the classification of user intent across 13 fixed matter categories like service channels, time limits, and required materials.
- Auditing knowledge splitting quality to identify redundant or mismatched QA pairs within a dataset.
How Domain Labeling System Architect Works
- Domain Context Definition: The skill first identifies the core business functions and existing reference structures within the target domain.
- Source Data Analysis: It parses Excel-based datasets to extract information titles, body content, and existing QA structures to determine knowledge depth.
- Taxonomy Construction: Based on the data analysis, it builds a hierarchical business tag system covering primary business stages and detailed sub-scenarios.
- Standardized Labeling: It executes the tagging process, applying specific rules to distinguish between business-phase tags and intent-based matter classifications.
- Granularity Validation: The system performs a final audit to flag incorrect splitting, non-domain content, or inconsistent labeling logic.
Domain Labeling System Architect Setup
To begin using this architectural framework within the Openclaw Skills environment, prepare your data and initialize the configuration:
# Initialize the domain labeling workspace
openclaw init labeling-structure
# Validate your source Excel structure
openclaw check --file domain_knowledge.xlsx
Domain Labeling System Architect Data Schema & Taxonomy
The skill organizes data through a three-sheet Excel structure to ensure comprehensive coverage:
| Data Sheet | Key Metadata Fields | Analytical Purpose |
|---|---|---|
| Info Export | Title, Content, Category | Understanding business coverage and existing classification |
| QA Export | Question, Answer | Analyzing existing knowledge structures for retrieval |
| Split Knowledge | Title, Question, Answer | Executing the tagging and auditing the granularity of data splits |
Domain Labeling System Architect Advanced Features
- Dynamic Hierarchy Adjustment: Automatically scales tag depth from Level 1 to Level 3+ based on the specific density of the domain knowledge.
- Intent-Specific Mapping: Maps user questions to 13 immutable matter categories such as 'Consultation Channels', 'Processing Timeframes', and 'Fee Standards'.
- Strategic Label Prioritization: Implements a priority logic that favors specific business affiliations over generic window information tags.
- Quality Assurance Reporting: Generates detailed feedback on knowledge splitting errors, including mismatched titles and redundant content suggestions.
SKILL.md
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