OpenClaw

moss-docs

Documentation and capabilities reference for Moss semantic search. Use for understanding Moss APIs, SDKs, and integration patterns.

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查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install coderomaster/moss

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Moss Agent Skills

Capabilities

Moss is the real-time semantic search runtime for conversational AI. It delivers sub-10ms lookups and instant index updates that run in the browser, on-device, or in the cloud - wherever your agent lives. Agents can create indexes, embed documents, perform semantic/hybrid searches, and manage document lifecycles without managing infrastructure. The platform handles embedding generation, index persistence, and optional cloud sync - allowing agents to focus on retrieval logic rather than infrastructure.

Skills

Index Management

Create Index: Build a new semantic index with documents and embedding model selection
Load Index: Load an existing index from persistent storage for querying
Get Index: Retrieve metadata about a specific index (document count, model, etc.)
List Indexes: Enumerate all indexes under a project
Delete Index: Remove an index and all associated data

Document Operations

Add Documents: Insert or upsert documents into an existing index with optional metadata
Get Documents: Retrieve stored documents by ID or fetch all documents
Delete Documents: Remove specific documents from an index by their IDs

Search & Retrieval

Semantic Search: Query using natural language with vector similarity matching
Keyword Search: Use BM25-based keyword matching for exact term lookups
Hybrid Search: Blend semantic and keyword search with configurable alpha weighting (Python SDK)
Metadata Filtering: Constrain results by document metadata (category, language, tags)
Top-K Results: Return configurable number of best-matching documents with scores

Embedding Models

moss-minilm: Fast, lightweight model optimized for edge/offline use (default)
moss-mediumlm: Higher accuracy model with reasonable performance for precision-critical use cases

SDK Methods

| JavaScript | Python | Description |...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/coderomaster/moss
Version: —
Commit: 0c9efc1a9a85c3a007a1bfc32535d48fb6217309
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Moss Agent Skills

## Capabilities

Moss is the real-time semantic search runtime for conversational AI. It delivers sub-10ms lookups and instant index updates that run in the browser, on-device, or in the cloud - wherever your agent lives. Agents can create indexes, embed documents, perform semantic/hybrid searches, and manage document lifecycles without managing infrastructure. The platform handles embedding generation, index persistence, and optional cloud sync - allowing agents to focus on retrieval logic rather than infrastructure.

## Skills

### Index Management

- **Create Index**: Build a new semantic index with documents and embedding model selection
- **Load Index**: Load an existing index from persistent storage for querying
- **Get Index**: Retrieve metadata about a specific index (document count, model, etc.)
- **List Indexes**: Enumerate all indexes under a project
- **Delete Index**: Remove an index and all associated data

### Document Operations

- **Add Documents**: Insert or upsert documents into an existing index with optional metadata
- **Get Documents**: Retrieve stored documents by ID or fetch all documents
- **Delete Documents**: Remove specific documents from an index by their IDs

### Search & Retrieval

- **Semantic Search**: Query using natural language with vector similarity matching
- **Keyword Search**: Use BM25-based keyword matching for exact term lookups
- **Hybrid Search**: Blend semantic and keyword search with configurable alpha weighting (Python SDK)
- **Metadata Filtering**: Constrain results by document metadata (category, language, tags)
- **Top-K Results**: Return configurable number of best-matching documents with scores

### Embedding Models

- **moss-minilm**: Fast, lightweight model optimized for edge/offline use (default)
- **moss-mediumlm**: Higher accuracy model with reasonable performance for precision-critical use cases

### SDK Methods

| JavaScript        | Python             | Description                    |...

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