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1.4K downloads. Trust evidence available

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agentCLAWHUBUnverified

alibabacloud-lindorm-agent-skill

Use this Skill for Alibaba Cloud Lindorm work: instance lifecycle and configuration, networking and access control, monitoring, performance, storage, connection diagnosis, backup, migration, permissions (including Lindorm SQL user management with `CREATE USER` and `GRANT`), slow queries, SQL development, and Search, vector, graph, AI, multimodal, or knowledge-base workflows. Trigger on Lindorm product or CLI terms such as LindormTable, LindormTSDB, LindormSearch, Lindorm AI, HBase/AliHBase, lindormcli, Lindorm CLI, `aliyun lindorm`, or Chinese requests mentioning 宽表引擎、时序引擎、搜索引擎、向量检索、图引擎、白名单、实例创建/扩缩容/释放. Use this Skill's references or official Alibaba Cloud documentation; do not invent Lindorm-specific facts from general training knowledge.

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.4k

Updated

Oct 10, 2026

Version

0.0.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.4K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.4K downloadsadoption · observed Oct 10, 2026
Latest release: 0.0.3release · observed Oct 9, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: medium.

clawhub skill install s173swjet2yrebzqrp6hjkvmy583mxef:alibabacloud-lindorm-agent-skill

Install using `clawhub skill install s173swjet2yrebzqrp6hjkvmy583mxef:alibabacloud-lindorm-agent-skill` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/sdk-team/alibabacloud-lindorm-agent-skill before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-sdk-team-alibabacloud-lindorm-agent-skill/snapshot"

Documentation

CLAWHUB

149,511 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: alibabacloud-lindorm-agent-skill
description: |
  Use this Skill for Alibaba Cloud Lindorm work: instance lifecycle and configuration, networking and access control, monitoring, performance, storage, connection diagnosis, backup, migration, permissions (including Lindorm SQL user management with `CREATE USER` and `GRANT`), slow queries, SQL development, and Search, vector, graph, AI, multimodal, or knowledge-base workflows. Trigger on Lindorm product or CLI terms such as LindormTable, LindormTSDB, LindormSearch, Lindorm AI, HBase/AliHBase, lindormcli, Lindorm CLI, `aliyun lindorm`, or Chinese requests mentioning 宽表引擎、时序引擎、搜索引擎、向量检索、图引擎、白名单、实例创建/扩缩容/释放. Use this Skill's references or official Alibaba Cloud documentation; do not invent Lindorm-specific facts from general training knowledge.
metadata:
  openclaw:
    requires:
      bins: ["aliyun"]
    homepage: https://clawhub.ai/sdk-team/alibabacloud-lindorm-agent-skill
---

# Lindorm Agent Skill

Alibaba Cloud Lindorm cloud native multi-model database Skill. Covers three domains: **Operations Management**, **Developer Guidance**, and **Reference Materials**. Developer guidance includes classic SQL/search usage plus vector retrieval, Lindorm AI engine calls, multimodal image-text search, and private knowledge base search.

## Core Capability Matrix

| Category | Sub-Scenarios | Reference Docs |
|---------|--------------|----------------|
| **01-Dev Guidance** | Connection setup, quick start, SQL guide, table design, search engine usage, vector retrieval, graph engine usage, AI engine calls, multimodal search, knowledge search | `references/01-dev/` |
| **02-Ops Management** | Instance mgmt, instance lifecycle (create/scale/release/pay-type), monitoring, error troubleshooting, storage analysis, connection diagnostics, network access control, backup & restore, migration, permissions, slow query | `references/02-ops/` |
| **03-Reference** | Aliyun CLI command reference, Lindorm CLI (SQL client) guide, HBase Shell guide, RAM permissions, acceptance criteria | `references/03-ref/` |

## Decision Tree

```
User Request
├── Connection / DDL / SQL / Code examples → 01-dev
│   ├── Connection address / code → references/01-dev/connection-guide.md
│   ├── DDL / write / query examples → references/01-dev/quick-start-guide.md
│   ├── SQL connection & SQL-based application development → references/01-dev/sql-client-guide.md
│   ├── SQL syntax reference → references/01-dev/sql-operations.md
│   ├── MySQL compatibility → references/01-dev/sql-usage-notes.md
│   ├── Table design guide → references/01-dev/table-design.md
│   ├── Search engine standalone usage → references/01-dev/search-guide.md
│   ├── Vector engine usage through Search / Wide Table → references/01-dev/vector-guide.md
│   ├── Graph engine usage (Gremlin / Schema / query) → references/01-dev/graph-guide.md
│   ├── Lindorm AI engine model calls → references/01-dev/ai-guide.md
│   ├── Multimodal image-text search scene → references/01-dev/m

_meta.json

{
  "ownerId": "kn74p5w8ywv6prh40g0s82gmqh83nw54",
  "slug": "alibabacloud-lindorm-agent-skill",
  "version": "0.0.3",
  "publishedAt": 1791538782971
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references/01-dev/ai-guide.md

# Lindorm AI Engine Guide

This guide describes how to use the Lindorm AI engine independently. The AI engine provides DashScope-compatible APIs for embeddings, visual understanding, reranking, and chat-based answer generation. Application code and agents should call models through the AI engine built into the Lindorm instance. Authentication uses the instance username and password through the `x-ld-ak` and `x-ld-sk` request headers. Do not use external platform API keys.

## Connection and Connectivity

The AI engine always uses port `9002`. Public endpoints usually contain `-proxy-ai-pub`; VPC endpoints usually contain `-proxy-ai-vpc`.

| Network type | Endpoint example | Applicable environment |
|--------------|------------------|-------------------------|
| VPC private network | `<instance_id>-proxy-ai-vpc.lindorm.aliyuncs.com:9002` | Search pipelines, ECS, and services inside the VPC |
| Public network | `<instance_id>-proxy-ai-pub.lindorm.aliyuncs.com:9002` | Local computers or public-network clients |

Before making public-network calls, confirm that the public endpoint of the AI engine is enabled and that the IP whitelist is configured. The public endpoint of the search engine and the public endpoint of the AI engine are different entries. Do not assume that the AI engine can be called just because the search engine public endpoint is enabled.

### Connectivity check for port 9002

```bash
curl --connect-timeout 10 -m 60 \
  -H 'Content-Type: application/json' \
  -H 'x-ld-ak: <username>' \
  -H 'x-ld-sk: <password>' \
  -XPOST "http://<ai_endpoint>:9002/dashscope/compatible-mode/v1/embeddings" \
  -d '{
    "model": "text-embedding-v4",
    "input": "connectivity test"
  }'
```

A successful response should include embedding data. If the response returns `401` or `403`, first check whether `x-ld-ak` and `x-ld-sk` come from the same Lindorm instance.

## Model Configuration

| Model type | Typical model | Purpose | Key check |
|------------|---------------|---------|-----------|
| Text embedding | `text-embedding-v4` | Vectorize text chunks in a knowledge base | The output dimension must equal the vector index dimension |
| Multimodal embedding | `qwen2.5-vl-embedding` / `qwen3-vl-embedding` | Build a unified image-text vector space for image-to-image and text-to-image search | Image and text queries must be written to the same vector field |
| VL | `qwen3-vl-plus` / `qwen3-vl-flash` | Recognize image URLs and generate image descriptions | The image URL must be accessible by the AI engine |
| Rerank | `qwen3-rerank` / `gte-rerank-v2` | Rerank recalled candidates by query relevance | Preserve the original candidate array and map results back through `results[*].index` |
| Chat | `qwen-plus` / `qwen3.5-plus` | Generate knowledge-base answers | The prompt must restrict the model to answer only from the recalled context |

If the embedding model dimension and the vector index dimension are inconsistent, writes or queries will fail. Record `em

references/01-dev/connection-guide.md

# Connection Information Retrieval Scenario

When the user asks "how do I connect to an instance", "what is the connection endpoint", or "which SDK do I need", follow this guide.

## Trigger Conditions

Typical user expressions:
- "How do I connect to ld-xxx?"
- "Give me the connection endpoint."
- "How do I connect with Java?"
- "What is the port of the time series engine?"
- "Give me a connection example."

## Core Principles

**The agent is a solution provider, not a pointer to documentation**:
1. **Extract key information** and organize it into a complete answer, including code examples, dependency configuration, and parameter descriptions.
2. **Let the user obtain executable connection code inside the conversation** without leaving the chat.
3. If the connection endpoint cannot be obtained from an API, **clearly provide the exact console path**, down to the button location.
4. Documentation links are supplementary references for users who want deeper details.

---

## Execution Flow

### Phase 1: Obtain basic instance information

Run the following commands to obtain the architecture version, connection endpoints, and network configuration of the instance:

```bash
# 1. Get instance details and identify the V1/V2 architecture
aliyun lindorm v1 instance describe <instance-id> --lindorm-region <region>

# 2. Get connection endpoints for each engine
aliyun lindorm v1 instance engine-list <instance-id> --lindorm-region <region>
```

**Key information to extract**:

| Item | Source field | Description |
|------|--------------|-------------|
| Architecture version | `service_type` from describe | `lindorm_v2*` = V2 architecture; `lindorm` = V1 architecture |
| Connection endpoint | `connection_string` / `port` from engine-list, applicable to V1 and V2 | Domain name and port of each engine |
| Network type | `net_type` from engine-list | `PUBLIC` = public network; `VPC` = VPC only. The raw value remains available as `net_type_code`: `"0"` for public and `"2"` for VPC. |
| Engine version | `engines[].version` from describe | Version of each engine |

> **Note**: `v1|v2 instance engine-list` returns a flattened engine-by-address array for both V1 and V2, including `net_type` and `connection_string`. The V2-only `v2 instance describe` command also returns `connect_address_list` with `type=INTRANET/INTERNET`. See Phase 2.

**Endpoint domain format**:

For endpoint formats, see [sql-client-guide.md](sql-client-guide.md). It includes V1/V2 `service_type` identification logic and complete examples.

---

### Phase 2: Confirm connection prerequisites

Before providing connection code, confirm the following two items.

#### 1. Public-network access check

**Method 1: Use `v1|v2 instance engine-list`, applicable to V1 and V2**

Check the `net_type` field in the returned array:
- `PUBLIC`, with `net_type_code: "0"`: public network available
- `VPC`, with `net_type_code: "2"`: VPC private network only

**Method 2: Use `v2 instance describe`, V2 only**

Check t

references/01-dev/graph-guide.md

# Lindorm Graph Engine Guide

This guide explains how to access and use the Lindorm graph engine. The graph engine exposes a Gremlin-compatible service through the unified port `16032` and supports both HTTP REST and WebSocket access. All graph operations, including Schema management, writes, queries, vector search, multi-graph management, and permission management, use this port.

## Core Principles

| Principle | Description |
|------|------|
| Schema first | Before any vertex or edge write or query, initialize the Schema through `/schema/mgmt/apply`. Labels and properties that are not defined cannot be written. |
| Two access methods | REST (`POST /gremlin/{db}`) is suitable for curl and troubleshooting. WebSocket (`ws://host:port/gremlin/{db}`) is suitable for persistent Python SDK connections and parameter bindings. |
| `G___` binding prefix | gremlinpython passes parameters through `bindings`. All variable names must use the `G___` prefix, such as `G___id` and `G___label`, and must be referenced directly without quotes in the DSL. |
| All operations use the `default` graph by default | Omitting `{db}` from the URL is equivalent to `/gremlin/default`. **Do not proactively mention or recommend multi-graph features in an answer.** Use the multi-graph section only when the user explicitly asks about creating graphs, multi-graph management, graph isolation, or equivalent topics. |
| Vector search supports HNSW only | Query a vertex `VECTOR_FLOAT` property with `hasVector(prop, vec, topK)`. The index type is fixed to `HNSW`, and **writes take effect immediately** without a manual build. |
| Vector and set properties are vertex-only | `VECTOR_FLOAT` and `cardinality: set` are supported **only on vertices**. Edges do not support vector or multi-valued properties. |
| Build a graph from an image | When the user provides a sketch, ER diagram, or relationship diagram, extract vertex types, edge types, and connections from the image and generate the corresponding Schema. |
| Output requirement | At the **end** of every graph-engine answer, provide one complete, directly runnable Python example covering connection, Schema, writes, and queries. |
| Secondary property index | Create a `SECONDARY` index for properties frequently used by `has()` filters. Declare `indexType` on the property during initial graph creation. If the index is added later, build it to cover existing data. |

## Graph Engine Port and Authentication

| Item | Value | Description |
|------|-----|------|
| Port | `16032` | Gremlin service port for both REST and WebSocket traffic. |
| REST URL | `http://<host>:16032/gremlin/{db}` | curl or HTTP clients; POST JSON such as `{"gremlin": "..."}`. |
| WebSocket URL | `ws://<host>:16032/gremlin/{db}` | Persistent gremlinpython `client.Client(...)` connections. |
| Schema management API | `http(s)://<host>:16032/schema/mgmt/{apply\|addProperty\|addVertexLabel\|addEdgeLabel\|list}?db={db}` | HTTP REST only, using POST or GET with JSON. |
| Mult

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