alicloud-ai-search-dashvector

Category: provider

cinience/alicloud-skills|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

Installation Guide

Select your AI agent

How to use alicloud-ai-search-dashvector on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add alicloud-ai-search-dashvector

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/cinience/alicloud-skills --skill alicloud-ai-search-dashvector

Fetches alicloud-ai-search-dashvector from cinience/alicloud-skills and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/alicloud-ai-search-dashvector

Restart Cursor to activate alicloud-ai-search-dashvector. Access via /alicloud-ai-search-dashvector in your agent's command palette.

⚠

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Normalized operations

Create collection

name (str)

dimension (int)

metric (str: cosine | dotproduct | euclidean)

fields_schema (optional dict of field types)

Upsert docs

docs list of {id, vector, fields} or tuples

Supports sparse_vector and multi-vector collections

Query docs

vector or id (one required; if both empty, only filter is applied)

topk (int)

filter (SQL-like where clause)

output_fields (list of field names)

include_vector (bool)

Quickstart (Python SDK)

import os import dashvector from dashvector import Doc client = dashvector.Client( api_key=os.getenv("DASHVECTOR_API_KEY"), endpoint=os.getenv("DASHVECTOR_ENDPOINT"), ) # 1) Create a collection ret = client.create( name="docs", dimension=768, metric="cosine", fields_schema={"title": str, "source": str, "chunk": int}, ) assert ret # 2) Upsert docs collection = client.get(name="docs") ret = collection.upsert( [ Doc(id="1", vector=[0.01] * 768, fields={"title": "Intro", "source": "kb", "chunk": 0}), Doc(id="2", vector=[0.02] * 768, fields={"title": "FAQ", "source": "kb", "chunk": 1}), ] ) assert ret # 3) Query ret = collection.query( vector=[0.01] * 768, topk=5, filter="source = 'kb' AND chunk >= 0", output_fields=["title", "source", "chunk"], include_vector=False, ) for doc in ret: print(doc.id, doc.fields)

Validation

mkdir -p output/alicloud-ai-search-dashvector for f in skills/ai/search/alicloud-ai-search-dashvector/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/alicloud-ai-search-dashvector/validate.txt

Pass criteria: command exits 0 and output/alicloud-ai-search-dashvector/validate.txt is generated.

alicloud-ai-search-dashvector

Installation Guide

How to use alicloud-ai-search-dashvector on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

Task Automation & Efficiency

Knowledge Enhancement

Quality Improvement

Install Skill

DashVector Vector Search

Prerequisites

Normalized operations

Create collection

Upsert docs

Query docs

Quickstart (Python SDK)

Script quickstart

Notes for Claude Code/Codex

Error handling

Validation

Output And Evidence

Workflow

References

Implementation Guide

Best Practices

When to Use This

Learning Path

Related Skills

alicloud-ai-audio-tts-voice-clone

google-search-console

multi-search-engine

boss-job-search

cn-ecommerce-search

web-search

Reviews

Discussion