nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Use OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches. Ideal for RAG and vector retrieval pipelines in Claude Code/Codex.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install cinience/alicloud-ai-search-opensearchInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Category: provider
Use the ha3engine SDK to push documents and execute HA/SQL searches. This skill focuses on API/SDK usage only (no console steps).
python3 -m venv .venv
. .venv/bin/activate
python -m pip install alibabacloud-ha3engine
OPENSEARCH_ENDPOINT (API domain)OPENSEARCH_INSTANCE_ID
OPENSEARCH_USERNAME
OPENSEARCH_PASSWORD
OPENSEARCH_DATASOURCE (data source name)OPENSEARCH_PK_FIELD (primary key field name)import os
from alibabacloud_ha3engine import models, client
from Tea.exceptions import TeaException, RetryError
cfg = models.Config(
endpoint=os.getenv("OPENSEARCH_ENDPOINT"),
instance_id=os.getenv("OPENSEARCH_INSTANCE_ID"),
protocol="http",
access_user_name=os.getenv("OPENSEARCH_USERNAME"),
access_pass_word=os.getenv("OPENSEARCH_PASSWORD"),
)
ha3 = client.Client(cfg)
def push_docs():
data_source = os.getenv("OPENSEARCH_DATASOURCE")
pk_field = os.getenv("OPENSEARCH_PK_FIELD", "id")
documents = [
{"fields": {"id": 1, "title": "hello", "content": "world"}, "cmd": "add"},
{"fields": {"id": 2, "title": "faq", "content": "vector search"}, "cmd": "add"},
]
req = models.PushDocumentsRequestModel({}, documents)
return ha3.push_documents(data_source, pk_field, req)
def search_ha():
# HA query example. Replace cluster/table names as needed.
query_str = (
"config=hit:5,format:json,qrs_chain:search"
"&&query=title:hello"
"&&cluster=general"
)
ha_query = models.SearchQuery(query=query_str)
req = models.SearchRequestModel({}, ha_query)
return ha3.search(req)
try:
print(push_docs().body)
print(search_ha())
except (TeaException, RetryError) as e:
print(e)
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
Category: provider
# OpenSearch Vector Search Edition
Use the ha3engine SDK to push documents and execute HA/SQL searches. This skill focuses on API/SDK usage only (no console steps).
## Prerequisites
- Install SDK (recommended in a venv to avoid PEP 668 limits):
```bash
python3 -m venv .venv
. .venv/bin/activate
python -m pip install alibabacloud-ha3engine
```
- Provide connection config via environment variables:
- `OPENSEARCH_ENDPOINT` (API domain)
- `OPENSEARCH_INSTANCE_ID`
- `OPENSEARCH_USERNAME`
- `OPENSEARCH_PASSWORD`
- `OPENSEARCH_DATASOURCE` (data source name)
- `OPENSEARCH_PK_FIELD` (primary key field name)
## Quickstart (push + search)
```python
import os
from alibabacloud_ha3engine import models, client
from Tea.exceptions import TeaException, RetryError
cfg = models.Config(
endpoint=os.getenv("OPENSEARCH_ENDPOINT"),
instance_id=os.getenv("OPENSEARCH_INSTANCE_ID"),
protocol="http",
access_user_name=os.getenv("OPENSEARCH_USERNAME"),
access_pass_word=os.getenv("OPENSEARCH_PASSWORD"),
)
ha3 = client.Client(cfg)
def push_docs():
data_source = os.getenv("OPENSEARCH_DATASOURCE")
pk_field = os.getenv("OPENSEARCH_PK_FIELD", "id")
documents = [
{"fields": {"id": 1, "title": "hello", "content": "world"}, "cmd": "add"},
{"fields": {"id": 2, "title": "faq", "content": "vector search"}, "cmd": "add"},
]
req = models.PushDocumentsRequestModel({}, documents)
return ha3.push_documents(data_source, pk_field, req)
def search_ha():
# HA query example. Replace cluster/table names as needed.
query_str = (
"config=hit:5,format:json,qrs_chain:search"
"&&query=title:hello"
"&&cluster=general"
)
ha_query = models.SearchQuery(query=query_str)
req = models.SearchRequestModel({}, ha_query)
return ha3.search(req)
try:
print(push_docs().body)
print(search_ha())
except (TeaException, RetryError) as e:
print(e)
```
## Script quickstart
```ba...AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
AI Agent Orchestrator with Skills System - Give AI Agents superpowers: memory search, code graph queries, agent-to-agent messaging. Manage Claude, Codex or any AI Agent from one dashboard. Move Agents between c...
AI agent skills for the Universal Registry - search, chat, and register 72,000+ agents across 14+ protocols. Works with Claude, Codex, Cursor, OpenClaw, and any AI assistant.
A cross-platform skills manager for AI IDEs. Search marketplace, download locally, and install to Claude, Cursor, Windsurf, and more with one click.