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.
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 cinience/alicloud-ai-search-opensearch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。
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)
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。
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.
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