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.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install cinience/alicloud-ai-search-opensearchExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
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)
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
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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