用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/duclm1x1/Dive-Ai --skill local-places命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
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基于 SOC 职业分类
| name | local-places |
| description | Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost. |
| homepage | https://github.com/Hyaxia/local_places |
| metadata | {"clawdbot":{"emoji":"📍","requires":{"bins":["uv"],"env":["GOOGLE_PLACES_API_KEY"]},"primaryEnv":"GOOGLE_PLACES_API_KEY"}} |
Find places, Go fast
Search for nearby places using a local Google Places API proxy. Two-step flow: resolve location first, then search.
cd {baseDir}
echo "GOOGLE_PLACES_API_KEY=your-key" > .env
uv venv && uv pip install -e ".[dev]"
uv run --env-file .env uvicorn local_places.main:app --host 127.0.0.1 --port 8000
Requires GOOGLE_PLACES_API_KEY in .env or environment.
Check server: curl http://127.0.0.1:8000/ping
Resolve location:
curl -X POST http://127.0.0.1:8000/locations/resolve \
-H "Content-Type: application/json" \
-d '{"location_text": "Soho, London", "limit": 5}'
curl -X POST http://127.0.0.1:8000/places/search \
-H "Content-Type: application/json" \
-d '{
"query": "coffee shop",
"location_bias": {"lat": 51.5137, "lng": -0.1366, "radius_m": 1000},
"filters": {"open_now": true, "min_rating": 4.0},
"limit": 10
}'
curl http://127.0.0.1:8000/places/{place_id}
location_bias from chosen locationfilters.types: exactly ONE type (e.g., "restaurant", "cafe", "gym")filters.price_levels: integers 0-4 (0=free, 4=very expensive)filters.min_rating: 0-5 in 0.5 incrementsfilters.open_now: booleanlimit: 1-20 for search, 1-10 for resolvelocation_bias.radius_m: must be > 0{
"results": [
{
"place_id": "ChIJ...",
"name": "Coffee Shop",
"address": "123 Main St",
"location": {"lat": 51.5, "lng": -0.1},
"rating": 4.6,
"price_level": 2,
"types": ["cafe", "food"],
"open_now": true
}
],
"next_page_token": "..."
}
Use next_page_token as page_token in next request for more results.