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.
Memory Search
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 aigentic-net/memory-search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。
You have two tools for recalling information from your memory files. Use them.
memory_searchSemantic vector search across your indexed memory files (MEMORY.md, memory/*.md, and session transcripts).
Parameters:
| Param | Type | Required | Description |
|---|---|---|---|
query |
string | yes | Natural language question or topic to search for |
maxResults |
number | no | Max results to return (default: 6) |
minScore |
number | no | Minimum relevance score threshold (0-1) |
Example calls:
{ "query": "what projects is the human working on" }
{ "query": "preferences about code style", "maxResults": 3 }
{ "query": "important dates birthdays deadlines", "maxResults": 10, "minScore": 0.3 }
Returns: Array of results, each with:
snippet — the matching text chunkpath — relative file path (e.g. MEMORY.md, memory/2026-02-07.md)startLine / endLine — line range in the source filescore — relevance scorecitation — formatted source reference (in direct chats)memory_getRead a specific section of a memory file by path and line range. Use this after memory_search to pull more context around a result.
Parameters:
| Param | Type | Required | Description |
|---|---|---|---|
path |
string | yes | Relative path from workspace (e.g. MEMORY.md, memory/2026-02-07.md) |
from |
number | no | Starting line number |
lines |
number | no | Number of lines to read |
Example calls:
{ "path": "MEMORY.md" }
{ "path": "memory/2026-02-07.md", "from": 15, "lines": 30 }
Always search before answering about:
The pattern is:
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。
# Memory Search
You have two tools for recalling information from your memory files. Use them.
## Tools
### `memory_search`
Semantic vector search across your indexed memory files (MEMORY.md, memory/*.md, and session transcripts).
**Parameters:**
| Param | Type | Required | Description |
|---|---|---|---|
| `query` | string | yes | Natural language question or topic to search for |
| `maxResults` | number | no | Max results to return (default: 6) |
| `minScore` | number | no | Minimum relevance score threshold (0-1) |
**Example calls:**
```json
{ "query": "what projects is the human working on" }
{ "query": "preferences about code style", "maxResults": 3 }
{ "query": "important dates birthdays deadlines", "maxResults": 10, "minScore": 0.3 }
```
**Returns:** Array of results, each with:
- `snippet` — the matching text chunk
- `path` — relative file path (e.g. `MEMORY.md`, `memory/2026-02-07.md`)
- `startLine` / `endLine` — line range in the source file
- `score` — relevance score
- `citation` — formatted source reference (in direct chats)
### `memory_get`
Read a specific section of a memory file by path and line range. Use this after `memory_search` to pull more context around a result.
**Parameters:**
| Param | Type | Required | Description |
|---|---|---|---|
| `path` | string | yes | Relative path from workspace (e.g. `MEMORY.md`, `memory/2026-02-07.md`) |
| `from` | number | no | Starting line number |
| `lines` | number | no | Number of lines to read |
**Example calls:**
```json
{ "path": "MEMORY.md" }
{ "path": "memory/2026-02-07.md", "from": 15, "lines": 30 }
```
## When to Use Memory Search
**Always search before answering about:**
- Prior conversations or decisions
- The human's preferences, habits, or opinions
- Dates, deadlines, birthdays, events
- Project status or history
- Anything the human said "remember this" about
- Todos, action items, or commitments
- People, names, relationships
**The pattern is:**
1. Receive a question that m...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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