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.
AI memory system for coding agents — code index + cognitive facts, persistent across sessions.
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 gladego/index1Inspect 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.
AI memory system for coding agents with BM25 + vector hybrid search. Provides 6 MCP tools for intelligent code/doc search and cognitive fact recording.
recall, learn, read, status, reindex, config)# Recommended
pipx install index1
# Or via pip
pip install index1
# Or via npm (auto-installs Python package)
npx index1@latest
One-click plugin setup:
index1 setup # Auto-configure hooks + MCP for Claude Code
Verify:
index1 --version
index1 doctor # Check environment
Create .mcp.json in your project root:
{
"mcpServers": {
"index1": {
"type": "stdio",
"command": "index1",
"args": ["serve"]
}
}
}
If
index1is not in PATH, use the full path fromwhich index1.
Add to your project's .claude/CLAUDE.md:
## Search Strategy
This project has index1 MCP Server configured (recall + 5 other tools). When searching code:
1. Known identifiers (function/class/file names) -> Grep/Glob directly (4ms)
2. Exploratory questions ("how does XX work") -> recall first, then Grep for details
3. CJK query for English code -> must use recall (Grep can't cross languages)
4. High-frequency keywords (50+ expected matches) -> prefer recall (saves 90%+ context)
Impact:
Without rules: Grep "search" -> 881 lines -> 35,895 tokens
With rules: recall -> 5 summaries ->...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# index1
AI memory system for coding agents with BM25 + vector hybrid search. Provides 6 MCP tools for intelligent code/doc search and cognitive fact recording.
## What it does
- **Dual memory**: corpus (code index) + cognition (episodic facts)
- **Hybrid search**: BM25 full-text + vector semantic search with RRF fusion
- **Structure-aware chunking**: Markdown, Python, Rust, JavaScript, plain text
- **MCP Server**: 6 tools (`recall`, `learn`, `read`, `status`, `reindex`, `config`)
- **CJK optimized**: Chinese/Japanese/Korean query detection with dynamic weight tuning
- **Built-in ONNX embedding**: Vector search works out of the box, no Ollama required
- **Graceful degradation**: Works without any embedding service (BM25-only mode)
## Install
```bash
# Recommended
pipx install index1
# Or via pip
pip install index1
# Or via npm (auto-installs Python package)
npx index1@latest
```
One-click plugin setup:
```bash
index1 setup # Auto-configure hooks + MCP for Claude Code
```
Verify:
```bash
index1 --version
index1 doctor # Check environment
```
## Setup MCP
Create `.mcp.json` in your project root:
```json
{
"mcpServers": {
"index1": {
"type": "stdio",
"command": "index1",
"args": ["serve"]
}
}
}
```
> If `index1` is not in PATH, use the full path from `which index1`.
## Add Search Rules
Add to your project's `.claude/CLAUDE.md`:
```markdown
## Search Strategy
This project has index1 MCP Server configured (recall + 5 other tools). When searching code:
1. Known identifiers (function/class/file names) -> Grep/Glob directly (4ms)
2. Exploratory questions ("how does XX work") -> recall first, then Grep for details
3. CJK query for English code -> must use recall (Grep can't cross languages)
4. High-frequency keywords (50+ expected matches) -> prefer recall (saves 90%+ context)
```
**Impact**:
```
Without rules: Grep "search" -> 881 lines -> 35,895 tokens
With rules: recall -> 5 summaries ->...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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