semantic-search-setup-step-1-install-dependencies
Sub-skill of semantic-search-setup: Step 1: Install Dependencies (+5).
Best use case
semantic-search-setup-step-1-install-dependencies is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of semantic-search-setup: Step 1: Install Dependencies (+5).
Teams using semantic-search-setup-step-1-install-dependencies should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/step-1-install-dependencies/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How semantic-search-setup-step-1-install-dependencies Compares
| Feature / Agent | semantic-search-setup-step-1-install-dependencies | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of semantic-search-setup: Step 1: Install Dependencies (+5).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Step 1: Install Dependencies (+5)
## Step 1: Install Dependencies
```bash
pip install sentence-transformers numpy
# or
uv pip install sentence-transformers numpy
```
## Step 2: Database Schema
```python
import sqlite3
def create_embeddings_table(db_path):
conn = sqlite3.connect(db_path, timeout=30)
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS embeddings (
id INTEGER PRIMARY KEY,
*See sub-skills for full details.*
## Step 3: Embedding Generator
```python
from sentence_transformers import SentenceTransformer
import numpy as np
import os
class EmbeddingGenerator:
def __init__(self, model_name='all-MiniLM-L6-v2'):
# Force CPU for stability
os.environ['CUDA_VISIBLE_DEVICES'] = ''
*See sub-skills for full details.*
## Step 4: Batch Processing
```python
def generate_all_embeddings(db_path, batch_size=100):
"""Generate embeddings for all chunks."""
conn = sqlite3.connect(db_path, timeout=30)
cursor = conn.cursor()
generator = EmbeddingGenerator()
# Get chunks without embeddings
cursor.execute('''
*See sub-skills for full details.*
## Step 5: Semantic Search
```python
def semantic_search(db_path, query, top_k=10):
"""Find most similar chunks to query."""
conn = sqlite3.connect(db_path, timeout=30)
cursor = conn.cursor()
generator = EmbeddingGenerator()
query_embedding = generator.embed_text(query)
# Get all embeddings
*See sub-skills for full details.*
## Step 6: Background Service
```bash
#!/bin/bash
# embed-service.sh - Background embedding service
DB_PATH="${1:-./knowledge.db}"
BATCH_SIZE="${2:-100}"
LOG_FILE="/tmp/embed.log"
PID_FILE="/tmp/embed.pid"
start() {
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