Efficient similarity search patterns for vector databases and semantic retrieval systems.
Works with
Covers four major vector database implementations: Pinecone, Qdrant, pgvector with PostgreSQL, and Weaviate, each with production-ready code templates
Explains three index types (Flat, HNSW, IVF+PQ) with trade-offs between search speed, recall accuracy, and data scale
Includes four distance metrics (Cosine, Euclidean, Dot Product, Manhattan) and guidance on when to use each
Demonstrates hybri
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsimilarity-search-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches similarity-search-patterns from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate similarity-search-patterns. Access via /similarity-search-patterns in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Patterns for implementing efficient similarity search in production systems.
| Metric | Formula | Best For | | ------------------ | ------------------ | --------------------- | --- | -------------- | | Cosine | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings | | Euclidean (L2) | √Σ(a-b)² | Raw embeddings | | Dot Product | A·B | Magnitude matters | | Manhattan (L1) | Σ | a-b | | Sparse vectors |
┌─────────────────────────────────────────────────┐
│ Index Types │
├─────────────┬───────────────┬───────────────────┤
│ Flat │ HNSW │ IVF+PQ │
│ (Exact) │ (Graph-based) │ (Quantized) │
├─────────────┼───────────────┼───────────────────┤
│ O(n) search │ O(log n) │ O(√n) │
│ 100% recall │ ~95-99% │ ~90-95% │
│ Small data │ Medium-Large │ Very Large │
└─────────────┴───────────────┴───────────────────┘
from pinecone import Pinecone, ServerlessSpec
from typing import List, Dict, Optional
import hashlib
class PineconeVectorStore:
def __init__(
self,
api_key: str,
index_name: str,
dimension: int = 1536,
metric: str = "cosine"
):
self.pc = Pinecone(api_key=api_key)
# Create index if not exists
if index_name not in self.pc.list_indexes().names():
self.pc.create_index(
name=index_name,
dimension=dimension,
metric=metric,
spec=ServerlessSpec(cloud="aws", region="us-east-1")
)
self.index = self.pc.Index(index_name)
def upsert(
self,
vectors: List[Dict],
namespace: str = ""
) -> int:
"""
Upsert vectors.
vectors: [{"id": str, "values": List[float], "metadata": dict}]
"""
# Batch upsert
batch_size = 100
total = 0
for i in range(0, len(vectors), batch_size):
batch = vectors[i:i + batch_size]
self.index.upsert(vectors=batch, namespace=namespace)
total += len(batch)
return total
def search(
self,
query_vector: List[float],
top_k: int = 10,
namespace: str = "",
filter: Optional[Dict] = None,
include_metadata: bool = True
) -> List[Dict]:
"""Search for similar vectors."""
results = self.index.query(
vector=query_vector,
top_k=top_k,
namespace=namespace,
filter=filter,
include_metadata=include_metadata
)
return [
{
"id": match.id,
"score": match.score,
"metadata": match.metadata
}
for match in results.matches
]
def search_with_rerank(
self,
query: str,
query_vector: List[float],
top_k: int = 10,
rerank_top_n: int = 50,
namespace: str = ""
) -> List[Dict]:
"""Search and rerank results."""
# Over-fetch for reranking
initial_results = self.search(
query_vector,
top_k=rerank_top_n,
namespace=namespace
)
# Rerank with cross-encoder or LLM
reranked = self._rerank(query, initial_results)
return reranked[:top_k]
def _rerank(self, query: str, results: List[Dict]) -> List[Dict]:
"""Rerank results using cross-encoder."""
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
pairs = [(query, r["metadata"]["text"]) for r in results]
scores = model.predict(pairs)
for result, score in zip(results, scores):
result["rerank_score"] = float(score)
return sorted(results, key=lambda x: x["rerank_score"], reverse=True)
def delete(self, ids: List[str], namespace: str = ""):
"""Delete vectors by ID."""
self.index.delete(ids=ids, namespace=namespace)
def delete_by_filter(self, filter: Dict, namespace: str = ""):
"""Delete vectors matching filter."""
self.index.delete(filter=filter, namespace=namespace)
from qdrant_client import QdrantClient
from qdrant_client.http import models
from typing import List, Dict, Optional
class QdrantVectorStore:
def __init__(
selfMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
kostja94/marketing-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
Useful defaults in similarity-search-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: similarity-search-patterns is focused, and the summary matches what you get after install.
We added similarity-search-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: similarity-search-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
similarity-search-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
similarity-search-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
similarity-search-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend similarity-search-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
similarity-search-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: similarity-search-patterns is focused, and the summary matches what you get after install.
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