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bloom-filter

Checks set membership using Bloom filters with a tunable false-positive rate.

Updated Jun 5, 2026
Search Tags:#hashing#data deduplication#bloom filter#probabilistic data structure#set membership
One-click install
npx skills add https://github.com/hung-phan/system-skills --skill bloom-filter
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bloom-filter
Source: https://github.com/hung-phan/system-skills/tree/main/skills/system-review/references/data-systems/bloom-filter
Command: npx skills add https://github.com/hung-phan/system-skills --skill bloom-filter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires murmurhash, xxhash, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a fast, space-efficient way to check membership in a set using Bloom filters, ideal for scenarios where exact set membership isn't critical.

Core Features & Use Cases

  • Membership Testing: Determines if an element is probably in a set, with a tunable false-positive rate.
  • Use Cases: Optimize join operations in large datasets, implement cache miss filters, and manage deduplication tasks in data streams.
  • Quick Start: Use the bloom-filter skill to check if the element 'example.com' is in the URL set.

Quick Start

Use the bloom-filter skill to check if 'example.com' is in the URL set.

Frequently Asked Questions about bloom-filter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I check set membership quickly without using a lot of storage space?▼

Use a Bloom filter to check set membership quickly while minimizing storage space. It provides probabilistic results, meaning it can confirm an element is probably in a set, making it highly space-efficient for lookups.

What is the best way to handle data deduplication in high-cardinality streams?▼

Handle data deduplication in high-cardinality streams by applying a Bloom filter. It efficiently identifies probable duplicate items with a tunable false-positive rate, preventing unnecessary downstream writes to authoritative storage.

How do Bloom filters reduce read amplification in LSM databases?▼

Bloom filters reduce read amplification in LSM databases by quickly checking if a key exists within a specific SSTable before performing disk reads. This prevents unnecessary disk access for non-existent keys, speeding up lookups.

Do I need MurmurHash or xxHash to implement a Bloom filter?▼

Yes, you need MurmurHash or xxHash to implement this Bloom filter. These hashing dependencies are required to generate the multiple hash values needed to map elements into the probabilistic data structure's bit array.

When should I not use a Bloom filter for set membership testing?▼

You should not use a Bloom filter for set membership testing when you require exact results with zero false positives. Because it is a probabilistic data structure, it can produce false positives, making it unsuitable for strict accuracy requirements.

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