Skill

role-algorithms:data-structures

Implements and selects optimal data structures — hash tables (chaining, open addressing, Robin Hood, cuckoo), balanced BSTs (AVL, Red-Black, B-trees), heaps (binary, Fibonacci, pairing), tries, skip lists, segment trees, Fenwick trees, Bloom filters, Count-Min Sketch, HyperLogLog, and Union-Find. Use when choosing structures for performance constraints, implementing custom collections, or optimizing memory access patterns.

Install

npx claudepluginhub rnavarych/alpha-engineer --plugin role-algorithms

Tool Access

This skill is limited to using the following tools:

ReadGrepGlobBash

Preview

- Choosing the right data structure before writing a collection or lookup mechanism

Supporting Assets

references/hash-and-trees.mdreferences/heaps-and-advanced.mdreferences/probabilistic-and-unionfind.md

SKILL.md

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Stats

Parent Repo Stars9

Parent Repo Forks0

Last CommitMar 3, 2026

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role-algorithms:data-structures

When to use

Choosing the right data structure before writing a collection or lookup mechanism

Implementing custom hash tables, trees, or heaps with specific performance requirements

Selecting between approximate data structures (Bloom filter, HyperLogLog, Count-Min Sketch)

Optimizing range queries or prefix operations on arrays

Implementing dynamic connectivity or equivalence class tracking

Moving from in-memory to disk-based storage structures

Core principles

Access pattern first — select for how the structure is queried, not what is most familiar

Load factor controls hash table performance — keep below 0.75 for open addressing

B-trees exist because disks exist — in-memory trees should not use disk-page logic

Segment tree when updates + queries interleave; Fenwick tree for prefix-only — do not overengineer

Union-Find is underused — path compression + union by rank gives near-O(1) for connectivity

Reference Files

references/hash-and-trees.md — hash collision strategies (chaining, open addressing, Robin Hood, cuckoo), perfect hashing, AVL vs Red-Black vs B-tree selection matrix

references/heaps-and-advanced.md — binary/d-ary/Fibonacci/pairing heap trade-offs, tries, radix trees, segment trees with lazy propagation, Fenwick trees, skip lists

references/probabilistic-and-unionfind.md — Bloom filters (optimal k formula, counting variant), Count-Min Sketch, HyperLogLog, Union-Find with path compression and union by rank, full selection decision matrix

Data Structures

When to use

Choosing the right data structure before writing a collection or lookup mechanism
Implementing custom hash tables, trees, or heaps with specific performance requirements
Selecting between approximate data structures (Bloom filter, HyperLogLog, Count-Min Sketch)
Optimizing range queries or prefix operations on arrays
Implementing dynamic connectivity or equivalence class tracking
Moving from in-memory to disk-based storage structures

Core principles

Access pattern first — select for how the structure is queried, not what is most familiar
Load factor controls hash table performance — keep below 0.75 for open addressing
B-trees exist because disks exist — in-memory trees should not use disk-page logic
Segment tree when updates + queries interleave; Fenwick tree for prefix-only — do not overengineer
Union-Find is underused — path compression + union by rank gives near-O(1) for connectivity

Reference Files

references/hash-and-trees.md — hash collision strategies (chaining, open addressing, Robin Hood, cuckoo), perfect hashing, AVL vs Red-Black vs B-tree selection matrix
references/heaps-and-advanced.md — binary/d-ary/Fibonacci/pairing heap trade-offs, tries, radix trees, segment trees with lazy propagation, Fenwick trees, skip lists
references/probabilistic-and-unionfind.md — Bloom filters (optimal k formula, counting variant), Count-Min Sketch, HyperLogLog, Union-Find with path compression and union by rank, full selection decision matrix