data-structures-algorithms

Use when solving algorithmic problems, optimizing code, or choosing the right data structure for performance.

Security Scan

This is an instruction-only helper for algorithms and data structures with no installs, no credentials, and no unexpected behaviors — it appears to do what it claims.

Purpose & Capability

Name and description match the SKILL.md content: guidance on complexity, data structures, and algorithmic patterns. There are no unrelated requirements (no binaries, env vars, or installs).

Instruction Scope

The runtime instructions are purely educational text and do not direct the agent to read files, call external services, or access environment variables. Some table entries are simplified (e.g., graph insert/delete complexities, or contextual details for array/linked-list operations) but these are content-accuracy notes rather than security concerns.

Install Mechanism

No install spec and no code files — lowest-risk pattern (instruction-only). Nothing will be downloaded or written to disk by the skill itself.

Credentials

The skill requests no environment variables, no credentials, and no config paths. There is no disproportionate access requested.

Persistence & Privilege

always is false and the skill is user-invocable. It does not request elevated or persistent system presence or modify other skills' configs.

Assessment

This skill is low-risk: it only contains educational guidance and does not request credentials or install software. If you plan to rely on it for technical correctness, review its algorithmic claims against authoritative sources (textbook or documentation) because some complexity entries are simplified. No special security precautions are needed before installing, aside from the usual caution about enabling any skill to run autonomously on your agent (this skill is allowed to be invoked by the model by default, which is standard).

Complexity Analysis

• Always analyze time AND space complexity
• Big O: O(1) < O(log n) < O(n) < O(n log n) < O(n²) < O(2ⁿ)
• Amortized analysis for dynamic structures

Core Data Structures

Structure	Access	Search	Insert	Delete	Use When
Array	O(1)	O(n)	O(n)	O(n)	Random access, cache locality
Hash Map	O(1)	O(1)	O(1)	O(1)	Key-value lookup
Linked List	O(n)	O(n)	O(1)	O(1)	Frequent insert/delete
Binary Search Tree	O(log n)	O(log n)	O(log n)	O(log n)	Sorted data
Heap	O(1) top	O(n)	O(log n)	O(log n)	Priority queue
Graph	-	O(V+E)	O(1)	O(E)	Relationships, paths

Algorithm Patterns

• Two Pointers — sorted arrays, palindromes
• Sliding Window — subarray/substring problems
• Binary Search — sorted data, search space reduction
• DFS/BFS — tree/graph traversal
• Dynamic Programming — overlapping subproblems, optimal substructure
• Greedy — locally optimal choices lead to global optimum
• Divide & Conquer — merge sort, quick sort

Choosing the Right Structure

1. Need O(1) lookup? → Hash Map
2. Need sorted order? → BST / Sorted Array
3. Need LIFO? → Stack
4. Need FIFO? → Queue
5. Need priority? → Heap
6. Need relationships? → Graph
7. Need prefix search? → Trie