clrs-algorithms
Skill17 starsChanged 9 months agoMITView on GitHub
Data structures and algorithms reference based on CLRS. Use this skill when implementing, discussing, or choosing data structures or algorithms. Auto-activates for algorithm selection, complexity analysis, and performance optimization. Comprehensive coverage of fundamental and advanced data structures with pseudocode examples.
In this repository7 other files it gives its agents.
- clean-codeSkill
- dddSkill
- ddiaSkill
- gof-patternsSkill
- pragmatic-programmerSkill
- sicpSkill
- software-patternsSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
Ask about or need to implement a data structure
- Distinctive: line 97
Choosing a structure: Start with the decision guides
- Distinctive: line 98
Learning a structure: Read the full documentation with examples
- Distinctive: line 99
Quick reminder: Use the tables above for at-a-glance reference
- Distinctive: line 100
Implementation: Follow the pseudocode, adapt to your language
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
CLRS Data Structures & Algorithms Reference
A comprehensive reference for data structures and algorithms based on "Introduction to Algorithms" (CLRS). This skill provides language-agnostic guidance with pseudocode examples that can be translated to any programming language.
When This Skill Activates
This skill automatically activates when you:
- Ask about or need to implement a data structure
- Need to choose between data structures for a problem
- Discuss time/space complexity trade-offs
- Need algorithm implementations (sorting, searching, graph algorithms)
- Mention specific structures: B-tree, heap, hash table, graph, etc.
Quick Data Structure Reference
Linear Structures
| Structure | Access | Search | Insert | Delete | Use When |
| Array | O(1) | O(n) | O(n) | O(n) | Known size, index access |
| Dynamic Array | O(1) | O(n) | O(1)* | O(n) | Unknown size, frequent append |
| Linked List | O(n) | O(n) | O(1) | O(1) | Frequent insert/delete |
| Stack | O(1) | O(n) | O(1) | O(1) | LIFO needed |
| Queue | O(1) | O(n) | O(1) | O(1) | FIFO needed |
| Deque | O(1) | O(n) | O(1) | O(1) | Both ends access |
Trees
| Structure | Search | Insert | Delete | Use When |
| Binary Search Tree | O(log n)* | O(log n)* | O(log n)* | Ordered data, frequent search |
| AVL Tree | O(log n) | O(log n) | O(log n) | Guaranteed balance needed |
| Red-Black Tree | O(log n) | O(log n) | O(log n) | Frequent inserts/deletes |
| B-Tree | O(log n) | O(log n) | O(log n) | Disk-based storage |
| Trie | O(m) | O(m) | O(m) | String/prefix operations |
| Heap | O(1)/O(n) | O(log n) | O(log n) | Priority queue needed |
| Splay Tree | O(log n)* | O(log n)* | O(log n)* | Self-adjusting, temporal locality |
| Treap | O(log n)* | O(log n)* | O(log n)* | Randomized balance, split/merge |
| Interval Tree | O(log n) | O(log n) | O(log n) | Interval overlap queries |
| Order-Statistic Tree | O(log n) | O(log n) | O(log n) | Rank/select queries |
| K-D Tree | O(log n)* | O(log n)* | O(log n)* | Multi-dimensional spatial data |
Hash-Based
| Structure | Search | Insert | Delete | Use When |
| Hash Table | O(1)* | O(1)* | O(1)* | Fast key-value lookup |
| Hash Set | O(1)* | O(1)* | O(1)* | Unique membership testing |
| Bloom Filter | O(k) | O(k) | N/A | Probabilistic membership |
Graphs
| Structure | Space | Add Edge | Query Edge | Use When |
| Adjacency List | O(V+E) | O(1) | O(degree) | Sparse graphs |
| Adjacency Matrix | O(V²) | O(1) | O(1) | Dense graphs |
Graph Algorithms
| Algorithm | Time | Use When |
| Network Flow | O(VE²) | Max flow, bipartite matching, min cut |
| Strongly Connected Components | O(V+E) | Find SCCs, 2-SAT, dependency analysis |
Strings
| Structure | Build | Search | Use When |
| Suffix Array | O(n log n) | O(m log n) | Space-efficient string matching |
| Suffix Tree | O(n) | O(m) | Fast pattern matching, LCS |
| String Algorithms | O(m) | O(n) | KMP, Rabin-Karp, Boyer-Moore, Aho-Corasick |
Advanced
| Structure | Use When |
| Skip List | Probabilistic balanced list |
| Disjoint Set | Union-find operations |
| Segment Tree | Range queries with updates |
| Fenwick Tree | Prefix sums with updates |
| Fibonacci Heap | Dijkstra, Prim with O(1) decrease-key |
| Binomial Heap | Mergeable priority queue |
| van Emde Boas Tree | Integer keys with O(log log u) operations |
Algorithms
| Algorithm | Use When |
| Sorting Algorithms | QuickSort, MergeSort, HeapSort, RadixSort, and more |
* = amortized or average case
Decision Guides
- Which Data Structure Should I Use? - Decision guide by use case
- Complexity Cheat Sheet - Quick reference for Big-O
How to Use This Reference
- Choosing a structure: Start with the decision guides
- Learning a structure: Read the full documentation with examples
- Quick reminder: Use the tables above for at-a-glance reference
- Implementation: Follow the pseudocode, adapt to your language
Language Translation Notes
The pseudocode in this reference uses these conventions:
classfor type definitionsfunctionfor methods/functions->for method calls on objects//for comments- Type hints shown as
name: Type
Translate to your language:
- PHP:
class,function,->,//, type hints in docblocks or PHP 8+ - JavaScript/TypeScript:
class,function/arrow,.,//, TS types - Python:
class,def,.,#, type hints - Java/C#: Direct mapping with
new, generics
Based on concepts from "Introduction to Algorithms" by Cormen, Leiserson, Rivest, and Stein (CLRS), MIT Press.
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