computer-science-algorithms

computer-science-algorithms is a Design patterns & component architecture skill for AI agents, published by pproenca in the dot-skills repository on GitHub. Skillspace rates computer-science-algorithms C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy algorithms, string/sequence algorithms, and the at-scale toolbox (Bloom filters, HyperLogLog, Count-Min Sketch, reservoir sampling, consistent hashing, external merge sort, Aho-Corasick, MinHash/LSH). Trigger on tasks involving "what's the right algorithm for…", performance-critical code, code with nested loops over the same input, recursive solutions, shortest-path / scheduling / matching / DP problems, code review for accidental O(n²) blowup, and any "how do I do X at scale / on a stream / without enough RAM" question — even if the user doesn't explicitly mention "algorithm" or "complexity."

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

180

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