Agent skill · research science · sickn33
mathguard
Math-heavy escalation for n >= 10^6 — Bloom, HyperLogLog, Count-Min, MinHash/LSH, FFT, JL projection, sweep line. Use when classical O(n log n) is the floor and approximate or math wins.
Why this skill is useful
Provides domain-specific knowledge on advanced mathematical algorithms and probabilistic data structures that the AI wouldn't reliably generate on its own.
What it needs
About 12k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
mathguard — Math-Heavy Optimization for AI Code lemmaly makes you pick the right classical algorithm. mathguard kicks in when the classical algorithm is already optimal but mathematics gives a better bound — usually by accepting bounded approximation, exploiting structure, or moving to a smarter algebraic space. The model knows these techniques. It almost never proposes them spontaneously. mathguard fixes that. Violating the letter of these rules is violating the spirit of the skill. A Bloom filter where the caller assumed exact answers is a production incident, not an optimization. When to Use This Skill Use mathguard when: Working with large-scale data (n ≥ 10⁶): similarity search, deduplication, top-K / heavy-hitters, streaming analytics, cardinality estimation, embeddings, recommender systems. Doing signal/image processing, polynomial or big-integer arithmetic, convolution, graph distance, computational geometry, randomized algorithms. The classical O(n log n) is already the floor and you need an asymptotic win (Bloom filter, HyperLogLog, Count-Min Sketch, MinHash/LSH, FFT/NTT, Johnson-Lindenstrauss projection, sweep line, kd-tree/BVH, fast exponentiation, monoid parallel reduction, amortized potential method). Loaded after lemmaly has confirmed the classical answer is not enough. Do not use mathguard when: The caller needs exact answers (auth, billing, dedup-for-correctness, primary keys). n is small (n < 10⁴) and the path is not hot. The bottleneck is I/O, not CPU/memory. The Iron Law Probabilistic data structures (Bloom, HyperLogLog, Count-Min, MinHash/LSH, t-digest), randomized projections (JL), and lossy transforms (floating FFT) all change the answer's meaning. Before proposing one: 1. Write the error parameter the caller will see (false-positive rate, relative error, distortion bound). 2. Identify the caller and state, in one sentence, that they tolerate this kind of wrong answer. 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/mathguard--05d808