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.
Profile →npx skills add sickn33/agentic-awesome-skills --skill mathguard --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 exponent
- When to Use This Skill
- The Iron Law
- Non-negotiable rules
- The pre-proposal protocol
- Playbook — math technique → problem → win → caveat
- Sketches and probabilistic structures (massive data, approximate)
- Fast arithmetic / transforms (numeric and combinatorial)
- Dimensionality reduction and linear algebra
- Geometry (spatial queries)
- Graph and algebraic tricks
- Amortized and online algorithms
- Canonical example — counting distinct users
- Without the protocol — silent OOM, or worse, silent billing error
- With the protocol — auditable HLL
What does the mathguard skill do?
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.
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill mathguard --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.