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Reusable instructions for compatible AI agents.
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Lower Big-O on existing code via a one-transformation-at-a-time playbook with verify-revert-stop. For new code use lemmaly; for math-level wins escalate to 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.
Correctness-first: forces writing the function contract, loop invariant, termination argument, and edge cases BEFORE code. Catches Boyer-Moore, leftmost binary search, QuickSelect traps.
Algorithm-first discipline: state Big-O, data structure, and algorithm family BEFORE writing loops, queries, or recursion. Catches O(n^2), N+1, and brute-force defaults.
Cost-safety discipline for paid AI / inference APIs: treat $-cost as a third complexity dimension alongside time and space. Forces a written per-run $-cap, per-day $-cap, max-iterations bound, concurrency limit, and a matching provider-dashboard hard cap BEFORE any call site is written.
Agent skill for ML / AI Engineer, Backend Developer, Product Manager, published by morsechimwai. Project details and file access are provided by the publisher.
Agent skill for Backend Developer, Data Engineer, ML / AI Engineer, published by morsechimwai. Project details and file access are provided by the publisher.
Agent skill for Data Scientist, ML / AI Engineer, Backend Developer, published by morsechimwai. Project details and file access are provided by the publisher.
Agent skill for Backend Developer, Data Scientist, ML / AI Engineer, published by morsechimwai. Project details and file access are provided by the publisher.
Agent skill for Data Scientist, ML / AI Engineer, Data Analyst, published by morsechimwai. Project details and file access are provided by the publisher.
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