Applies [Author]'s "[Book Title]" framework ([2-4 core concept names]) to [what kind of task]. Use whenever user wants a "[book/approach name]" approach, mentions [core term 1], [core term 2], or [core term 3] — even if they don't name the book. Also use when [implicit trigger scenario].
Applies the framework from Stuart Russell & Peter Norvig's "Artificial Intelligence: A Modern Approach" (AIMA) — the rational-agent model (PEAS, environment properties), search (uninformed, informed/heuristic A, adversarial minimax/alpha-beta), constraint satisfaction, knowledge representation and logical inference…
Applies Al Sweigart's "Automate the Boring Stuff with Python" framework — spotting repetitive/rule-based manual tasks worth scripting, picking the right tool (regex, file/folder ops via os/shutil/pathlib, CSV/Excel/PDF/Word/JSON handling, web scraping with requests/BeautifulSoup or Selenium, scheduling, email/SMS, GUI…
Applies Kernighan & Ritchie's "The C Programming Language" (2nd Edition, ANSI C) framework — pointer/array duality and pointer arithmetic, reading declarations by the right-left rule, manual memory management and ownership, undefined behavior, the preprocessor and separate compilation, storage classes/linkage, and C's…
Applies Ben Horowitz's "The Hard Thing About Hard Things" framework (The Struggle, wartime vs. peacetime CEO, no-easy-answers decision making, the Law of Crappy People, hiring for strength vs. lack of weakness, taking care of people/products/profits in order, managing your own psychology, firing/demoting fast but…
Applies Eric Ries's "The Lean Startup" framework (Build-Measure-Learn loop, validated learning, MVP, innovation accounting, pivot vs. persevere, actionable vs. vanity metrics, five whys, small batches, split testing) to plan or evaluate a feature, product, or business idea. Use this skill whenever the user wants a…
Applies Tom M. Mitchell's "Machine Learning" (1997) framework — the well-posed learning problem (Task/Performance/Experience), hypothesis space and inductive bias, overfitting and the bias-variance tradeoff, proper train/test/cross-validation evaluation with statistical significance, the Bayesian learning perspective…
Applies Andrew Hunt & David Thomas's "The Pragmatic Programmer" (20th Anniversary / 2nd Edition, 2019) framework — DRY, orthogonality, reversibility, tracer bullets, broken windows, good-enough software, Design by Contract, decoupling and the Law of Demeter, programming by coincidence, refactoring, property-based…
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originalMIT
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