Arcadi4

CSAPP; "Computer Systems A Programmer's Perspective" by Randal E. Bryant and David R. O'Hallaron, covering computer architecture, systems programming, and performance optimization.

TaPL; the foundational computer science textbook "Types and Programming Languages" by Benjamin Pierce, covering type systems, programming language theory, lambda calculus, and more.

Use when approximating NP-hard optimization problems, proving approximation ratios, checking PTAS or FPTAS claims, or reviewing CLRS vertex cover, metric TSP, set cover, randomized MAX-3-CNF, LP rounding, subset-sum trimming, bin packing, scheduling, clique, matching, spanning-tree, or knapsack approximation arguments.

Use when working with CLRS, Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein, including algorithm design, data structures, asymptotic analysis, recurrences, proof style, or textbook-grounded algorithm explanations.

SICP; the classic computer science textbook "Structure and Interpretation of Computer Programs" by Harold Abelson and Gerald Jay Sussman.

Use when classifying decision problems as P, NP, NP-hard, NP-complete, or co-NP; proving polynomial-time reductions; reviewing SAT, 3-CNF-SAT, CLIQUE, VERTEX-COVER, HAM-CYCLE, TSP, SUBSET-SUM, encoding, or pseudo-polynomial complexity arguments.

Use when answering CLRS-style machine-learning algorithm questions about k-means clustering, Lloyd's procedure, multiplicative weights, weighted majority, online experts, gradient descent, projected gradient descent, convex optimization, linear regression, or regularization.

Use when solving exact pattern search, rolling-hash matching, finite-automaton matching, KMP prefix-function problems, suffix-array queries, LCP-array tasks, longest repeated/common substring problems, cyclic rotation tests, or Burrows-Wheeler transform exercises.

Use when removing dead code, renaming symbols, or refactoring — any change with no external consumers. Triggers on deprecating dead code, wrapper shims, compatibility aliases, TODO-future-cleanup, or refactors that patch structure instead of simplifying.

Use when configuring custom providers for OpenCode.

Use when writing new React components that involve state derived from props, event-driven side effects, or external subscriptions. Also use when auditing existing code for unnecessary useEffect calls, reviewing useEffect-heavy components, or refactoring to eliminate cascading renders. Applies to React 18+ codebases.

Use when about to make git commits, when tempted to bundle unrelated changes behind a single message, or when commit history needs to support git bisect, revert, and cherry-pick workflows. Also use when reviewing a branch before creating a PR. If you are writing a plan, specify that the executor must load this skill before start working.

Use when configuring or enhancing oxlint rules for a JavaScript/TypeScript project, when deciding which lint rules to enable beyond the default correctness category, or when evaluating whether a lint rule's autofix status and noise level make it worth enabling. Also use when bulk-enabling pedantic or style categories has produced excessive noise.

Use when a user provides examples prefixed by "for example", "such as", "e.g.", "like", or ends a list with "etc." / "and so on". For architecture/design tasks, abstract first to identify the concept, then enumerate. For research/clarification tasks, enumerate first — the concept organizes the output but the list is the deliverable.

Use when an agent violates a loaded skill despite it being present in context — a gap exists between what the skill says and what the agent did. Also use when reviewing skill effectiveness after a violation, or when deciding whether a skill needs enhancement vs the failure is tolerable.

Use when skill documents are too long, bloated with repetitive prose, or the user asks to compress/shorten/tighten/trim a SKILL.md. Also use when a skill has overlapping sections, duplicate explanations, or verbose examples that inflate word count without adding behavioral content.

Use when adding, updating, or reviewing GitHub Actions workflows that automatically bump Homebrew casks from upstream release metadata, especially GitHub release assets, checksums, scheduled runs, workflow_dispatch, or bot commits.

Use when creating, updating, reviewing, or troubleshooting Homebrew Cask definitions in a tap, especially when GitHub release assets, cask audit/style failures, local tap testing, or macOS-only dependencies are involved.