91건 · 관련도순
Review code changes for correctness, readable names, clear interfaces, and design problems. Examine duplication, hidden assumptions, and unnecessary abstractions, then report actionable findings with file locations, explanations, and concrete suggestions for fixes.
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Review and simplify the ego-lite codebase by finding unused code, duplicated state, and unnecessary layers. The skill checks real callers and API requirements, explains behavior changes, and coordinates implementation, tests, and documentation updates.
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Guides reinforcement learning, where models learn from rewards, using torchforge and PyTorch. Covers reward design, custom loss functions, and distributed training so researchers can experiment with algorithms while keeping infrastructure concerns separate.
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Migrate .NET tests from xUnit.net v2 to v3 while preserving the existing test runner. Update packages, project settings, and affected test code, then verify test discovery, execution, and skip behavior.
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Scan C# code for dependencies on clocks, files, environment settings, and other resources that make tests difficult. Get source locations, consistent counts, and suggested abstractions, interfaces or providers that let tests control those dependencies.
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Share shopping carts, themes, and other state across Blazor components that are not directly connected. Choose cascading values or shared services, notify components when data changes, and account for rendering boundaries and service lifetimes.
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Implement data loading and API submissions in Blazor, including loading indicators, error handling, and cancellation when navigation changes. Configure HttpClient and organize data services for components that run on the server or in the browser.
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Review concrete code changes for defects, compatibility problems, repository-rule violations, and unnecessary code. Produce evidence-based findings and, when explicitly requested, make minimal cleanup changes while checking that existing behavior is preserved.
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Review code changes with five parallel agents before committing. Each checks a separate area: code quality, simplicity, consistency, robustness, or task scope. Results identify issues, and every check must pass before the changes are considered ready.
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Apply when refactoring, evaluating diff size, or tempted to add abstractions, layers, or signal threading. Bias toward deletion and the smallest change that solves the problem.
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Apply John Ousterhout-inspired software design rules when reducing complexity, designing module boundaries, or reviewing APIs and abstractions.
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Remove AI-generated code slop, unnecessary comments, and over-engineering from the current branch diff. Cleans up boilerplate, simplifies abstractions, strips defensive code, and in skill-file mode lints SKILL.md files for quality. Use when cleaning up code, simplifying, removing boilerplate, before committing, or when reviewing a skill before promoting it.
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Apply when refactoring, evaluating diff size, or tempted to add abstractions, layers, or signal threading. Bias toward deletion and the smallest change that solves the problem.
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Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.
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Refactor cleanly instead of layering sediment. Use for rename requests, stale or misleading names, legacy leftovers, changed scope, or when a change reveals duplicated concepts, local adapters, obsolete owners, redundant inputs or guards, compatibility wrappers, parallel abstractions, an over-large module that has accreted many responsibilities, or "just tack this on" pressure in any code area.
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Critical Akka.NET best practices including EventStream vs DistributedPubSub, supervision strategies, error handling, Props vs DependencyResolver, work distribution patterns, and cluster/local mode abstractions for testability.
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Build declarative 3D scenes with React Three Fiber (R3F) - a React renderer for Three.js. Use when building interactive 3D experiences in React applications with component-based architecture, state management, and reusable abstractions. Ideal for product configurators, portfolios, games, data visualization, and immersive web experiences.
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Evaluate architecture and module boundaries for volatility-based decomposition using Juval Lowy's framework (from "Righting Software", building on Parnas 1972). Use when reviewing module splits, service boundaries, new abstractions, or any decomposition decision. Trigger on phrases like "where should this boundary be", "how to split this", "module boundaries", "encapsulate change", "volatility", or references to Lowy, Parnas, or "Righting Software". Complements /hickey (interleaved concerns) with a different lens (change encapsulation).
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Writes idiomatic Rust code with memory safety, zero-cost abstractions, ownership patterns, lifetime management, trait hierarchies, and async applications with tokio.
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One-shot efficiency audit of a file, diff, or whole repo across BOTH axes at once: over-engineered code (reinvented stdlib, needless abstractions, speculative config) AND bloated prose (verbose comments, padded docstrings, redundant doc sections). Neither a pure code-minimizer nor a pure prose compressor does both in one pass. That's the point. Ranked report, biggest saving first; changes nothing. Use when the user says "chisle audit", "/chisle-audit", "audit this for bloat", "what can I cut", "review this PR for over-engineering and verbosity".
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Review a diff or file through the Chisle lens: flag over-engineering, speculative abstractions, reinvented stdlib, and verbose code that a lazier approach would shrink. One finding per line, no praise. Use when the user says "chisle review", "review this for bloat", or invokes /chisle-review.
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Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".
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Code simplification suggestions — clarity, unnecessary complexity, redundant abstractions. Advisory only.
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Identify dangerous API footguns, surprising default behaviors, and sharp edges in codebases and dependencies. Adapted from Trail of Bits. Use during code review to catch APIs that are easy to misuse, configurations that surprise, and abstractions that leak.
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