Results for “simplicity”

Review designs, products, and features with Steve Jobs' standards: ruthless simplicity, focus, and end-to-end excellence. Use when the user mentions "Steve Jobs review", "design review", "product review", "what would Steve do", "insanely great", "this feels too complicated", "too many features", "product taste", "saying no", or "is this good enough to ship". Also trigger when critiquing a UI, feature, or roadmap for focus and simplicity, cutting scope to the essential, or pressure-testing the whole experience from first run to daily use. Covers the simplicity audit, the no list, design-is-how-it-works, end-to-end ownership, demo culture, and a Jobs-style review protocol with binary verdicts. For visual design fundamentals, see refactoring-ui. For usability audits, see ux-heuristics. For detail polish, see microinteractions.

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Review a pull request, a proposed code change, for code that can be removed or simplified. Consider product decisions, final functionality, and whether rare edge cases have led to unnecessary complexity.

• Review a pull request for simplicity
• Find code that could be deleted
• Reconsider overcomplicated handling of rare cases

Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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Review code for bugs, security vulnerabilities, API misuse, consistency issues, simplicity problems, or test coverage gaps and low-value tests by running internal reviews and a peer review in parallel and returning combined findings. Single-concern with a type argument, or full review with no argument. Use when the user asks to "review my code", "full code review", "review my changes", "check for bugs", "scan for bugs", "review correctness", "security audit", "find vulnerabilities", "review security", "check API usage", "verify against docs", "check for cross-file duplication", "review consistency", "check for code reuse", "review simplicity", "find untested code", "find redundant tests", or "review test coverage".

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Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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Review changed code from four angles: reuse, simplicity, efficiency, and whether fixes address the underlying mechanism. Applies suitable improvements within the reviewed scope while preserving intended behavior, and reports fixes or skipped findings.

• Clean up code changes before committing
• Replace duplicated logic with existing helpers
• Reduce redundant computation and repeated I/O

Guide AI coding work toward simple implementations, focused edits, and explicit assumptions. Use it when writing, reviewing, or refactoring code to keep changes tied to the request and define success through tests or other concrete checks.

• Keeping bug fixes limited to the necessary code
• Simplifying code that has become overcomplicated
• Making implementation assumptions explicit

Review Qwen Code issues and pull requests through staged checks. Assess product fit, code simplicity, and CI evidence, then prepare English and Chinese comments that explain findings and flag decisions needing maintainer attention.

• Triage incoming Qwen Code issues
• Check pull requests against product direction
• Review changes for unnecessary complexity

Prepare a development branch for final maintainer review by checking security, unnecessary complexity, and billing abuse risks. Fix confirmed issues, run local checks and tests, then push changes and create or update the pull request.

• Preparing a feature branch for final review
• Checking security and billing abuse risks
• Simplifying unnecessarily complex changes

Turn a research proposal into an experiment plan tied to specific paper claims. Define comparisons, evaluation measures, tests that remove individual components, run order, and compute estimates, with a written plan and tracker for execution.

• Turning a research proposal into concrete experiments
• Designing comparisons and component-removal tests
• Prioritizing runs within a compute budget

Simplicity-focused design with ample whitespace, legible typography, and a limited color palette to reduce visual clutter.

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Manage software complexity through deep modules, information hiding, and strategic programming. Use when the user mentions "module design", "API too complex", "shallow class", "complexity budget", "strategic vs tactical", "deep module", "information leakage", "pass-through method", "this code is over-engineered", or "simplify this design". Also trigger when reviewing an interface for simplicity, evaluating whether an abstraction is pulling its weight, deciding whether a comment is worth writing, or choosing between general-purpose and special-purpose approaches. Covers deep vs shallow modules, red flags for complexity, and comments as design documentation. For code quality, see clean-code. For architecture boundaries, see clean-architecture.

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Review code for quality, simplicity, and common mistakes before declaring work complete.

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Run a structural-review debate between two lenses — lowy (volatility-based decomposition) and hickey (structural simplicity) — on the current diff. Each reviews independently, then they cross-examine every finding until they agree per-finding, and the agreed fixes are applied. Use when the user types `/lens-debate`, or asks to "have lowy and hickey review this", "run the lens debate", "debate this diff structurally", or "argue the structure of this PR until the lenses agree".

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Audits legal AI outputs and workflows for honest compression: unnecessary complexity, false simplicity, excessive caveats, hidden uncertainty and poor cost per legally acceptable output.

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Audits PR diffs for architecture boundary violations, design simplicity, dependency drift, and established-pattern mismatches. Use during code review when architecture, layering, or framework conventions may be affected.

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Pre-implementation gate covering think-first, simplicity, surgical edits, and verifiable goals. Use when starting implementation to verify the approach.

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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

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Applies the reasoning style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

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Review code for quality, simplicity, and common mistakes before declaring work complete.

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Review a Go design spec before implementation begins - dispatch a subagent that checks a design doc for completeness, consistency, and idiomatic Go (simplicity, small consumer-defined interfaces, explicit wrapped errors, context propagation) plus Cobra/Viper conventions where applicable. Use when a user has a Go spec to review, asks "is this spec ready?", or is about to implement from a written design. Distilled from spf13/go-skills and adapted to this repo's conventions.

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Go is the language of infrastructure. From Docker to Kubernetes to the entire cloud-native ecosystem, Go powers the systems that run the internet. It's not about what you can build - it's about what you won't break at 3 AM. This skill covers idiomatic Go patterns, error handling, concurrency with goroutines and channels, HTTP servers, microservice architecture, and the standard library that makes Go so powerful. Key insight: Go's simplicity is a feature. Fight the urge to abstract. Embrace boring, readable code. 2025 lesson: The teams succeeding with Go are the ones who resist overengineering. A main.go with 500 lines beats a "clean architecture" with 50 packages. Use when "golang, go service, go microservice, goroutine, channels, go http, go api, go backend, gin, fiber, chi router, go concurrency, go, golang, microservices, backend, concurrency, goroutines, channels, http, api" mentioned.

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