「code quality」の検索結果

Run or maintain the full tdmcp repo quality-audit team: command sweeps, all package/Makefile/CI gates, security review, usability/flow review, refactor/test-gap analysis, coverage hardening, QA, and follow-up fix waves. Use whenever the user asks for a complete audit, improve repo/code quality, test all commands, find security/usability failures, refactor debt, add missing tests, re-run the audit, continue a previous quality wave, or verify the repo is ready.

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Code refactoring strategies, technical debt reduction, architecture modernization, and code quality improvement. Use when refactoring code, modernizing legacy code, reducing complexity, or improving maintainability.

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Perform code reviews following Sentry engineering practices. Use when reviewing pull requests, examining code changes, or providing feedback on code quality. Covers security, performance, testing, and design review.

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Stop AI agents from secretly bypassing your rules. Mechanical enforcement with git hooks, secret detection, deployment verification, and import registries. Born from real production incidents: server crashes, token leaks, code rewrites. Works with Claude Code, Clawdbot, Cursor. Install once, enforce forever.

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Implement test-driven development (TDD) workflow using the red-green-refactor cycle. Use when writing new features, fixing bugs, or refactoring existing code. Always write the failing test first, then implement minimal code to pass, then refactor. Essential for ensuring code reliability, preventing regressions, improving design through testability requirements, documenting expected behavior through tests, enabling confident refactoring, and maintaining high code quality standards throughout the development process.

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[61] VALIDATE. Comprehensive code quality check combining ESLint, TypeScript compilation, and unused code detection. Runs full lint suite with detailed error reporting and fix suggestions. Use before commits, after major changes, or when ensuring code quality standards.

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You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.

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Code-review quality gate and test-enhancement rubric. Use when ROSE needs sharper review output contracts, severity and risk prioritization, evidence anchors, negative review cases, fixture or golden-output drift guidance, or a Chinese review report profile; do not use as a duplicate code-review agent or final PASS authority.

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Generates comprehensive, context-aware code review checklists tailored to the specific codebase, programming language, and team standards. Analyzes PR diffs and suggests what reviewers should focus on.

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Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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言語モデル内部の反応を少数の特徴に分解し、学習した概念を調べます。学習済みSAEの分析、自作SAEの訓練、特徴を使った出力調整と品質評価を案内します。

• 学習済みSAEで特徴を調べたいとき
• 特定のモデル層にSAEを訓練する
• 入力文ごとの特徴の反応を比較する

Quality review of a change: is the logic right, is it safe, does it hold under real load, is risky code tested, is it fast enough, and is every line needed. Reads the connected code, not only the diff. Each finding is explained in plain English. Use for "review this", "code review", "review the last commit", "review my PR", "is this over-engineered", /ponytail-review.

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Add tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.

日本語の概要は準備中です。原文の説明を表示しています。

Autonomous AI code generation safety guardrail register: static AST analysis, forbidden import filters, and zero-day vulnerability checks.

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Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars. Supports multiple programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable analysis depth, categorization methods, and documentation formats to establish coding standards and maintain consistency across development teams.

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Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

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Create premium, Awwwards-quality website designs as React (.jsx) components that look like they were built by a top-tier agency charging $50k+ per project. Use this skill whenever the user asks for a website, landing page, portfolio, or web component that should look expensive, premium, luxury, editorial, high-end, or agency-quality. Also trigger when the user mentions Awwwards, FWA, award-winning design, or references brands like Apple, Aesop, Bottega Veneta, Stripe, or any luxury/fashion/design-forward brand. Trigger when the user says things like "make it look professional", "make it look expensive", "I want it to look really good", "high quality design", "not generic", or expresses dissatisfaction with typical AI-generated website aesthetics. Do NOT trigger for dashboards, admin panels, internal tools, or functional UI where aesthetics are secondary to utility — the standard frontend-design skill handles those.

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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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Review recent code changes for reuse, code quality, and efficiency, then directly apply straightforward cleanup improvements. Use when the user wants a post-implementation cleanup pass, pre-PR polish, or asks to simplify/refine recent changes. Invoke with `/simplify` or `/simplify <focus>`.

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Use when reviewing changed or staged code, a branch, or a PR in the Handsontable monorepo across architecture, code quality, performance, accessibility, and tests. Covers SOLID / Law of Demeter / plugin decoupling / breaking-changes policy, custom ESLint rules / JSDoc / naming / cognitive complexity, large-array and render-batching performance, WCAG 2.1 AA + keyboard navigation, and the tests a change ships (a named exercising test per new path, scoped mutation, a ticket on every weakened assertion). Trigger when asked to review changes, check a diff against Handsontable conventions, assess architectural correctness, spot performance regressions, verify accessibility, or judge whether a change's tests prove it, and as the design lens before or while implementing any core change.

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Evidence-first pull request judge that reviews a PR and posts one consolidated GitHub review with inline comments via the gh CLI. Runs the repo's own deterministic checks first, researches current official docs before any claim about external libraries or APIs, then reviews correctness, security, structural quality (code judo, spaghetti growth, file-size limits), and AI slop including useless code comments. Every finding must carry evidence, every comment passes a deterministic noise gate before posting, and the verdict (APPROVE, COMMENT, REQUEST_CHANGES) is weighed by findings. Use when asked to review a PR, judge this PR, review this branch or diff before merge, run the-judge, "revise esse PR", "faca o code review", or "julgue esse PR". Do NOT use for reviewing prose or documents, fixing CI failures, resolving merge conflicts, writing the fix itself, or responding to review comments (use gh-address-comments).

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Comprehensive code review assistant that analyzes code quality, security, and best practices

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Detect, suggest, and evaluate GoF design patterns in TypeScript/JavaScript codebases. Use when refactoring code, applying singleton/factory/observer/strategy patterns, reviewing pattern quality, or finding stack-native alternatives for React, Angular, NestJS, and Vue.

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Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.

日本語の概要は準備中です。原文の説明を表示しています。