「code quality」の検索結果

Use when forensically auditing a Claude Code skill/agent/command/workflow library against RULESET.md by a fixed mechanical protocol — probing every file against the mechanically-checkable rules and reporting CONFORMS / VIOLATES / UNVERIFIABLE / N-A counts with quoted evidence. It casts NO judgment and emits NO pass/fail verdict; counts are the headline. Triggers on "investigate the library", "audit against RULESET", "find every violation", "forensic library audit", "evidence-only check". Not for routing/quality/single-responsibility judgment — use skill-library-review. Not the full sharded sweep — use the audit-library command.

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。

Scientific research engine v6.0 NEXUS — adversarial review (Reviewer 2), 32 quality gates, tree search, serendipity tracking, confounder harness, cross-session learning. Use for ANY scientific analysis, hypothesis testing, data validation, literature review, or task where correctness > speed.

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。

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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。

Used by the Reviewer agent during Phase 9 (DesignVerify) and Phase 11 (Quality) to evaluate frontend code. Assesses visual hierarchy, spacing consistency, accessibility, and detects formulaic 'AI slop' design patterns, providing actionable feedback to the Developer.

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。

Weekly engineering retrospective from commit history, work patterns, and code-quality metrics, with trend tracking and per-person breakdown. NOT for per-session reflection (mk:memory / Phase 6).

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。

Systematically QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Proactively suggest when the user says a feature is ready for testing or asks "does this work?". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary.

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。

Weekly-cadence engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. Use when asked to "weekly retro", "what did we ship", or "engineering retrospective". Proactively suggest at the end of a work week or sprint. NOT for per-session or per-task reflection (see mk:memory / Phase 6 Reflect).

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。

Worker-Reviewer iterative pipeline for quality-critical code with review cycles

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。

Opt-in post-PR analysis and improvement suggestions for code quality enhancement

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。

Publication-quality statistical visualization with mandatory best practices. Use when creating plots, charts, figures, or any data visualization. Enforces accessibility, uncertainty display, proper labeling, and statistical accuracy.

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。

Core statistical methodology and pedagogical approach for all Inquiro analyses. Always loaded to maintain teaching quality and reproducibility standards.

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。

Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput vs IgG control rationale, rRNA / snoRNA contamination, fragment-length distribution, and ENCODE-compliance thresholds. Use when assessing whether a CLIP library passed, deciding lenient vs stringent peak thresholds, comparing replicates with IDR rescue and self-consistency ratios, or distinguishing failed IP from over-amplified library.

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 experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

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。

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

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。

Practical UI refactoring and visual design review inspired by Refactoring UI by Steve Schoger and Adam Wathan. Use when an AI coding agent needs to improve an existing interface, audit visual polish, redesign a rough UI, review frontend code for hierarchy/spacing/typography/color/depth/image issues, or turn a plain implementation into a cleaner product-quality screen. Do not use for creating UI from scratch, scaffolding a new app, building a new landing page, or designing a new design system unless the user explicitly asks to review or refactor an existing interface.

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。

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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。

Up-to-date Zig programming language patterns for version 0.17.0-dev (0.16.0 stable APIs plus the 0.17 deltas). Use when writing, reviewing, or debugging Zig code, working with build.zig and build.zig.zon files, or using comptime metaprogramming. Critical for avoiding outdated patterns from training data - especially std.net→std.Io.net (requires Io instance), std.time timestamps removed (use clock_gettime), std.Thread.Mutex/Condition/sleep removed (use pthreads), std.crypto.random removed, build system APIs (root_module, Compile methods→Module methods), I/O APIs (buffered writer pattern), container initialization (.empty/.init), allocator selection (DebugAllocator), ArrayList now unmanaged by default, @typeInfo lowercase fields (.@"struct" not .Struct), and removed language features (async/await, usingnamespace). Also covers 0.17.0-dev deltas: b.args→run_cmd.addPassthruArgs(), std.gpu→std.spirv, @bitCast logical-bit (endian-agnostic) semantics, build configurer/maker split, and the zig-pkg/ package directory. Also covers quality tooling Zig does not ship: coverage with kcov (and why in-file tests wreck the denominator), detecting unused private functions (neither the compiler nor zlint's unused-decls finds them), duplicate detection with jscpd, zlint (NOT the same-named X.509 linter in Homebrew), and the built-in fuzzer whose testOne now takes *std.testing.Smith rather than []const u8.

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。

Verify like Fable 5 — obsessive, systematic, evidence-based quality assurance woven into your reasoning. Use this skill when you have just written or modified code, need to confirm something works, are running tests, or need to validate output against requirements.

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。

Pre-commit review: security scan, quality gates, auto-fix.

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。

Apply frontend code quality rules for React and TypeScript components. Auto-activates when editing component files.

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。

Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context. Call with 'craft' to run the full shape-then-build flow, or 'teach' for design context setup.

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。

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

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。

Use when a diff builds more than the task needs — code that should not exist, a dependency the platform already covers, or a clever form where a flat one reads better. Deletion-hunting, not quality.

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。

Avalia LLMs em mais de 60 benchmarks acadêmicos (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use quando benchmarking de qualidade de modelo, comparação de modelos, relatório de resultados acadêmicos ou rastreamento de progresso de treinamento. Padrão da indústria usado por EleutherAI, HuggingFace e laboratórios principais. Suporta HuggingFace, vLLM, APIs.

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。