「code quality」の検索結果

Code quality review — patterns, security, performance, correctness. Finds bugs, suggests improvements, triggers fix for issues found. Escalates to opus for security-critical code.

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Feature implementation orchestrator. ALWAYS use this skill for ANY code change — implement, build, add feature, create, fix bug, or any task that modifies source code. This is the default route for 70% of all requests. Runs full TDD cycle: understand → plan → test → implement → quality → verify → commit.

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。

Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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。

Audit feature engineering code for leakage, quality issues, and pipeline correctness. Use when asked to "audit our feature pipeline", "is there data leakage", or "find feature quality issues".

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。

Runs deterministic Python quality checks against a path or scope — formatting, linting, type checking, and typing-boundary policy. Use when checking or fixing code quality via prek, ruff, ty, pytest, or the check-typing-boundaries policy script. Reports results grouped by category; fixes only when explicitly requested.

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Use when the user asks for a read-only, actionable audit of Python code quality, modernization, maintenance burden, ecosystem opportunities, or Pythonic design across a PR, git diff, staged/unstaged changes, files, or directories.

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。

Review a code change for correctness-maintainability defects not owned by a narrower security/performance/accessibility perspective. Use for an independent quality perspective on a defined change set.

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 user asks to review code for quality and security issues.

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。

Multi-agent task orchestration for Claude Code. When given multiple tasks to execute, use this skill to spawn parallel worker agents through Sibyl's MetaOrchestrator instead of Claude Code's built-in Task tool. This enables true multi-process execution with budget controls, quality gates, and coordination.

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。

Review touched Incan source for Python-quality readability, idiomatic Incan structure, dogfooding integrity, public API polish, and source-level anti-patterns. Use for `.incn` stdlib, examples, language-surface implementations, or when the user asks whether Incan code reads like well-written Python rather than Rust-shaped scaffolding.

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 only for reviewing completed Biome PRs, branches, commit ranges, diffs, or working trees against business logic and requirements. Excludes broad code-quality and process audits, triage, reproduction, and implementation.

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。

TypeScript best practices: strict mode, ESLint rules, file naming, imports, type safety, Zod validation, error handling, and testing with Vitest. Use when writing TypeScript code, reviewing for code quality, fixing lint errors, creating types/interfaces, writing tests, or adding validation.

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 skill isn't triggering as expected, before adding a new skill (to check for a name collision), during periodic cleanup of an accumulated skills directory, or when asked to review skill quality — checks frontmatter validity, description quality against Skill Discovery Optimization conventions, body size, and cross-scope name collisions.

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。

Generates render and output configuration for Remotion videos. Focuses ONLY on output settings, codec, format, quality, resolution. Input is target platform/quality requirements. Output is RENDER_CONFIG.md with Remotion render settings.

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。

Map testable surfaces, audit existing tests for quality, and write test specifications that prevent AI shortcuts. Use when asked to "audit tests", "specify tests", "test architect", "map test coverage", or invoke /test-architect. Two modes — audit (evaluate existing tests against Beck's principles) and specify (write test specs for new code). Powered by Carmack × Beck quality-testing.md reference doc. Stack: Next.js App Router / TypeScript / Vitest / Cypress / tRPC / Prisma / Neon.

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 MCAF non-functional-requirements guidance to capture or refine explicit quality attributes such as accessibility, reliability, scalability, maintainability, performance, and compliance. Use when a feature or architecture change needs explicit quality attributes and trade-offs.

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Use when benchmarking, profiling, or A/B-testing CUDA kernels or end-to-end perf in the imp inference engine on RTX 5090 (sm_120), including refreshing tests/perf_baseline.json or publishing numbers to docs/BENCHMARKS.md and the README. Triggers on "benchmark kernel", "profile cuda", "ncu", "nsys", "kernel timing", "kernel sum", "occupancy", "bandwidth bound", "compute bound", "roofline", "perf baseline", "is this regression real", "decode dropped", "aggregate throughput", "two-image A/B", "prefill kernel A/B". Do NOT use for writing/optimizing kernel code (sm120-cuda-expert) or output-quality checks (check-degeneration).

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。

Run CodeQL and Semgrep static analysis with SARIF output for vulnerability detection, code quality assessment, and security compliance scanning across multiple languages.

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。

Guide creating and reviewing skills that extend Claude with specialized knowledge, workflows, or tool integrations. Covers SKILL.md structure, frontmatter quality, trigger design, eval pipelines, and packaging. Triggers on create a skill, review this skill, new skill, optimize skill, skill 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。

Evaluates whether work-in-design is ready to hand off from a chat design conversation to autonomous execution (Claude Code, n8n, agent runtime). Runs a 7-condition gate: spec stability, verification surface, invariants, pump-primer, work decomposition, rollback cost, token budget headroom. Three invocation modes — deliberate (caller provides work_context, returns JSON verdict), proactive sensing (Claude detects handoff signals in design conversation and offers the check), conversational walkthrough (Claude walks the 7 conditions as discussion topics). Returns structured JSON in all modes. Use when designing a Claude Code deployment, scoping an autonomous run, or deciding next move. Trigger on: "ready to hand off," "ready for execution," "is this CC-ready," "handoff check," "walk me through readiness," "are we ready to ship this," "should I just hand this off," or proactive detection. Do NOT use for evaluating completed work, runtime selection, or prompt quality. Profile-agnostic.

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。

Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.

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。

App Store / Google Play 用のプロモーションスクリーンショットを Pencil (.pen) で生成する Agent Team スキル。creative-director が戦略を立て、screenshot-designer が Pencil で デザインを構築し、copy-writer がコピーを提供し、spec-validator が技術仕様を数値検証し、 quality-reviewer が最終品質を確認する。5エージェント体制で高品質なスクリーンショットを生成。 Use when: App Store スクリーンショットを作りたい、プロモーション画像を作成したい、 スクショのデザインをしたい。 Triggers: "スクリーンショット作成", "App Store 画像", "プロモーションスクショ", "screenshot creator", "スクショ作って", "App Store 素材"

Convert SVG files to high-quality PNG images with precise control over dimensions, background color, and CSS rendering (including mix-blend-mode, filters, gradients). Use this skill when the user wants to: (1) convert SVG to PNG, (2) generate app icons or splash screens from SVG source, (3) batch-generate multiple PNG sizes from a single SVG, (4) render SVG with advanced CSS features like mix-blend-mode or filters to PNG. Triggers: "SVG to PNG", "SVG変換", "PNG変換", "アイコン生成", "スプラッシュ生成", "app icon from SVG", "favicon generation", "splash screen generation".

Chrome で表示中の Web 画面を Pencil (.pen) ファイルに高精度で再現する Agent Team スキル。 3つの専門エージェント（screen-analyzer, design-builder, quality-reviewer）が 分析→構築→品質検証のサイクルを回し、忠実な画面再現を実現する。 Use when: Pencil で再現して、画面を Pencil に写して、Chrome を Pencil にコピー、 pencil-replicate、デザインをキャプチャ、UI を Pencil に