「quality」の検索結果

Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality.

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Use when reviewing, critiquing, or stress-testing an existing strategy document. Evaluates seven dimensions — diagnosis quality, guiding policy strength, action coherence, assumption exposure, falsifiability — with optional 7S, Five Forces, Balanced Scorecard, and Hoshin Kanri lenses. Triggers on: review my strategy, poke holes in this plan, what's weak here, strategy audit, red team this. Does NOT build strategy (use strategy-interview) or brainstorm project ideas (use brainstorm-beagle).

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Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.

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Use when comparing two or more code implementations against a spec or requirements doc. Triggers on "which repo is better", "compare these implementations", "evaluate both solutions", "rank these codebases", or "judge which approach wins". Also covers choosing between competing PRs or vendor submissions solving the same problem. Does NOT review a single codebase for quality — use code review skills instead. Does NOT evaluate strategy docs — use strategy-review. Requires a spec file and 2+ repo paths.

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。

Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.

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.

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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。

Register, activate, and test a custom detekt rule in :quality:detekt-rules. Use when adding a DI/privacy/suppression guardrail rule or debugging a false positive.

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 clean Conventional Commits: inspect the diff, group related changes, run quality checks, and write type(scope) messages. Use when committing work, or when the user runs /smart-commit or asks to commit changes.

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Code review knowledge base: quality, security (OWASP Top 10), error-handling, performance, and test-coverage checklists with severity-ranked output format. Use when reviewing code changes, PRs, or before commits. Loaded automatically by the code-reviewer agent.

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Audit project health across 7 dimensions (documentation, test coverage, security, code quality, dependencies, database, build/deploy) producing a scored report with prioritized fixes. Use for periodic checkups, before releases, or when the user runs /project-health-check.

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。

Investigate performance bottlenecks and validate optimizations without weakening retrieval quality or correctness.

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Pre-commit implementation analysis for Retail-Agentic-Commerce. Use before commits, after significant changes, or when validating Apps SDK tools, mock data, MCP communication, widget isolation, or implementation quality.

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Discoverability shim for the Retail-Agentic-Commerce pre-commit analysis Codex skill. Use before commits, after significant changes, or for implementation-quality 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。

Create high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.

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Investment portfolio analyzer. Audits current allocation vs target, asset class diversification (stocks, bonds, real estate, alternatives), expense ratios, tax efficiency, rebalancing needs, factor tilts (value, momentum, quality), three-fund portfolio fit, target-date fund analysis, and Boglehead-style optimization. Produces FINANCE-PORTFOLIO.md with a portfolio score, specific rebalancing trades, and fund swap recommendations.

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Catalog of common CI/CD patterns (build, test, deploy, security, quality), scan scope resolution (org-wide or specific repos), the reusable workflow template, selection criteria, and usage-documentation template. Load when detecting recurring CI/CD patterns across GitHub orgs and/or repositories to generate standardized `reusable-*.yml` workflows with corresponding `docs/<name>-usage.md` files.

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Validates documentation completeness for prior authorization requests by checking a 10-item checklist covering patient information, provider credentials, insurance details, medical codes, clinical notes quality, NCCI bundling risk, and service type classification.

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 existing API documentation for completeness, accuracy, and developer experience. Use when asked to "audit our API docs", "are our docs accurate", or "review documentation 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。

Use when asked to audit UI for visual quality, check design consistency, review brand alignment, evaluate design system compliance, or find visual issues before a launch. Examples: "audit our UI", "check visual consistency", "review the design for issues", "is our UI on-brand", "find visual bugs", "design QA before launch".

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。

Theory-backed design audit — names the principle violated, cites the source, shows the fix. Use when asked to "evaluate design quality", "check if this follows design principles", "theory check", "design exam", "audit against best practices", or "what's wrong with this design".

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。

Design a data cleaning and transformation pipeline — missing values, outliers, and deduplication. Use when asked to "clean this dataset", "handle missing values", or "deduplicate this data".

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Design support metrics dashboard -- CSAT, FRT, TTR, ticket deflection rate, volume trends, and agent efficiency. Use when asked to "what metrics should support track", "build our support dashboard", "measure support quality", or "audit our support performance".

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 broad SEO audit across technical SEO, on-page SEO, schema, sitemaps, content quality, AI search readiness, and GEO / Audit SEO menyeluruh (Technical, On-Page, Schema, Sitemap, E-E-A-T, GEO)

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。