Eliminate sycophantic agreement patterns in AI responses. Load via /skill anti-sycophancy.
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。
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概要と使いどころ
Eliminate sycophantic agreement patterns in AI responses. Load via /skill anti-sycophancy.
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。
Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.
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。
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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。
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning
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 verified service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
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。
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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 the user asks to "plan my site structure", "design the page hierarchy / navigation / URL taxonomy", "fix internal linking", or "find orphan pages"; runs two modes — architecture (hierarchy, nav, URL patterns, hub/spoke clusters, Mermaid site maps) and linking (link graph, authority flow, anchor text, orphan disposition, source/target/anchor plan) — and outputs a structure score /100 plus a handoff summary. Not for external backlinks — use offsite-signal-analyzer; not for XML sitemap or indexation issues — use technical-seo-checker. 网站架构/信息架构/站点地图/内链优化
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Use when the user asks to "optimize my Shopping feed", "fix product disapprovals", "improve product titles/attributes", or "build feed-driven PMax asset groups"; audits and rewrites the Shopping/Performance Max product feed — title/description patterns, required and recommended attributes, GTIN/availability/price hygiene, disapproval triage, and feed-driven asset-group / listing-group structure — informing the ROAS O (Offer) dimension. Not for text ad copy — use ad-creative-builder; not for scoring the account or the RQS — use ad-account-auditor. 商品Feed优化/购物广告Feed/商品标题优化/商品禁投修复
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 the user asks to "map the category narrative", "tear down how competitors tell their story", or "find the language conventions in our market"; produces a category narrative map — the dominant stories and points of view in the category, its language conventions and framing clichés, and a per-competitor narrative teardown (arc, claimed onlyness, proof pattern) plus how each rival's messaging has shifted over time (scraped copy vs archived copy). Not for the positioning canvas itself — use positioning-mapper; not for the beachhead's beliefs and objections — use audience-belief-mapper; not for SERP keyword targeting — use keyword-research; not for claim adjudication — use offer-claims-registry. 品类叙事/竞争叙事拆解/语言惯例/叙事演变
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。
You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C
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Search logs and codebases for error patterns, stack traces, and anomalies. Correlates errors across systems and identifies root causes.
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Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
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Build resilient applications with robust error handling strategies that gracefully handle failures and provide excellent debugging experiences.
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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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Guide for diagnosing and improving MSBuild project evaluation performance. Only activate in MSBuild/.NET build context. USE FOR: builds slow before any compilation starts, high evaluation time in binlog analysis, expensive glob patterns walking large directories (node_modules, .git, bin/obj), deep import chains (>20 levels), preprocessed output >10K lines indicating heavy evaluation, property functions with file I/O ($([System.IO.File]::ReadAllText(...))), multiple evaluations per project. Covers the 5 MSBuild evaluation phases, glob optimization via DefaultItemExcludes, import chain analysis with /pp preprocessing. DO NOT USE FOR: compilation-time slowness (use build-perf-diagnostics), incremental build issues (use incremental-build), non-MSBuild build systems. INVOKES: binlog MCP server tools (evaluations, evaluation_global_properties, evaluation_properties, imports, properties); falls back to dotnet msbuild -pp:full.xml for preprocessing, /clp:PerformanceSummary.
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。
Expert in Kubernetes and DevOps with infrastructure-as-code and cloud-native patterns
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。
Serverless and microservices development guidelines covering FastAPI, cloud-native patterns, API gateways, and best practices for scalable serverless architectures.
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。
Machine learning development with JAX, functional programming patterns, and high-performance computing.
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。
Build tools that agents can use effectively, including architectural reduction patterns. Use when creating new tools for agent systems, debugging tool-related failures or misuse, or optimizing existing tool sets for better agent performance.
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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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To create new CLI skills following Anthropic's official best practices with zero manual configuration. This skill automates brainstorming, template application, validation, and installation processes while maintaining progressive disclosure patterns and writing style 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。
Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.
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。
12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.
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 guide for creating and managing skills in Claude Code with auto-activation system, following Anthropic's official best practices including the 500-line rule and progressive disclosure pattern.
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。