「rag」の検索結果

XML sitemap audit — find and fix the sitemap problems that quietly waste crawl budget and slow indexing. Discovers the sitemap (robots.txt, /sitemap.xml, sitemap index), validates structure and size limits, and cross-checks the URLs it lists against reality: non-200 / redirected / noindex / canonicalized-away URLs that shouldn't be in a sitemap, plus indexable pages that are missing from it. Reviews lastmod accuracy, sitemap-index organization, and robots.txt reference. Use this skill whenever the user asks about sitemaps, sitemap errors in Search Console, "sitemap couldn't fetch / has errors", crawl budget, pages not getting indexed, or whether their sitemap is clean. Trigger on: "sitemap", "sitemap.xml", "XML sitemap", "sitemap errors", "sitemap audit", "couldn't fetch sitemap", "crawl budget", "pages not indexed sitemap", "sitemap index", "lastmod", "robots.txt sitemap", or any sitemap/crawl-coverage question. For a full-site SEO audit use /seo-analysis; for broken links use /broken-link-checker.

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。

Competitor page gap analysis — take a query you want to win and the pages currently outranking you, and produce a concrete brief on what to add or change to compete. Compares content depth and coverage (subtopics they cover that you don't), structure (headings, format, tables, media), search-intent match, E-E-A-T signals, schema/rich-result eligibility, internal linking, and on-page optimization — across your page vs. the top-ranking competitors. Use this skill whenever the user wants to know why a competitor outranks them on a specific query, what a top-ranking page does better, a content-gap analysis against specific competitor URLs, or a brief to beat a specific SERP. Trigger on: "why does competitor X outrank me", "content gap analysis", "competitor page analysis", "what does the top result have that I don't", "how do I beat this page", "analyze competitor pages", "SERP gap", "compete for [keyword]". For seed keyword discovery use /keyword-research; for a content calendar use /content-planner.

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。

Generate, export, load, and verify forensic evidence from GitHub sources. Use when creating verifiable evidence objects from GitHub API, GH Archive, Wayback Machine, local git repositories, or security vendor reports. Handles evidence storage, querying, and re-verification against original sources.

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。

Hypothesis-driven, tool-grounded security review of coverage gaps

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。

Check if specific lines were executed using gcov data

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, convert, or repair one Tutti workspace app as either a self-contained publishable package under package/ or a Chrome-style local debug app under .tutti/dev-app/. Use for mention://workspace-app-factory/create handoffs, mention://workspace-app-factory handoffs, standalone app generation, adapting existing repositories, Load unpacked repair flows for invalid local project directories, tutti.app.json and tutti.cli.json manifests, bootstrap.sh scripts, package-local AGENTS.md, local HTTP runtimes, TUTTI_APP_* host/port/storage rules, healthchecks, app assets, i18n, validation, and optional Tutti CLI integration.

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 this skill when the user mentions "pinme", or needs to upload files, store to IPFS, create/publish/deploy websites or full-stack services (including frontend pages, backend APIs, database storage, email sending, etc.), or any feature requiring backend database/server support.

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 PinMe Cloudflare Worker needs R2 object storage, including secure file or image upload, streaming download, metadata lookup, deletion, listing, Range requests, or R2+D1 coordination. Guides AI to use PinMe's automatically injected env.R2 binding without R2 credentials or manual Wrangler configuration.

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。

Domain routing and boundary guidance for authorized cloud security testing, including IAM misconfiguration, exposed storage, metadata services, and serverless injection. Use when a task belongs to the cloud testing domain and needs scope, evidence, pivot, or exit criteria.

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。

Domain routing and boundary guidance for authorized mobile application security testing, including insecure storage, certificate pinning bypass, exposed components, and binary reverse engineering. Use when a task belongs to the mobile testing domain and needs scope, evidence, pivot, or exit criteria.

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。

Domain routing and boundary guidance for authorized clickjacking testing, including missing X-Frame-Options, CSP frame-ancestors bypasses, and drag-and-drop hijacking. Use when a task belongs to the clickjacking domain and needs scope, evidence, pivot, or exit criteria.

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。

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

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 writing or revising scientific manuscript prose: IMRAD sections, abstracts, figure/table captions, citation-integrated paragraphs, reporting-guideline language, terminology, clarity, and journal-submission text.

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。

World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.

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。

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.

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-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

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。

Structured clarification workflow for underspecified requirements. Use before planning to resolve ambiguities through coverage-based questioning. Records answers in spec clarifications section.

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。

Generate structured test reports with pass/fail rollups, coverage summaries, and test artifacts. Use when the user is asking for test-result packaging or delivery, not for root-cause debugging or feature 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。

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

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。

Property-based testing with Hypothesis for discovering edge cases and validating invariants. Use when implementing comprehensive test coverage, testing complex logic with many inputs, or validating mathematical properties and invariants across input domains. Triggered by: hypothesis, property-based testing, @given, strategies, generative testing.

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。

Validate feature-level integration after all tasks are implemented. Checks cross-task consistency, full test suite, and overall spec coverage.

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 writing, reviewing, or refactoring SwiftUI code for iOS or macOS, including state and `@Observable` data flow, view composition, resizable layouts, safe areas, display scale, performance, lists, environment, localization, animation, Liquid Glass, and API migration. Also use for iPhone Duo, foldable, or large-display layouts (`NavigationSplitView` on large displays, iPhone tab sidebar, two-column reflow, foldable grids, `ArrangementView`, `ReservedRegion`), hinge effects, vertical bars, `@State` initialization or synthesized-property diagnostics, `@ContentBuilder` ambiguity, `reorderable` drag/drop, custom `AsyncImage` `URLSession`, swipe actions outside List, item-bound `alert`/`confirmationDialog`, `ToolbarOverflowMenu`, `AnimatableValues`, Document APIs (`Document`/`DocumentReader`), and Instruments `.trace` capture or analysis.

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。

Install Acontext, Login & Init Acontext Project, Add Skill Memory to Agent.

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 a contribution schedule and calculate a supplied price-path cost basis without predicting returns. Use when the user asks to dollar-cost averaging with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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。