「code analysis」の検索結果

Generic PowerPoint authoring skill for A-share investment analysis and pitch decks. Creates professional 路演PPT / 投资分析PPT for any A-share company using live data from iFind MCP (Tier-1) and AkShare MCP (Tier-2 fallback) tools. All company-specific values are parameterized — never hardcoded. Triggers on "A股PPT制作", "投资PPT", "制作PPT", "路演PPT", "pitch deck", "PowerPoint [company/ticker]", or any request to create slides for a Chinese stock. When invoked, use the `generate_a_share_ppt` script with --company and --ticker args.

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。

Strategic SEO planning for new or existing websites. Industry-specific templates, competitive analysis, content strategy, and implementation roadmap. Use when user says "SEO plan", "SEO strategy", "SEO planning", "SEO best practices for <topic>", "content strategy", "keyword strategy", "content calendar", "site architecture", "SEO roadmap", or asks for organic growth guidance without a specific URL.

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。

Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", or "data-driven SEO".

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。

Search Experience Optimization: reads Google SERPs backwards to detect page-type mismatches, derives user stories from search intent signals, and scores pages from multiple persona perspectives. Identifies why well-optimized pages fail to rank by analyzing what Google rewards for each keyword. Use when user says "SXO", "search experience", "page type mismatch", "SERP analysis", "user story", "persona scoring", "why isn't my page ranking", "intent mismatch", or "wireframe".

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。

Image optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, CLS prevention, image SERP rankings (via DataForSEO), and image file optimization (WebP/AVIF conversion, IPTC/XMP metadata injection). Use when user says "image optimization", "alt text", "image SEO", "image size", "image audit", "optimize images", "image metadata", "image SERP", "convert to webp", or "image file optimize".

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。

Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".

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。

Maps intelligence for local SEO — geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification (Google/Bing/Apple/OSM), competitor radius mapping, and LocalBusiness schema generation from API data. Three-tier capability: free (Overpass + Geoapify), DataForSEO (full intelligence), DataForSEO + Google (maximum coverage). Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", "maps analysis", "local rank tracking", "Share of Local Voice", or "SoLV".

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。

Full-site crawling, scraping, and site mapping via Firecrawl MCP. Use when user says "crawl site", "map site", "full crawl", "find all pages", "broken links", "site structure", "discover pages", "JS rendering", or needs site-wide 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。

Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", "GEO", "AI search", "LLM optimization", "Perplexity", "AI citations", "ChatGPT search", or "AI visibility".

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。

Discover and validate product ideas, analyze markets, scope MVPs, and optimize app store presence for iOS/macOS apps. Use when user asks to discover, validate, assess, scope, or analyze product ideas, market opportunities, or when they mention "product agent", "app idea validation", "should I build this", "MVP", "market analysis", or "ASO".

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。

Guides you through comprehensive iOS/Swift app planning and analysis. Use for new apps (concept to architecture) or existing apps (audit current state, plan improvements, evaluate tech stack). Covers product planning, technical decisions, UI/UX design, and distribution strategy.

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。

Deep market analysis for iOS/macOS apps including market sizing (TAM/SAM/SOM), growth trends, market maturity, entry barriers, distribution channels, and revenue potential. Use when user asks for market research, market size, market opportunity, growth potential, TAM/SAM/SOM, or market trends.

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。

Guideline-4.3 anti-spam / originality gate — score whether an app (and each portfolio addition) is meaningfully distinct in function, content, and metadata before you invest or submit. Use at validation/new-app time and again before submission. Protects the whole developer account from 4.3 (spam/duplicate) rejections. NOT rejection-handler (that works an existing rejection) and NOT competitive-analysis (that positions in the market).

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 ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.

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。

End-to-end product development for iOS/macOS apps. Covers market research, competitive analysis, PRD generation, architecture specs, UX design, implementation guides, testing, and App Store release. Use for product planning, validation, or generating specification documents.

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。

Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).

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。

Git change frequency hotspot analysis — find the most-changed files in your codebase

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。

Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.

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。

Provides reverse engineering techniques. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.

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 for authorized macOS and Mach-O reverse engineering including codesign, Objective-C/Swift recovery, endpoint security surfaces, and Apple platform malware 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。

User-invoked learning toolkit — guided reading, investigation, pattern extraction, or second opinions. Manual `/maestro-learn` only; NEVER auto-invoke for code exploration or analysis — route those intents to the analyze step via /maestro-next

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 the `hf` CLI for Hub authentication, downloads, uploads, repositories, cache, jobs, buckets, webhooks, and endpoint administration. Use for CLI operations, not model recommendations, paper analysis, training design, or building a Space.

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。

Get a token-efficient overview of any project using file tree, code structure, and call graph 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。

Map code questions to the optimal tldr command by detecting intent and routing to the right analysis layer.

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。