「overview」の検索結果

Generate comprehensive test case documentation from test code, test framework output, existing test docs, and source code context. Use when documenting test suites, creating test specifications, generating test coverage reports, onboarding developers to testing practices, or preparing QA documentation. Analyzes test functions (pytest, unittest) to extract test names, docstrings, assertions, and organization, then produces structured markdown with both overview-level summaries and detailed test case specifications including purpose, preconditions, steps, expected results, and test data. Triggers when users ask to document tests, generate test specifications, create test reports, summarize test coverage, or explain what tests do.

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Generate comprehensive, user-friendly README.md files for code repositories. Use when creating documentation for new projects, updating existing READMEs, or improving project onboarding. Produces READMEs with project introduction, prerequisites, environment setup, executable usage instructions, and repository structure overview. Supports application projects, libraries, and research codebases.

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Produces comprehensive summaries and insights about legacy codebases to help understand unfamiliar code. Use when onboarding to a new project, planning refactoring efforts, assessing code for acquisition/migration, or generating documentation for undocumented systems. Analyzes architecture, dependencies, code quality issues, and test coverage. Creates high-level overviews with architecture diagrams, key components, entry points, and actionable insights for understanding and improving legacy code.

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Generate concise summaries of source code at multiple scales. Use when users ask to summarize, explain, or understand code - whether it's a single function, a class, a module, or an entire codebase. Handles function-level code by explaining intention and core logic, and large codebases by providing high-level overviews with drill-down capabilities for specific modules.

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Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. **Perfect for:** - Public company valuation (M&A, investment analysis) - Benchmarking performance vs. industry peers - Pricing IPOs or funding rounds - Identifying valuation outliers (over/under-valued) - Supporting investment committee presentations - Creating sector overview reports **Not ideal for:** - Private companies without comparable public peers - Highly diversified conglomerates - Distressed/bankrupt companies - Pre-revenue startups - Companies with unique business models

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Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.

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Conduct comprehensive market research combining web search, academic sources, news coverage, and industry data. Produces market sizing signals, competitive landscape overviews, trend analysis, and evidence-backed market opportunity assessments.

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Map any industry's competitive landscape using company databases, academic research, news coverage, LinkedIn activity, and job market signals. Produces industry overviews with key players, market segments, growth indicators, and emerging disruptors.

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Build circular genome visualizations using circlize (R), pyCirclize (Python), or Circos (Perl CLI) with ideogram tracks, multi-data tracks (scatter, histogram, heatmap), chord/link arcs for interactions, and explicit circos.clear() between plots. Covers when circular is appropriate vs when Cartesian wins (Cleveland-McGill 1984), karyograms, and chromosome adjacency in chord diagrams. Use when adjacency on the circle conveys meaning — chromosome-level overview, structural variants, Hi-C interactions, cross-genome comparisons.

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Load project-specific context -- tech stack, architecture overview, directory layout, trusted sources, and conventions -- so that all skills operate with awareness of what this project actually is. Use when a knowledge base document exists, or when the user asks about the project's tech stack, architecture, conventions, framework, directory layout, or says 'tell me about this project', 'what are we using?', 'what's our stack?', or 'what framework is this?'. Use the knowledge-priming-refiner to create a knowledge base document.

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Audit how AI search engines (ChatGPT, Perplexity, Google AI Overviews, Claude) describe and recommend your product, then propose fixes. Triggers on requests to audit AI search visibility, improve AEO/GEO, check ChatGPT/Perplexity coverage, or when user asks "do we show up in AI search?", "AEO audit", "generative engine optimization", "AI discoverability", "how does ChatGPT describe us?", "Perplexity ranking". Outputs GTM-AEO-* entries and a Coverage Matrix.

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Define how system components connect, establishing boundaries, patterns, and integration approaches during PRD v0.6 Architecture. Triggers on requests to design architecture, create system design, define component relationships, or when user asks "design architecture", "system design", "how do components connect?", "architecture decisions", "technical architecture", "system overview". Consumes TECH- (stack selections), RISK- (constraints), FEA- (features). Outputs ARC- entries documenting architecture decisions with rationale. Feeds v0.6 Technical Specification.

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Vulnerability audit and dataflow analysis built on the neug CPG engine. file_audit (default, recommended): first builds a function-level summary graph (tier1) over the entire project, then incrementally builds detailed CPGs (tier2) on demand for chosen functions; both tiers coexist in a single neug database (unified schema, cross-layer refines edges; C/Java/Python/Rust). All capabilities are exposed as CLI tool subcommands (audit/overview/build/query/methods/paths), so an agent can drive the audit step by step via shell; the audit subcommand can also run the built-in LLM loop. The same tooling also supports a dataflow analysis mode (Workflow C): no vulnerability hunting, only variable-level dataflow tracing for functions of interest, producing dataflow_analysis.json. repo_audit: multi-round LLM/Cypher audit over a full-repo CPG. Use when the user asks to audit a codebase/project for vulnerabilities, scan a project with CPG/neug, run repo_audit/file_audit, find source-to-sink taint paths, or analyze dataflow inside a function. Supports C, Java, Python, and Rust.

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Find local-business SEO prospects in a single command. Searches Google for "<niche> <city>", filters out aggregators (Yelp, Yellow Pages, Angi), enriches each remaining domain with DataForSEO Labs domain overview, On-Page technical audit, and Backlinks summary in parallel, then ranks the businesses by a closeability score (0-100) — highest scores are the easiest to land as SEO clients.

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60-second SEO snapshot powered by the DataForSEO API. Three parallel calls (DataForSEO Labs domain overview, Backlinks summary, ranked keywords) for core metrics on any domain — total keywords, traffic estimate, backlinks, top rankings — without waiting for a full audit.

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。

AI SEO workflow skill. Use this skill when the user needs Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

AI SEO workflow skill. Use this skill when the user needs Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Interpret video content visually by sampling frames into timestamped contact sheets that can be read as images. Use when: user asks what happens in a video; asks to summarize, describe, review, or QA video content or footage; asks about scenes, actions, people, or objects in a video; needs a storyboard-style overview of a clip; asks to find where something occurs in a video. Triggers on 'watch this video', 'what's in this video', 'summarize the video', 'describe the footage', 'contact sheet', 'storyboard', 'review this clip', 'find the scene where', 'scene detection', 'shot boundaries', 'detect cuts', 'dwell points'. For converting, trimming, or transcoding video, use processing-video instead.

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Generate hierarchical _FEATURES.md files that describe what a codebase DOES from a user/consumer perspective, anchored to source symbols via tree-sitting. Supports large complex codebases through feature-driven decomposition into sub-feature files. Uses a multi-pass synthesis: orientation → detail → overview rewrite. Use when someone says "what does this do", "document features", "feature inventory", "_FEATURES.md", or needs to understand a codebase's purpose before modifying it. Complements tree-sitting (structural) with semantic (why/what-for) 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。

Use when asked about harness9 architecture, module design, or how components interact — explains the system 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。

SE Ranking AI visibility analyst (extension). Tracks AI Share-of-Voice across ChatGPT, Gemini, Perplexity, AI Overviews, and AI Mode in a single query. Highest-impact new extension per the v2 gap analysis — no other vendor covers all 5 AI platforms in one API.

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Blog strategy development including topic cluster architecture with hub-and-spoke design, audience mapping, competitive landscape analysis, AI citation surface strategy across ChatGPT/Perplexity/AI Overviews, distribution channel planning (YouTube, Reddit, review platforms for GEO), content scoring targets, measurement framework, and content differentiation through original research and first-hand experience. Use when user says "blog strategy", "content strategy", "blog positioning", "what should I blog about", "blog topics", "content pillars", "blog ideation".

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When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' or 'optimize for Claude/Gemini.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

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。

Suede-owned SEO and generative-search audit discipline. Runs a nine-lane evidence-based audit: access, intent, metadata, structure, supported schema, E-E-A-T, clusters, and exact rewrite fixes. Use when a page or site needs a deep standalone SEO/AI-visibility audit with scored lanes. NOT FOR: a fast A-F launch-appeal grade (use suede-visibility-grader); conversion-path rework after the audit (use suede-site-alchemy); getting cited by ChatGPT/Perplexity/AI Overviews (use suede-ai-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。