「structure」の検索結果

Audit and improve content for AI Overviews and answer engines, including citability, entity clarity, crawler access, brand signals, and passage structure.

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Enhance and improve existing AI prompts using professional techniques from a 2,500+ prompt database. Adds detail, fixes structure, optimizes for specific models, and applies proven patterns from top-performing prompts. Use when user says "enhance my prompt", "improve this prompt", "make this prompt better", "optimize prompt", "fix my prompt", or "upgrade this prompt".

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。

Analyze existing XML sitemaps or generate new ones with industry templates. Validates format, URLs, and structure. Use when user says "sitemap", "generate sitemap", "sitemap issues", or "XML sitemap".

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Translate existing blog posts into one or more target languages with SEO-optimized localization. Produces native-quality translations that preserve markdown structure, frontmatter, schema JSON-LD, image and chart embeds, and evidence-backed explanations. Localizes keywords, meta tags, numbers, dates, currencies, and quote styles per locale. Flags machine-translation artifacts for review. Run BEFORE blog-localize: this handles language conversion; localize handles cultural adaptation after translation completes. Use when user says "translate blog", "blog translate", "uebersetzen", "traduire", "traducir", "translate post", "blog auf Deutsch", "blog en espanol".

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Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Not for verifying statistics (blog-factcheck) or gathering authority sources. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".

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 one blog post file for AI-citation readiness (self-contained, sourced passages, entity clarity, structure, crawler access) and score it with an internal 0-100 heuristic plus per-surface notes for AI Overviews, AI Mode, ChatGPT, Perplexity, Claude, Gemini, and Copilot. Use when the user says "geo", "aeo", "ai citation", "citation audit", "chatgpt citation". Not for a site-wide audit, live AI-visibility tracking, or schema generation (use claude-seo seo-geo, blog-audit, blog-schema), and not a promise of citations.

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Create and manage writing personas with NNGroup 4-dimension tone framework (Funny-Serious, Formal-Casual, Respectful-Irreverent, Enthusiastic-Matter-of-fact). Personas define readability targets, sentence length distribution, vocabulary tier, contraction frequency, and summary box label. Used by blog-write and blog-rewrite to enforce consistent voice. Use when the user says "create persona", "use persona", "list personas", or wants tone sliders and writing rules saved as structured JSON. Not for measuring style from existing posts (blog-style) or for audience, positioning, and taboo-phrase context files (blog-brand).

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。

Establish durable brand and voice context for cross-skill consumption. Generates BRAND.md (audience, positioning, do/don't editorial rules, taboo phrases, competitor differentiation) and VOICE.md (existing persona JSON re-expressed as readable prose), both written to the project root. When present, the blog orchestrator loads these files, fenced as untrusted data, for write, rewrite, brief, outline, calendar, and strategy workflows. Pairs with blog-persona, which manages the structured persona JSON. Use when the user says "blog brand", "create brand context", "BRAND.md", "VOICE.md", "establish editorial brand". Not for learning a voice from existing posts (blog-style) or managing persona JSON (blog-persona).

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 Reddit Ads measurement, campaign structure, community and interest targeting, creative-native fit, catalog advertising, budgets, brand safety, and reporting. Use for Reddit Ads, promoted posts, conversation ads, community targeting, Reddit Pixel, Reddit Conversions API, or Reddit dynamic product ads.

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Audit Meta Ads measurement, Pixel and Conversions API, attribution, Facebook and Instagram creative, audiences, placements, automation, budgets, account structure, and policy. Use for Meta Ads, Facebook Ads, Instagram Ads, Advantage+, Pixel, CAPI, Events Manager, creative fatigue, or Meta campaign optimization.

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Audit Apple Ads measurement, AdServices and AdAttributionKit, campaign and keyword structure, Search Match, App Store placements, custom product pages, bidding, budgets, MMP reconciliation, and policy. Use for Apple Ads, Apple Search Ads, App Store ads, Search Match, custom product pages, AdServices, or Apple app-install campaigns.

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Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Grok Build, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 78 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "auto research", "switch model", "hand off a session", "session handoff", "switch engine", "continue on codex", "continue on claude", "move this session to another engine", "multi-agent coding", "coding session", "session inbox", "grok session", "grok build", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness", "clawo runs", "run ledger", "session cost", "coding-agent token usage", "spend cap", "budget limit", "maxBudgetUsd", "durable workflow", "resume a run", "verify a coding run", "acceptance contract", "evidence bundle", "did the agent's tests actually pass", "protected tests", "agent edited the tests", "human gate", "repair loop", "clawo workflow", "clawo verify", "clawo solve", "clawo fanout", "fix and verify", "solve from the terminal".

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。

Edit academic prose in LaTeX manuscripts or supplied text at light, moderate, or strict intensity. Improve grammar and clarity while preserving scientific claims, uncertainty, numbers, citations, and LaTeX structure.

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 whenever the user wants to create or improve a presentation for an academic context — conference papers, seminar talks, thesis defenses, grant briefings, lab meetings, invited lectures, or any presentation where the audience will evaluate reasoning and evidence. Triggers include: 'conference talk', 'seminar slides', 'thesis defense', 'research presentation', 'academic deck', 'academic presentation'. Also triggers when the user asks to 'make slides' in combination with academic content (e.g., 'make slides for my paper on X', 'create a presentation for my dissertation defense', 'build a deck for my grant proposal'). This skill governs CONTENT and STRUCTURE decisions. For the technical work of creating or editing the .pptx file itself, also read the pptx SKILL.md.

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 prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.

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。

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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 guidance for Swift Testing: test structure, #expect/#require macros, traits and tags, parameterized tests, test plans, parallel execution, async waiting patterns, and XCTest migration. Use when writing new Swift tests, modernizing XCTest suites, debugging flaky tests, or improving test quality and maintainability in Apple-platform or Swift server projects.

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Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.

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Create polished dark-themed architecture diagrams as self-contained HTML+SVG files. Use when the user asks for system, infrastructure, cloud, security, or network topology diagrams.

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Diagnoses, remediates, and manages Google Cloud Identity and Access Management (IAM) access issues. Supports two distinct operational modes: (1) Requester Flow for developers encountering access denials (capturing error context, self-service PAM JIT activations, elevated developer self-remediation, or logging structured tickets), and (2) Resolver Flow for privileged administrators (authoritative Policy Troubleshooter analysis, deny policy exemptions, least-privilege role discovery, and PAM/IAM provisioning).

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。

Restructure code while preserving behavior. Use for extraction, consolidation, ownership moves, or cleanup where verification must bracket structural edits.

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。

Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).

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Diagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.

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Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global 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。