「output」の検索結果

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

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。

When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.

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。

Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.

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 an Estuary derivation that flattens a nested array into one output document per array element. Use for normalising nested JSON, exploding line items, unpacking tags, or producing one-row-per-item tables from arrays. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (some destinations can `UNNEST` server-side without one). Use when user says "flatten-array derivation", "derivation to explode an array", "transformation to unnest", "derive one row per item", or "flatten nested data with a derivation".

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。

Write Estuary derivations in Python — async-generator transforms, Pydantic-typed source/output, optional persistent state, and dependency installation via uv. Use for ML/embeddings, async API calls, or any transform where SQL and TypeScript are awkward. Derivations add complexity and cost (and Python derivations require a private/BYOC data plane) — confirm the user wants a Python derivation before reaching for this. Use when user says "python derivation", "write a derivation in python", "embed with a python derivation", "ML derivation", "call an API from a derivation", or anything mentioning model2vec / huggingface / pydantic in a derivation context.

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。

Run flowctl catalog status to check the control-plane state of an Estuary resource, or list catalog specs to see which connectors exist and which are active. Use when checking whether a task is running, disabled, or failing, interpreting status output, or enumerating connectors. Use when user says "check task status", "is my capture running", "catalog status", "flowctl catalog status", "what's wrong with my capture", "task failing", "check catalog status", "collection status", "derivation status", "why is my task disabled", "list my connectors", "show me my connectors", "list captures", "list materializations", "what connectors do I have", or "show me all my active connectors".

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。

Converts TypeScript to idiomatic Dart 3 code, covering syntax mapping, type system translation, async patterns, and module migration. Activates when translating TypeScript interfaces, classes, enums, generics, union types, async/await patterns, or modules to Dart; migrating frontend or Node.js logic to Flutter/Dart; converting RxJS Observables to Dart Streams; adapting TypeScript utility types to Dart equivalents; or producing idiomatic Dart 3 output (sealed classes, records, pattern matching) from TypeScript source.

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。

Generative UI (GenUI) for Flutter using AI models like Google Gemini, Anthropic Claude, or OpenAI via the genui package. Use this skill when building dynamic AI-driven interfaces, conversational UI flows, server-rendered screen layouts, LLM-powered dashboards, chat-based UI generation, or implementing prompt-to-UI systems. Supports AI-generated widgets, reactive data binding, ContentGenerator integration, and A2uiMessageProcessor for real-time UI updates from AI responses. Ideal for apps where UI structure is determined by AI/LLM output rather than hardcoded layouts.

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。

Builds SEO-friendly web applications with Dart using the Jaspr framework. Use when creating Dart-based websites, blogs, or landing pages requiring semantic HTML output; implementing server-side rendering (SSR) or static site generation (SSG); adding Tailwind CSS via jaspr_tailwind; routing with jaspr_router; testing components with jaspr_test; or building full-stack Dart web apps where Flutter Web's canvas rendering is unsuitable.

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。

Reproduce, isolate, and diagnose a specific software failure before changing production behaviour, then propose the smallest safe fix with regression protection. Use whenever the user reports a bug, crash, error, stack trace, flaky test, incorrect output, "works on my machine," intermittent failure, regression after a deploy, or any behaviour that diverges from expectation and the cause is not obvious. Prefer this over jumping straight to a fix. Part of the audit suite; inherits the shared constitution, while its own job is proving causality.

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。

Small-business bookkeeping skills for converting financial evidence into accountant-reviewable outputs, including setup/context, receipt processing, expense categorization, bank reconciliation, tax preparation, monthly close, jurisdiction-aware tax references, plus specialized skills for home office, vehicle, meals, depreciation, 1099 contractors, and estimated taxes. Trigger on "help me with my bookkeeping", "set up my bookkeeping", "bookkeeping context", "chart of accounts", "process my receipts", "categorize expenses", "reconcile my bank statement", "get my books ready for taxes", "close the month", "organize my finances".

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。

Extract structured data from receipts and invoices for bookkeeping, expense tracking, and tax preparation. Supports email, photos, scanned images, PDFs, and accounting exports. Outputs vendor, date, amount, tax, line items as table, CSV, or JSON. Trigger on "process receipts", "extract receipts from email", "scan invoices", "capture expenses".

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 wants to capture a real repo's working patterns as a reusable skill — analyzes the local git log and diffs the agent runs itself and drafts a new SKILL.md describing the team's coding patterns, into a scratch/output path the user names. Never overwrites an existing skill.

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。

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

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 Nauthilus validation, guardrails, dependency patch refreshes, vendoring, generated-output checks, `make release-guardrails`, govulncheck-sensitive publishing, structured commits, branch/tag choreography, website-doc side effects, and final clean-state reporting.

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 about to claim work is complete, fixed, or passing — requires running verification commands and confirming output before making any success claims. Evidence before claims, always.

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。

Document templates for all pipeline output documents — specs, indexes, reports, and strategy docs. Use when any workflow needs to produce a structured document.

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。

Decompose approved architecture design into IA shards and layered spec structure, then validate dependencies and tracking outputs

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 ideation completeness through constraints, exhaustion, rubric checks, and output compilation

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。

Exhaustive idea extraction through recursive breadth-before-depth exploration with Deep Think protocol and fractal structure. Use during /ideate to transform raw ideas into comprehensive, structured ideation output. Writes to a fractal folder hierarchy — every node has an index, CX file, and children.

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。

Orchestrate ideation from raw input to validated ideation outputs with shard composition

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 Kotlin Compose design tokens from one of three supported sources — Google Stitch (MCP), Figma (MCP or JSON), or Codex Design (kta-design-spec output, Codex HTML/markdown artifacts). Three-layer architecture (Primitive/Semantic/Component) with code generation. Use when user asks to "generate design tokens", "create theme from Figma/Stitch/Codex design", "build Compose tokens from design spec", or "convert design source to tokens". Refuses any other input source and asks user for correct seed 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。

Senior Kotlin specialist code review — framework-agnostic. Use whenever the user asks to review, audit, critique, or get feedback on Kotlin code, a file, a class, a function, a module, a diff, a branch, a commit, or a pull request. Judges Kotlin language idioms, logic correctness, architecture and layering, concurrency (coroutines, Flow, structured concurrency, cancellation, race conditions), null safety, exception safety, testability, performance, and security. Detects force-unwraps, unchecked casts, GlobalScope, leaked mutability, broken structured concurrency, hidden side effects, weak abstractions, untestable seams, allocation hot spots, weak crypto, input-validation gaps, and PII logging. Outputs prioritized findings with file:line anchors and minimal-diff fixes. No assumptions about DI framework, UI framework, or build system.

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 Kotlin Compose design tokens from one of three supported sources — Google Stitch (MCP), Figma (MCP or JSON), or Claude Design (kta-design-spec output, Claude HTML/markdown artifacts). Three-layer architecture (Primitive/Semantic/Component) with code generation. Use when user asks to "generate design tokens", "create theme from Figma/Stitch/Claude design", "build Compose tokens from design spec", or "convert design source to tokens". Refuses any other input source and asks user for correct seed 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。