「structured output」の検索結果

An infographic skill for AI agents — works inside Claude Code, Codex, Hermes, OpenClaw, Cursor, and any agent host that loads skills. Create professional infographics as self-contained HTML files with PNG exports. Use when the user asks to create, design, generate, build, or make an infographic, data visualization, visual explainer, chart poster, cheatsheet, or pocket guide. Trigger phrases include "make this visual", "turn this into a graphic", "visualize this data", "infographic for X", "cheatsheet for X", or when the user provides structured data (tokenomics allocations, ecosystem/partner lists, timelines, comparisons, step-by-step processes) AND asks for a visual output. Covers tokenomics, ecosystem overviews, game overviews, cheatsheets, airdrop guides, comparisons, and process flows. Do NOT trigger for plain text answers, markdown tables, bulleted summaries, questions asking for advice or explanations about a topic (even when the topic is tokenomics, ecosystem, vesting, etc.), inline chat diagrams, or data processing with no visual output requested. The signal is "asks for a visual", not "mentions a keyword".

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。

Choose OpenRouter models and routers, query live catalog data, use structured outputs and batch inference, and debug routing with metadata. Use when working with OpenRouter APIs, openrouter/auto, openrouter/free, model discovery, pricing, providers, batch jobs, or structured JSON responses.

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。

Transform transcripts, conversations, or raw notes into shareable content — either multiple social posts OR a single long-form article. Learns style from user's published content. Handles transcripts of any length (tested up to 5500+ lines / 90+ min). Use when user: (1) provides transcript/conversation/recording and wants social content, (2) asks to "turn recording/notes into posts", (3) mentions generating content from recordings/notes, (4) needs multiple publishable pieces from single source, (5) asks to write an article from a transcript — e.g. "write an article/long-form", "organize into an article", "write up this session", "turn this talk into a blog post", "deep dive". Social mode outputs 2-15 ready-to-publish pieces. Article mode outputs a structured long-form piece (3000-10000+ chars) written section-by-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。

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

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。

Central authority for Claude Agent SDK (TypeScript and Python SDKs). Covers SDK installation, authentication (Anthropic key, Bedrock, Vertex), sessions and resumption, forking sessions, streaming vs single mode, custom tools, permissions (allowedTools, disallowedTools, permissionMode), MCP integration, system prompts (CLAUDE.md, appendSystemPrompt, outputStyle), cost tracking, todo tracking, structured outputs, hosting patterns, plugins, and SDK branding guidelines. Assists with building custom agents, configuring SDK options, and troubleshooting SDK issues. Delegates 100% to docs-management skill for official documentation.

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 a set of design samples (HTML mockups + website URLs) and produce a structured style guide that anchors the rest of the stardust redesign pipeline. Use when the user supplies reference designs ("design samples", "mockups", "inspiration sites", "style references") and wants to extract a consistent visual language — type system, color palette, spacing, motion, and reusable component patterns — before redesigning a target site. Triggers on phrases like "distill these samples", "extract a style guide from these examples", "use these as design references", or "analyze these mockups". Outputs a `trait-matrix.json` (structured per-sample + cross-sample design tokens) and a `SAMPLES.md` narrative brief that `stardust:direct` reads under Mode B (anchor-reference precedence) when picking a redesign direction.

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。

Design structured evaluation rubrics for scoring LLM and agent outputs — defining quality dimensions, scoring scales, hard gates, score descriptions, and edge cases. Load when the user asks to create an eval rubric, define evaluation criteria, design scoring dimensions, write an eval spec, or says "what should I evaluate", "design a rubric", "create eval criteria", "define quality dimensions", "evaluation rubric for", "how do I measure quality of". Sub-skill of eval-output orchestrator.

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 a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

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。

The Shape System — the content_ir kind registry for structured content. Use when adding, activating, or rendering a kind/shape, writing `__kind` JSON, kind schemas or samples, XML-tag/fence detection surfaces, kind components, workflow node input_kind/output_kind, or binding agent output to a kind.

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 LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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 GNU awk (gawk) one-liner generation and transformation. Use this skill whenever the user asks to process text fields, extract columns, compute sums or aggregates, filter structured output, reformat CSV/TSV, parse logs, generate reports, or do any record/field processing task. Trigger on keywords like "awk", "extract column", "sum a field", "count occurrences", "print field N", "filter by column", "reformat output", "aggregate log data", "process CSV", "TSV", or any request that maps to a field-oriented text transformation. When in doubt, use this skill — awk is often the right tool when sed isn't enough.

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エージェントが失敗や同じ操作を繰り返す原因を、エラーと実行状況から整理します。小さな復旧操作を試し、結果と根拠を引き継げる報告にまとめるスキルです。

• エージェントの連続失敗を診断したいとき
• 同じツール操作のループ調査
• 情報の蓄積による出力劣化の点検

AIエージェントの成果物を、正確さ・網羅性・明瞭さ・実行しやすさ・簡潔さの5軸で自己評価し、根拠付きの点数と優先順位を付けた改善案をまとめます。

• 複数ファイルの実装後の自己評価
• 複数工程を終えた成果物の点検
• 設計文書や分析レポートの評価

質問応答や文書検索を組み合わせたAI処理をDSPyで構築するスキル。入力と出力を定義して部品を組み合わせ、学習例と評価指標を使ってプロンプトを自動調整します。

• 文書検索付きの質問応答を作りたいとき
• 学習例でプロンプトを調整したいとき
• 文章から構造化データを抽出したいとき

Integrate Firecrawl `/scrape` into product code for single-page extraction. Use when an app already has a URL and needs markdown, HTML, links, screenshots, metadata, or structured page output. Prefer this skill over broader crawl patterns when the feature is page-level.

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-powered PPT generation with document analysis and styled images via the NanoBanana stack. Combines image generation with structured deck output.

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. For researching accounts you're selling to, see prospecting.

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 needs a comprehensive marketing plan for a client, a company they advise, or their own product. Also use when the user mentions "marketing plan," "growth plan," "GTM plan," "go-to-market plan," "AARRR plan," "90-day marketing plan," "12-month marketing roadmap," "fractional CMO plan," "fCMO plan," "market sizing," "TAM SAM SOM," "how big is this market," or "market research." Generates an exhaustive 13-section plan structured by AARRR (Acquisition, Activation, Retention, Referral, Revenue), customized to the client's current budget, team, and stage, mapped to future funding milestones, cross-referenced with the 139-idea marketing-ideas library and an embedded 17-section current-state audit rubric, with a marketing operations stack showing which skills and MCP/API integrations execute each part. Outputs a Notion-paste-ready markdown document. For positioning and ICP context before planning, see product-marketing. For stage-specific deep work, see onboarding, signup, emails, referrals, pricing.

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。

Runs the Code Mate Qoder CN CLI non-interactively with structured output. Use when the user asks to delegate a bounded coding task to Qoder.

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 eth2-quickstart to autonomously deploy a hardened Ethereum node, install execution and consensus clients, configure validator metadata, expose RPC safely, and inspect node health with structured JSON output.

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。

Render Markdown from templates and structured data using Knap CLI. Use when the user asks to apply a Knap template, turn JSON or CSV data into notes, batch-generate Markdown files, or format Defuddle output into a note.

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。

Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

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 structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.

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。