「structured output」の検索結果

Agent-Friendly CLI Spec v0.1 workflow skill. Use this skill when the user needs Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description 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。

Agent-Friendly CLI Spec v0.1 workflow skill. Use this skill when the user needs Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description 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。

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。

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. 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。

Generates a 3-month SEO performance HTML report for any domain using DataForSEO data. Fetches baseline vs current traffic, keyword rankings, top content pages, and competitive landscape, then outputs a report styled with the Infrasity brand design system: #0D0A1A background, #8157F2 purple accent, Instrument Sans + DM Sans + DM Mono typography. Use this skill whenever a user provides a target domain and a list of competitor URLs and asks for an SEO report, performance report, SEO analysis, competitive SEO comparison, traffic report, ranking report, or 3-month SEO summary. Also trigger when the user says "generate SEO report for X vs Y and Z", "create a performance report", "compare my SEO against competitors", or pastes a domain and asks how it's performing versus the market. Always use this skill for SEO report generation — do not attempt to build the report without following this structured data-fetch and HTML generation workflow.

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。

Prospects companies across developer-focused tech verticals for developer marketing agency outreach. Use this skill whenever a user provides a vertical (e.g. AI Agentic, IAC, DevTools, Observability, DevOps, FinOps, AI/SDLC, AI Orchestration, or any other tech vertical) and wants a list of prospect companies to target. Also trigger when the user says "prospect for", "find companies in", "give me leads for", "build a prospect list", or provides a vertical name alongside funding stage and headcount filters. Always use this skill — never attempt prospecting without following this structured workflow. The skill finds exact-fit companies only (never adjacent), maps a real outreach signal and email pain point for each, sources every data point with a URL, and outputs a unified nine-column table: Company, URL, LinkedIn, Headcount, Signal, Signal Sources, Geography, Pain Point, Pain Point Sources.

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。

Generates a fully structured SEO content **outline** (not a finished brief) and exports it as a formatted .docx Word document. The output is a skeleton for a writer to fill in — section headings, topic prompts, angles — not pre-written paragraphs. Use this skill whenever a user provides a blog title, focus keyword, domain URL, or any combination of those and asks to generate an outline, brief, blog brief, SEO brief, article structure, or content plan. Also trigger when the user says "create a brief for X", "generate an outline for [topic]", "make a content brief", "I need a brief for [URL or keyword]", or pastes a title and asks what the structure should look like. This skill handles input validation, domain analysis, keyword enrichment, audience inference, archetype-aware section selection, and full .docx generation — always use it rather than writing ad-hoc outlines.

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 AI MCP release QA, scoped to this pack's own server (mcp/suede-skills-mcp.mjs) and its catalog, install, and docs surface. Runs the full JSON-RPC lifecycle against a live server (initialize, notifications/initialized, ping, tools/list, tools/call, resources/list, resources/read, prompts/list, prompts/get), plus protocol negotiation, closed input and output schemas, tool annotations, structuredContent with text fallbacks, malformed-input probes, clean stdio, catalog-to-folder agreement, and install-path language. Use when the MCP server, mcp/catalog.json, a tool, resource, or prompt definition, or the MCP install docs change, or before publishing an MCP release. A check that did not run against the live server is a FAIL, not a skip. NOT FOR: a generic third-party MCP server, which this skill's hardcoded surface does not describe; fixing or testing the public install path itself (use suede-launch-packaging).

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。

Parse git history and produce or update a CHANGELOG.md following the Keep a Changelog convention. Supports Conventional Commits, basic prefix conventions, and unstructured commit messages. Intelligently categorizes changes, detects breaking changes, links to PRs/issues, and handles both initial generation and incremental updates. **Triggers — use this skill when:** - User asks to "generate", "create", "update", or "write" a changelog - User mentions "CHANGELOG", "changelog", "release notes" - User says "document changes", "what changed since last release" - User wants to "prepare a release" and needs a changelog entry - User asks to "clean up" or "reformat" an existing changelog **Covers:** Any git-based project. Handles Conventional Commits (feat/fix/chore), Angular convention, basic prefixes (Add/Fix/Remove), and freeform commit messages. Outputs Keep a Changelog format with optional Common Changelog enhancements.

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。

Generate comprehensive status reports for git repositories. Analyzes commits, codebase structure, type errors, branch health, dependency status, GitHub PRs/issues/CI, working tree changes, and optionally integrates with `td` (task management CLI) if a `.todos` folder is present. **TRIGGERS - Use this skill when:** - User asks for the status of a git project or repo - User asks "what's going on in [project]" or "give me an overview of [repo]" - User wants a project summary, progress report, or codebase overview - User asks about recent commits, code structure, or project health - User says "check my project" or "project status" **Output:** A rich, structured text report covering overview, branches, commit history, codebase breakdown, GitHub activity, dependency health, issues/errors, working tree changes, task management, and summary.

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。

Convert image files into Word, Excel, PPT, PDF, HTML, RTF, CSV, TXT, or JSON with ComPDF. Use for screenshots, scans, receipts, forms, and photo-based business documents that need editable or structured 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。

Applies production AI integration patterns for chat, structured output, guardrails, provider routing, and AI UX. Use when adding LLM-powered features to an application.

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。

Prompt engineering for production LLMs — structured outputs, evals, RAG, tool workflows, multimodal prompting, and safety. Use when designing, debugging, or shipping 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。

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," or "fCMO plan." 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 full 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。

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。

Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.

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。

Parse PDFs, Office files, images, and HTML into Markdown/structured outputs with MinerU. Use when SciForge or Codex needs document parsing/OCR for scientific papers, supplementary files, PDFs, scanned documents, tables, formulas, or URL/local-file parsing through MinerU standard or Agent lightweight APIs.

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 PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.

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。

Side-by-side comparison of two financial scenarios (buy vs rent, new job vs stay, payoff house vs invest, Roth vs Traditional, lease vs buy car, etc). Projects each scenario over 10 years, compares total cost, opportunity cost, risk profile, and produces a structured recommendation. Output saved as FINANCE-COMPARE.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。

Expert guide for type-safe Python AI agent development with Pydantic AI — dependency injection, structured outputs, model-agnostic routing, and graph workflows / Panduan ahli pengembangan agen AI Python type-safe dengan Pydantic AI.

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 guide for Prompt Engineering, Chain-of-Thought, few-shot prompting, structured output, prompt injection defense, and automated AI evaluations & regression benchmarking (Promptfoo, DeepEval) / Panduan ahli rekayasa prompt dan evaluasi otomatis AI.

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。

Synthesis step in the multi-angle technical research pipeline. Receives structured outputs from all four research angles (api-state, ecosystem-research, impact-measurement, codebase-auditor), applies cross-angle signal weighting and conflict resolution, and produces a single synthesized Research section. Invoked by the technical-researcher orchestrator after all angle skills complete. Returns content to the orchestrator — does not write to the backlog.

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。

Reviews LLM integration, prompt templates, model selection, token/cost budgets, evaluation harnesses, structured outputs, retries, and streaming. Checks trust boundaries and task evidence against the selected provider/model contract rather than prescribing universal model tiers or sampling settings.

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 extracting specific data points from large agent output transcripts, kaizen analysis reports, or JSONL session files — tool timings, query counts, error summaries, or any structured facts — without loading raw data into orchestrator context. Activates when the orchestrator needs targeted facts from large files and context pollution must be avoided.

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。