データ分析、機械学習、LLMアプリ、プロンプト設計（8,894 件）
概要と使いどころ
Use when modifying, converting, or transforming structured data with dasel v3 — in-place mutations, format conversion, batch operations, array manipulation, object construction, and merge patterns across JSON, YAML, TOML, XML, CSV, HCL, INI
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 exploring unknown structured data files with dasel v3 — discover schema, list keys, find nested values, sample arrays, identify data types across JSON, YAML, TOML, XML, CSV, HCL, INI formats
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。
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or when users request help with ideation, marketing, and strategic planning. Explores user intent, requirements, and design before implementation using research-validated prompt patterns.
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 transcript analysis findings into actionable improvements. Triggers on "generate hooks from findings", "improve agent", "fix anti-pattern", "kaizen improvement", "generate hook proposals", or "create improvement plan". Provides templates for hook generation, agent prompt refinement, skill patches, CLAUDE.md updates, and script automation from analysis 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。
Decompose substantive work into phases, dispatch each phase to a sub-agent, and adjudicate what comes back. Use whenever a request asks for implementation, investigation, a fix, a review, or any change to files — including small ones — and whenever you are about to read source or run a diagnostic yourself instead of handing it off. Also use when a report from a sub-agent needs judging, when a phase needs re-dispatching, or when a user names one instance of a pattern. Does not apply when your own prompt begins "Your ROLE_TYPE is sub-agent." — then follow references/sub-agent-contract.md instead.
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。
Walks a user from one rough content idea to a finished post, platform-adapted variants, and repurposed follow-on assets using a 7-step sequential prompting workflow. Use when the user wants to turn a half-formed idea, frustration, story, or client situation into publishable content quickly and consistently — covering idea extraction, hook generation, outline, draft, humanizer pass, platform adaptation (LinkedIn, Reddit, X/Twitter, newsletter, Telegram), and repurpose engine.
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。
Explains Chain of Verification (CoVe) prompt design — a 4-step pattern separating generation from independent factual verification. Use when designing prompts that require factual accuracy, reducing hallucinations, checking technical standards or APIs, or producing step-by-step procedures where subtle errors are costly.
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。
Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.
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。
Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.
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。
Turns a raw Qualtrics conjoint export into an analysis-ready long-format dataset, covering export settings, identifying the implementation, package or manual wide-to-long reshaping, choice-profile alignment, ratings, reference categories, pilot exclusion, and subgroup merges. Use when the user has a conjoint CSV to clean, reshape, or debug, cannot align choices with profiles, or asks what the data must look like before AMCE estimation. Design goes to conjoint-design, validity review to conjoint-diagnostics.
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。
Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.
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, improve, or audit Vapi voice agent and Squad system prompts for production phone and web based voice agents. Use when the user wants help designing a Vapi assistant prompt, multi-assistant Squad prompt set, refining an existing prompt, creating prompt sections, building an intake or handoff workflow, improving tool-use instructions, adding guardrails, or optimizing voice-agent behavior for brevity, turn-taking, error handling, caller data collection, escalation, handoffs, and spoken formatting.
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。
Opt-in recreation of the Bun + TypeScript framework used to build Vapi's landing-page voice agents from a ROUGH_DRAFT.md spec, including a scenario registry, language stacks, prompt composer, assistant builder, and idempotent bootstrap script. Use only when the user explicitly invokes vapi-bootstrap-framework or specifically asks to reproduce or experiment with the landing-page-agent architecture. Do not use for ordinary Vapi assistant builds; use create-assistant, create-tool, create-squad, and vapi-prompt-builder instead. Targets Bun + TypeScript + @vapi-ai/server-sdk.
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, create, or validate saved and transient Vapi voice assistants. Use for new phone or web agents, production system prompts and first messages, saved-versus-transient architecture, model/voice/transcriber selection, multilingual compatibility, existing tool attachment, native call-control tools, assistant hooks, and Create Assistant API validation errors.
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。
Incremental LLM-friendly wiki generator for Obsidian note vaults. Use when: (1) Building wiki from notes, (2) Ingesting notes to wiki, (3) Obsidian LLM wiki, (4) Incremental knowledge base management. Triggers: 'build wiki from notes', 'ingest notes to wiki', 'Obsidian LLM wiki', 'incremental knowledge base'.
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。
Semantic codebase search, code indexing, and prompt enhancement via standalone CLI. Use when: (1) Semantic code search with natural language queries, (2) Code indexing for remote codebase retrieval, (3) Prompt enhancement with codebase context, (4) Before grep/find/glob operations for better accuracy, (5) Complex requirements clarification, (6) Large codebase navigation. Triggers: "search context", "enhance prompt", "find code that", "index project", "clarify requirements". IMPORTANT: Always use ace-tool BEFORE grep/find/glob for semantic-level code location.
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。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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。
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
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。
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
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。
Performance measurement, attribution modeling, and marketing ROI analysis. Use when setting up tracking, analyzing campaign performance, building attribution models, or creating marketing reports.
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。
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
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 raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.
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。
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
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。
构建一个全自动化的AI驱动数据收集代理，适用于任何公共来源——招聘网站、价格信息、新闻、GitHub、体育赛事等任何内容。按计划进行抓取，使用免费LLM（Gemini Flash）丰富数据，将结果存储在Notion/Sheets/Supabase中，并从用户反馈中学习。完全免费在GitHub Actions上运行。适用于用户希望自动监控、收集或跟踪任何公共数据的场景。
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。