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Data viz design: production-level visual encodings, chart contrast, RTL axes. Use when implementing, reviewing or testing data viz design for React, Vite, Next.js or Perfect_AI projects.

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。

Plan and execute Convex schema migrations safely, including adding fields, creating tables, and data transformations. Use when schema changes affect existing 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。

Plans Convex schema and data migrations with widen-migrate-narrow and @convex-dev/migrations. Use for breaking schema changes, backfills, table reshaping, or zero-downtime rollouts.

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 says /localstop, localstop, stop localhost for this app, /localstopall, localstopall, or asks to stop local development servers for this Teleprompt app.

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。

Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data 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。

Reference for Tinker SDK types — Datum, ModelInput, TensorData, SamplingParams, response types, error types, and helper functions. Use when the user needs to build training data, construct model inputs, understand response objects, or handle 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。

Set up and run supervised fine-tuning (SFT) on instruction or chat datasets using the Tinker API. Use when the user wants to do instruction tuning, chat fine-tuning, or supervised learning.

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。

Set up and run multi-turn RL training for interactive environments (terminal tasks, tool use, search/RAG, games) using the Tinker API. Use when the user wants multi-turn RL, agentic training, tool-use RL, or interactive environment training.

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。

Guide for defining RL environments — the Env protocol, EnvGroupBuilder, RLDataset, and custom environment creation. Use when the user asks about RL environments, reward functions, or how to define custom tasks for RL training.

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。

Set up and run Direct Preference Optimization (DPO) training on preference datasets using the Tinker API. Use when the user wants to train with preference data, chosen/rejected pairs, or DPO.

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。

Token budgeting discipline for all agents. Treats tokens as a resource from session start — not just when the buffer hits 60%. Covers prompt compression, context hygiene, handoff packing, and HOT memory pruning.

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。

Behavioral guardrails for implementation work. Reduces common LLM coding mistakes: overcomplication, silent assumption-picking, scope drift, and unverifiable success criteria. Load on Builder and software-engineer-mode.

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 team lead points Architect at a prior agistra.dev hub path and asks it to check for or perform additional migration — trigger phrasing such as 'migrate my data from <path>', 'check what didn't get migrated', 'did we miss anything from my old hub', or 'do a completeness pass on <path>'. Walks the prior hub's full directory tree read-only, diffs it against the fixed scripted migration scope plus any existing migration report, and asks the team lead one clear question at a time about anything a scripted migration would never have discovered. Never writes or copies anything itself — every confirmed item is delegated to the existing migration primitives. Do not use for the first-run scripted migration prompt during `npm run setup` — that is unrelated, already-automated flow this skill does not replace.

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。

Living project knowledge for OpenFoodJournal — an iOS 26 food journaling app with AI-powered nutrition scanning. Consult this skill FIRST when starting any work on this project. Contains architecture decisions, data models, service contracts, current state, known issues, and conventions. Update this skill whenever requirements, architecture, or project state changes so it stays accurate across sessions.

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。

Resize and validate App Store screenshots with current asc screenshot-size data and macOS sips. Use when preparing or fixing screenshots for App Store Connect submission.

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。

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

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 and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.

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。

Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.

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 in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt 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。

When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.

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 patterns for Segment Customer Data Platform including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destinations configuration, and data governance best practices. Use when: segment, analytics.js, customer data platform, cdp, tracking plan.

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。

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

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 in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

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。

Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.

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。