「prompt」の検索結果

Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.

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 /update, @update, update project docs, or asks to sync task.md, changelog.md, and files.md after completing work.

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。

Convex UI component patterns from the live Storybook preview. Use when building React components, forms, modals, navigation, feedback states, or app layouts that should match the current Convex design system. Applies to both shared primitives and dashboard style product UI.

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。

Router for Convex static-hosting deploy requests. Prefer deploydev for /deploydev and deployprod for /deployprod.

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。

Review a feature and enhance it with purposeful animations, micro-interactions, and motion effects that improve usability and delight. Use when the user mentions adding animation, transitions, micro-interactions, motion design, hover effects, or making the UI feel more alive.

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。

Routes general Convex requests to the right project skill. Use when the user asks which Convex skill to use or gives an underspecified Convex app task.

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。

Build reactive, type-safe, production-grade backends. ALWAYS use this skill when the user asks to build, plan, design, or implement backend features, APIs, data models, server logic, database schemas, web apps, full stack apps, or mobile apps. This includes planning and architecture discussions.

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 deciding WHERE to do work on Syntropic137 itself - "run this through Syntropic", "dispatch a workflow", "should this go in a workspace or local", "kick off a run on the Mini", "dogfood this", "build Syntropic inside Syntropic", "why did the agent's push fail", "can an agent run our QA", "the execution says completed but nothing shipped". Covers what a workspace agent can and cannot do, why `.github/workflows` pushes must come from the local machine, how to write a task prompt that does not repeat a known failure, and how to verify a run actually delivered. Do NOT use for PR/release mechanics (see `devops`), for using the `syn` CLI as an end user (see the syntropic137 plugin skills), or for authoring workflow YAML.

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 向けテキスト指示（skill / slash command / task プロンプト / CLAUDE.md 節 / コード生成プロンプト）を、バイアスを排した実行者に動かしてもらい、両面（実行者の自己申告 + 指示側メトリクス）で評価して反復改善する手法。改善が頭打ちになるまで回す。プロンプトや skill を新規作成・大幅改訂した直後、またはエージェントの挙動が期待通りにならない原因を指示側の曖昧さに求めたいときに使う。

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。

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。

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。

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。

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

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 guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.

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Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.

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。

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_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。

Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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。

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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You know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.

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You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.

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。

Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII Tespit and content policy enforcement, and the Guardrails AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts, stripping PII, ...

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。

Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapt

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。

Edit PDF text/typos/titles via nano-pdf CLI (NL 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。