Task sequences, progressive disclosure, and the pacing of an experience over time. Use when designing multi-step flows, evaluating onboarding, or diagnosing why users get lost.
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。
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概要と使いどころ
Task sequences, progressive disclosure, and the pacing of an experience over time. Use when designing multi-step flows, evaluating onboarding, or diagnosing why users get lost.
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 and evaluate programmatic SEO strategies for creating SEO-driven pages at scale using templates and structured 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。
LLM Agent engineering: pattern selection (12-mode matrix), data simulation, convergence iteration. Covers intent routing, function calling, ReAct, MCP, prompt chaining, guardrails, evaluation.
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 design, optimize, or evaluate App Store screenshots and preview videos. Also use when the user mentions "screenshots", "app preview", "product page design", "screenshot design", "creative assets", or "what should my screenshots show". For A/B testing screenshots, see ab-test-store-listing. For full ASO audit, see aso-audit.
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。
Audit and improve SwiftUI runtime performance. Use when diagnosing slow rendering, janky scrolling, high CPU, memory usage, excessive view updates, layout thrash, body evaluation cost, identity churn, view lifetime issues, lazy loading, Instruments profiling guidance, and performance audit requests.
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 discover, evaluate, or prioritize App Store keywords. Also use when the user mentions "keyword research", "find keywords", "search volume", "keyword difficulty", "keyword ideas", or "what keywords should I target". For implementing keywords into metadata, see metadata-optimization. For auditing current keyword performance, see aso-audit.
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。
Stress-tests ideas by surfacing assumptions, counterarguments, and confidence gaps. Use when evaluating claims, proposals, plans, or decisions that need rigorous intellectual scrutiny.
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。
Helps detection engineers identify coverage blind spots in system command execution detection rules, evaluate bypass risks, and reduce false negatives. Strictly limited to system command execution rules; not applicable to browser credential theft or abnormal process creation without command semantics.
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 job description, hiring post, or recruitment page for role requirements, evaluation criteria, keywords, risks, and resume or cover letter angles. Use when the user provides a JD URL/text, asks what a company is looking for, wants JD-resume gap analysis, or needs inputs for a tailored cover letter. 한국어 요청도 포함합니다 - 채용공고 분석, 공고 분석, JD 분석, 직무기술서 분석, 자격요건, 우대사항, 이 공고 뭘 원하는지, 공고랑 내 경력 비교.
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。
Provide universal memory capabilities for different AI agents and development frameworks, unifying the capture, retrieval, and management of user, project, and task context.
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 building or changing Skyline UI (apps/syn-ui, packages/syn-ui/*) from a design - "build this screen from the board", "implement the design", "match the canvas", "new board landed", "build the Eval trend panel", "port this screen to Skyline", "does this match the design", "update the Shipped table", "snapshot the boards". Covers reading a `*.dc.html` board and the Skyline spec, building from them, comparing 1440px and 390px screenshots against the board, and recording the shipped screen. Do NOT use for small tweaks to a screen that has already shipped (change the code directly, no board needed), for the frozen React app apps/syn-dashboard-ui (never edit it), or for refreshing the CI screenshot baselines alone (see design/README.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。
Use when acting as the orchestrator agent that drives Syntropic137 - running the dogfood loop, deciding what to dispatch next, landing agent PRs, deploying a beta, triaging failed executions, or keeping the scratchpad, paper cuts and retrospectives current. Trigger phrases include "orchestrator tick", "keep the loop running", "what should we dispatch next", "land these PRs", "why are PRs stacking up", "deploy the next beta", "pit stop", "the run failed, what now", "write the retro", "is the system helping", "scale quality", "learning loop for the orchestrator". Do NOT use for deciding whether one task belongs in a workspace or locally (see `dogfooding`), for turning one incident into an eval case (see `learning-loop`), for designing a workflow A/B experiment (see `workflow-experimentation`), or for release-branch and version-bump mechanics (see `devops`).
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 using completers — TokenCompleter and MessageCompleter for text generation during RL rollouts and evaluation. Use when the user asks about generating text, completing messages, or using completers in RL environments.
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。
Analytical methodology skill for intelligence-grade research. Covers cognitive security, logical fallacy detection, source epistemology, and probability discipline. Load when evaluating sources, building analytical assessments, or detecting manipulation in information.
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。
Evaluates code against Apple's App Store Review Guidelines. Use this skill when reviewing iOS, macOS, tvOS, watchOS, or visionOS app code (Swift, Objective-C, React Native, or Expo) to identify potential App Store rejection issues before submission. Triggers on tasks involving app review preparation, compliance checking, or App Store submission readiness.
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。
Comprehensive software architecture skill for designing scalable, maintainable systems using ReactJS, NextJS, NodeJS, Express, React Native, Swift, Kotlin, Flutter, Postgres, GraphQL, Go, Python. Includes architecture diagram generation, system design patterns, tech stack decision frameworks, and dependency analysis. Use when designing system architecture, making technical decisions, creating architecture diagrams, evaluating trade-offs, or defining integration 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。
This skill should be used when the user asks to "perform vulnerability scanning", "scan networks for open ports", "assess web application security", "scan wireless networks", "detect malware", "check cloud security", or "evaluate system compliance". It provides comprehensive guidance on security scanning tools and methodologies.
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 plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," or "free resource." This skill bridges engineering and marketing — useful for founders and technical marketers.
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。
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system 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。
This skill should be used when the user asks to "test for broken authentication vulnerabilities", "assess session management security", "perform credential stuffing tests", "evaluate password policies", "test for session fixation", or "identify authentication bypass flaws". It provides comprehensive techniques for identifying authentication and session management weaknesses in web applications.
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。
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
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。
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
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。
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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。