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概要と使いどころ
Research any topic across China's major social platforms — Weibo, Douyin, Xiaohongshu, Bilibili, and Zhihu — simultaneously. Aggregates discussion volume, sentiment, key opinion leaders, and platform-specific trends for comprehensive China market intelligence.
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 influencer performance metrics across TikTok, Instagram, YouTube, Bilibili, and Kuaishou. Compare engagement rates, audience growth, content consistency, and cross-platform presence for data-driven partnership decisions.
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 hiring patterns, job market demand, salary signals, and talent competition across LinkedIn and Apollo. Covers job posting analysis, company hiring velocity, role requirement trends, and competitive talent landscape mapping.
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。
Track real-time events, announcements, and breaking developments across Twitter, Reddit, news outlets, and web search. Monitors event progression, public reaction, key moments, and aftermath coverage for crisis response and opportunity detection.
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。
Monitor brand mentions, sentiment, and reputation across Twitter, Reddit, news, and social platforms through SandBase. Use when asked for brand monitoring, reputation tracking, social listening, PR coverage analysis, or competitive brand intelligence.
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。
Discover and evaluate KOLs (Key Opinion Leaders) and creators across TikTok, Instagram, YouTube, and Xiaohongshu through SandBase. Use when asked for influencer research, creator outreach lists, KOL evaluation, or partnership prospecting.
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。
Translate text content between languages with high quality and natural phrasing. Covers document translation, content localization, and multi-language research support for teams working across language barriers.
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。
Search and monitor news articles across Google News through SandBase. Use when asked for news monitoring, current events research, news coverage analysis, or media tracking.
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。
Track hashtag performance across Instagram, TikTok, Twitter, and Xiaohongshu simultaneously. Monitors post volume, engagement rates, top contributors, and content patterns for campaign tracking, trend research, and community activation measurement.
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 any topic across the last 30 days using Reddit, Twitter, YouTube, Hacker News, web search, and news sources through SandBase. Use when asked for recent trends, launch reactions, competitor activity, person/company profiles, or current event analysis.
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 target audiences across Reddit, Twitter, Instagram, and Xiaohongshu to understand demographics, interests, pain points, language patterns, and content preferences. Produces audience personas backed by real community 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。
Research online communities across Reddit, Telegram, and social platforms. Analyzes community size, engagement patterns, discussion themes, moderation culture, and member demographics for community strategy, partnership, and go-to-market planning.
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 implementing or modifying behavior that must stay consistent across TUI and Web (mode/input/tool/replay/order). Require canonical semantics first, then TUI/Web adapters, then renderer-specific 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。
Safe refactoring workflow using trace-mcp — assess risk, find candidates, check impact, and rename symbols across all files without missing import sites or cross-file references.
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 trace-mcp apply_codemod for any bulk mechanical change instead of repeated Edit calls. Activate whenever the same edit pattern would be applied 2+ times, across one file or many.
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。
Cross-agent self-inspection of your AI-agent stack. Audits skills, MCP servers, hooks, plugins, commands, credentials, and memory files across Claude Code, Codex, OpenClaw, and NanoClaw. Produces a structured inventory and narrative briefing with cross-ecosystem risk analysis. Use when the user asks to audit their own setup, check what they have installed, review their agent stack security posture, or understand cross-tool interactions. Use when a user has accumulated skills/plugins/MCP servers over time and wants visibility into their attack surface. Use after installing new skills or plugins. Do NOT use for vetting external code before install (that is repo-forensics). Do NOT use for incident response during active attacks. Do NOT use for fixing or patching vulnerabilities (forensify is read-only).
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 products across the web with Firecrawl and produce a shopping recommendation or cart-ready summary. Use when the user wants to compare products, find the best option, evaluate reviews, respect budget/preferences, or shop with a saved browser session.
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 a learner or instructor asks for a report, summary, reflection, progress note, performance recap, activity metrics, instructor-shareable record of a MATLAB AI tutoring session, aggregate report across multiple MATLAB tutoring session reports, or instructor dashboard artifact with metric drilldowns. Supports optional start and end datetime arguments for multi-session report and dashboard ranges.
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 this skill when the user wants to author, design, scope, or refine an Agent Skill (a SKILL.md file). Trigger phrases include "build a new skill", "design an agent skill", "scope a SKILL.md", "how should I structure this skill", "write a skill for X", "my skill isn't working well", or any request to improve an existing SKILL.md. Walks the user through an empirical, test-first process — probe the agent for real failures, design only for genuine knowledge gaps, iterate against runnable examples, and verify across models.
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 this skill for all requirements-related work in a MATLAB MBSE project using the Requirements Toolbox (slreq). Covers creating and populating requirement sets, derivation links, test case requirements, verification coverage, reading and tracing links across requirement sets and models, checking link health, allocating requirements to components (Implement links), and building traceability reports. Trigger when the user asks about slreq API, slreqx files, slmx link files, outLinks/inLinks, traceability matrices, coverage analysis, broken links, or mapping requirements to architecture components. Use proactively for any requirements or traceability 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。
Design environment strategy for testing across dev, CI, preview, staging, and production — Docker Compose test infrastructure, multi-stage Dockerfiles, seed-data lifecycle, per-PR preview environments, production parity, and external-dependency stubbing at the HTTP boundary. Use when: "set up test environment," "docker-compose for tests," "per-PR preview environment," "staging parity," "spin up test infra," "environment tiers." Not for: choosing mock-vs-stub-vs-fake per dependency — use service-virtualization; factory and fixture data patterns — use test-data-management; pipeline/Actions config — use ci-cd-integration. Related: test-data-management, ci-cd-integration, contract-testing, service-virtualization.
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 EXISTING test code for quality, smells, and testability issues. Detects test smells across six dimensions — readability, reliability, diagnostic value, design, AI-generated, and coverage — analyzes testability of application code, and backs the qualitative smells with mutation testing. Use when: "review my tests," "test quality audit," "test smells," "testability analysis," "are these tests any good." Not for: generating new tests — use `ai-test-generation`. Not for: testing AI features in your product — use `ai-system-testing`. Related: unit-testing, shift-left-testing, coverage-analysis, ai-test-generation.
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。
End-to-end testing of email-dependent flows — signup confirmation, password reset, magic-link login, OTP/MFA codes, and notification emails. Covers the capture-inbox decision tree (Mailpit, Mailosaur, MailSlurp, Ethereal), Playwright polling without fixed sleeps, regex extraction of links/OTPs from the email body, deterministic per-test addresses (plus-addressing, per-inbox), subject/from/header/link assertions, and SPF/DKIM/DMARC deliverability checks as a separate suite. Use when: "test the signup confirmation email," "password reset email test," "magic-link login test," "capture OTP from email," "Mailpit," "Mailosaur," "MailSlurp," "email arrives flaky in CI," "assert email subject/from/links." Not for: Sending transactional email from your app code, or API-only contract tests of an email provider — those are api-testing / app concerns. Email HTML rendering across clients (Outlook/Gmail dark mode) is out of scope (note it as a gap; use visual-testing or Litmus). Related: playwright-automation, api-testing, test-data-management, qa-project-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。
Create or modify a konsistent.json configuration file that enforces structural conventions in a TypeScript codebase. Use when the user wants to enforce consistent code structure, validate exports/imports across files, ensure directories contain required files, enforce naming patterns, add/remove/update convention rules, configure case map overrides for acronyms or special casing (kebabToPascalMap, kebabToCamelMap), or troubleshoot why konsistent is reporting violations. Triggers on: "konsistent", "konsistent.json", "enforce conventions", "structural consistency", "consistent exports", "consistent structure", "code conventions config", "add convention", "update convention", "fix konsistent errors", "case map", "case override", "acronym casing".
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。