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概要と使いどころ
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
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 for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown 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。
Use when cognee is slow, expensive, or hitting rate limits — speeding up or throttling ingestion (remember/cognify batching, chunk size, LLM and embedding rate limits, per-stage models), cutting read latency, estimating cost before ingesting, running work in the background, or planning how to scale a deployment.
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。
Perform a structured code review using change detection and impact
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。
Extract a sanitized shared team/company brain from a personal brain. Strips internal ratings, compensation, performance assessments, retention and political dynamics from pages, takes, and facts across the full scan scope (people, companies, meetings, dailies, cross-references — not just people/), verifies with grep + retrieval passes, and purges sensitive git history behind the data-loss-gate confirmation card. Also runs as a report-only re-audit on an existing shared brain.
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。
Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.
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。
Drive a native GUI app (macOS, Windows, Linux) via the cua-driver CLI (default) or MCP server; snapshot its accessibility tree, act through snapshot-bound element tokens, native menu paths, exact window geometry, or pixel coordinates, and verify from fresh state. Use when the user asks you to operate, drive, automate, or perform a GUI task in a real application on the host, or to continue, resume, or recall recent Cua activity.
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 you need to visually interact with a GUI: test buttons, fill forms, verify visual layouts, fuzz web pages, automate user flows, take screenshots, or perform end-to-end QA on any application. Works on cloud VMs, Docker containers, local machines, and sandboxes. Needs the `cua` CLI (curl -fsSL https://cua.ai/install.sh | sh).
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。
Analyzes sales data from PayPal and QuickBooks to find top performers and slow movers, layers in seasonality, and produces a prioritized 30-day content brief: what to push, what offers to run, what to hold. Strategic output only — no calendars or assets. Use when the user asks what to post, wants a content plan, asks what's selling, or what to promote this month.
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。
The ads consultant that becomes an ads agent. Reads paid-ad performance natively through the TikTok Ads connector, or via a build-connector Zapier connection for other ad platforms, explains in plain English what is working and what is burning money, recommends budget, targeting, and creative changes with the dollar impact of each, drafts new ad copy in the owner's voice plus Canva creative briefs, and — only after an explicit yes — executes the approved changes in the ad account. Works from exported CSV reports when nothing is connected, the normal starting point. Use this whenever the owner talks about ads or ad spend at all, including "are my ads working," "what's my ROI on ads," "I'm spending money on Facebook and I don't know what I'm getting," "should I turn this campaign off," "my cost per lead is going up," "write me a new ad," "who should I be targeting," or "the ad guy quit."
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。
Reconcile accounts by comparing GL balances to subledgers, bank statements, or third-party data. Use when performing bank reconciliations, GL-to-subledger recs, intercompany reconciliations, or identifying and categorizing reconciling items.
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 and analyze product metrics with trend analysis and actionable insights. Use when running a weekly, monthly, or quarterly metrics review, investigating a sudden spike or drop, comparing performance against targets, or turning raw numbers into a scorecard with recommended actions.
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 draft multi-email sequences with full copy, timing, branching logic, exit conditions, and performance benchmarks. Use when building onboarding, lead nurture, re-engagement, win-back, or product launch flows, when you need a complete drip campaign with A/B test suggestions, or when mapping a sequence end-to-end with a flow diagram.
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 code changes for security, performance, and correctness. Trigger with a PR URL or diff, "review this before I merge", "is this code safe?", or when checking a change for N+1 queries, injection risks, missing edge cases, or error handling gaps.
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。
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell 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。
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
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 a Mastra maintainer through publishing an npm snapshot from a pull request or a specified repository branch. Use when asked to release, publish, or create a PR snapshot, branch snapshot, canary package build, or branch-specific npm tag. Performs source and branch preflight checks, requires confirmation before the irreversible publish, dispatches the existing Publish to npm workflow, monitors it, and reports installable package tags.
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。
Drive a native GUI app (macOS, Windows, Linux) via the Qwen Cua Driver CLI (default) or MCP server; snapshot its accessibility tree, act through snapshot-bound element tokens, native menu paths, exact window geometry, or pixel coordinates, and verify from fresh state. Use when the user asks you to operate, drive, automate, or perform a GUI task in a real application on the host.
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 asks to prepare for SOC 2 audits, map Trust Service Criteria, build control matrices, collect audit evidence, perform gap analysis, or assess SOC 2 Type I vs Type II 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。
Senior Quality Manager Responsible Person (QMR) for HealthTech and MedTech companies. Provides quality system governance, management review leadership, regulatory compliance oversight, and quality performance monitoring per ISO 13485 Clause 5.5.2. Use when leading management reviews, setting quality policy and objectives, monitoring quality KPIs and cost of quality, or exercising QMR governance and regulatory oversight responsibilities.
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。
Generates high-converting landing pages as complete Next.js/React (TSX) components with Tailwind CSS. Creates hero sections, feature grids, pricing tables, FAQ accordions, testimonial blocks, and CTA sections using proven copy frameworks (PAS, AIDA, BAB). Outputs SEO meta tags, structured data, and performance-optimised code targeting Core Web Vitals (LCP < 1s, CLS < 0.1). Use when the user asks to create a landing page, marketing page, homepage, single-page site, lead capture page, campaign page, promo page, or conversion-optimised web page — or when they want to A/B test landing page variants or replace a static page with one designed to convert.
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 develop social media strategy, plan content calendars, manage community engagement, or grow their social presence across platforms. Also use when the user mentions 'social media strategy,' 'social calendar,' 'community management,' 'social media plan,' 'grow followers,' 'engagement rate,' 'social media audit,' or 'which platforms should I use.' For writing individual social posts, see social-content. For analyzing social performance data, see social-media-analyzer.
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。
Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.
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。
Atlassian Jira expert for creating and managing projects, planning, product discovery, JQL queries, workflows, custom fields, automation, reporting, and all Jira features. Use when setting up or configuring Jira projects, writing JQL and advanced searches, creating dashboards, designing workflows, or performing technical Jira operations.
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。