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概要と使いどころ
Use when working with Mysql — mySQL and MariaDB database analysis, performance tuning, query optimization, and health monitoring. Covers slow query investigation, index analysis, table statistics, replication health, InnoDB status, connection pool analysis, and schema inspection.
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Use when working with Cockroachdb — cockroachDB cluster health, range distribution, SQL statistics, schema change monitoring, and query optimization.
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 working with Timescaledb — timescaleDB hypertable analysis, chunk management, continuous aggregates, compression policies, and time-series optimization.
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 working with Trino — trino (Presto) query analysis, catalog management, worker health, memory allocation, and query optimization.
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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.
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An advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion).
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。
AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.
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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.
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Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.
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Optimize Core Web Vitals (LCP, INP, CLS) for better page experience and search ranking. Use when asked to "improve Core Web Vitals", "fix LCP", "reduce CLS", "optimize INP", "page experience optimization", or "fix layout shifts".
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 en Docker (Dockerfile optimization, multi-stage builds, Compose, registry)
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。
Customer journey v3 — mapping, touchpoints, pain points, optimization, moments of truth
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 en parcours client (mapping, touchpoints, pain points, optimization, moments of truth)
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Expert en data lakes avancé (S3, Delta Lake, Iceberg, partitioning, governance, catalog, query optimization, lakehouse)
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Compiler designer v2 — lexing, parsing, AST, code generation, optimization, LLVM, DSLs
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Expert en ingénierie du contexte IA (compression de prompts, chunking, gestion tokens, optimisation fenêtre)
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 en cloud v2 (AWS, GCP, Azure, multi-cloud, optimisation coûts, contexte DZ)
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 en ASO (keywords, screenshots, reviews, A/B testing, featured)
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Expert en ASO avancé (keywords, screenshots, reviews, A/B testing, featured, marché DZ, App Store, Google Play)
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 en optimisation des coûts d'agents IA (sélection modèle, caching, compression prompts, budgeting tokens)
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。
A/B Testing Specialist IA — Expert en tests A/B (hypothèses, significativité, multivarié, culture d'expérimentation).
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Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
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。
Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy 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。