Help users design and execute a high-signal interview process that prioritizes real-world performance over superficial charisma or pedigree.
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
Help users design and execute a high-signal interview process that prioritizes real-world performance over superficial charisma or pedigree.
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。
Help users identify why a team is struggling and implement a structured recovery plan by addressing leadership gaps, individual performance, and structural misalignments.
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。
Codified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships. Informed by transportation managers with 15+ years experience. Includes scorecarding frameworks, RFP processes, market intelligence, and compliance vetting. Use when managing carriers, negotiating rates, evaluating carrier performance, or building freight 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。
Email campaign/sequence performance review composite. Pulls campaign data (sends, opens, replies, bounces), reads actual email copy and subject lines, analyzes reply content (objections, positive interest, questions), and produces a diagnostic report covering quantitative metrics, copy quality, lead quality, and actionable recommendations. Tool-agnostic — works with Smartlead (MCP), Instantly, Outreach, Lemlist, Apollo, or CSV 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。
Diagnose Meta Ads campaign performance and account gaps using Meta's actual system mechanics — including customer-journey coverage, Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue. Use for performance diagnosis, account audits, full-funnel or TOF/MOF/BOF gap analysis, deciding what to test or create next, and producing novice-friendly recommendations without forcing every campaign or ad into a funnel stage.
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 .NET types for performance. Seal classes, use readonly structs, prefer static pure functions, avoid premature enumeration, and choose the right collection types.
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 guidance for implementing OpenTelemetry instrumentation in .NET codebases, covering tracing (Activities/Spans), metrics, logs, naming conventions, error handling, performance, SDK setup, resources, context propagation, and API design best practices.
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 alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda scheduling, soft-core potentials, MBAR/BAR analysis, cycle-closure validation, and protocol-appropriate enhanced sampling. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.
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 wants automated performance profiling, headless Instruments analysis, or CLI-based trace collection.
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 an Apple-platform app feels slow, memory grows, battery drains, or ANY performance issue needs diagnosing. Covers memory leaks, profiling, Instruments workflows, retain cycles, 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 the user mentions GRDB performance review, slow GRDB queries, app-group database setup audit, a ValueObservation that stopped updating, or pre-release GRDB scan.
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 mentions SwiftUI performance, janky scrolling, slow animations, or view update issues — expensive bodies, formatters, whole-collection dependencies, and missing lazy containers.
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 new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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。
Profile, diagnose, and remediate SwiftUI runtime performance using code review, Instruments, and repeatable measurements. Use when a SwiftUI screen renders slowly, scrolling or animations hitch, view bodies update excessively, list identity churns, layout work spikes, or broad Observation dependencies raise CPU cost. Covers evidence-based triage, SwiftUI Instruments lanes, lazy-container guardrails, state lifetime, and before/after verification.
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 capturing or analyzing ETTrace profiles for a focused iOS launch or runtime flow, including exact-build dSYM UUID matching, Simulator or device capture, processed per-thread flamegraph JSON, sampled inclusive/exclusive time, unresolved symbols, and comparable verification. Use debugging-instruments or swiftui-performance for generic profiling instead.
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。
Debug iOS apps and profile performance using LLDB, the interactive Memory Graph Debugger, and Instruments. Use for crashes, retain-cycle inspection, hangs, build failures, and generic CPU, memory, energy, or network profiling. Use ios-memgraph-analysis for .memgraph capture, leaks CLI ownership paths, or persistent heap growth; use ios-ettrace-performance for ETTrace capture and JSON.
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。
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
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。
Upgrade C, C++, and Fortran MEX source files from the Separate Complex (SC) API to the Interleaved Complex (IC) API. Use when converting mxGetPr/mxGetPi to mxGetComplexDoubles, migrating MEX files to -R2018a, adding MX_HAS_INTERLEAVED_COMPLEX guards for SC/IC guarded builds, or modernizing legacy MEX code that uses mxGetData/mxSetData/mxGetImagData/mxSetImagData. Covers C (.c), C++ (.cpp, .cxx), and Fortran (.F, .f90) MEX functions. Triggers on: interleaved complex, IC MEX, mex upgrade, mex migration, separate complex, mxGetPi, mxGetPr replacement, -R2018a, complex MEX, Fortran MEX, .F MEX file, C++ MEX, .cpp MEX file, MEX performance, MEX slow complex, MEX call overhead, improve MEX performance complex.
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。
Generate, verify, refine, and accelerate C/C++ or CUDA code from MATLAB with MATLAB Coder, Embedded Coder, GPU Coder, or MATLAB Test. Also covers writing codegen-ready MATLAB code: language constraints, coder.* directives, and optimization patterns. Triggers on: codegen, MEX, deploy MATLAB as C/C++, GPU Coder, coder.screener, coder.config, coder.gpuConfig, coder.typeof, coder.runTest, matlabtest.coder.TestCase, SIL, embedded config, no dynamic memory, EnableMexProfiling, coder.timeit, coder.perfCompare, %#codegen, writing codegen-ready MATLAB, code generation readiness, coder.varsize, coder.unroll, coder.noImplicitExpansionInFunction, coder.ceval, coder.inline, coder.extrinsic, coder.const, coder.classSignature, class codegen limitations, temporal types codegen, DMA-off, stack-only, host-target InstructionSetExtensions, SIMDAcceleration, OptimizeReductions, host SIMD tuning, host OpenMP, codegen performance.
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 dynamic application security testing with OWASP ZAP, Burp Suite, and Nikto. Test running applications for security vulnerabilities through automated and manual testing. Use when testing web applications, APIs, or performing penetration testing.
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 and analyze e-commerce sales performance across platforms. Set up KPI dashboards, trend analysis, and performance alerts to catch issues and opportunities early.
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。
Writing efficient code that handles large data and tight constraints
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。
React 19 performance patterns and composition architecture for Vite + Cloudflare projects. 50+ rules ranked by impact — eliminating waterfalls, bundle optimisation, re-render prevention, composition over boolean props, server/client boundaries, and React 19 APIs. Use when writing, reviewing, or refactoring React components. Triggers: 'react patterns', 'react review', 'react performance', 'optimise components', 'react best practices', 'composition patterns', 'why is it slow', 'reduce re-renders', 'fix waterfall'.
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。
Modern web design trends, principles, and implementation patterns for 2024-2025. Use this skill when designing websites, creating interactive experiences, implementing design systems, ensuring accessibility, or building performance-first interfaces. Triggers on tasks involving modern design trends, micro-interactions, scrollytelling, bold minimalism, cursor UX, glassmorphism, accessibility compliance, performance optimization, or design system architecture. References animation skills (GSAP, Framer Motion, React Spring), 3D skills (Three.js, R3F, Babylon.js), and component libraries for implementation guidance.
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。