「orr」の検索結果

Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing.

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Use when a user asks to "proofread", "review and correct", "fix grammar", "improve readability while keeping my voice", and to proofread a document file and save an updated version.

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。

Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.

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 working changes - code quality, security, library correctness, and standards auto-fix

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Fit and independently validate rigid scan alignment from named, source traceable correspondences.

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Design, validate, and optimize schema.org structured data for eligibility, correctness, and measurable SEO impact.

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Reviews Swift code for concurrency correctness, modern API usage, and common async/await pitfalls. Use when reading, writing, or reviewing Swift concurrency code.

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Diagnose, fix, or validate SwiftUI animation and scroll behavior by temporarily instrumenting views with MotionEyes, capturing console traces over time, and comparing motion data to user intent or test expectations. Use when users report bugs such as wrong direction, premature fade, timing or easing mismatch, unexpected movement, missing movement, incorrect relative movement between views, scroll jumps, scroll restoration drift, content-offset desynchronization, or when they want to turn MotionEyes traces into focused assertions. Prefer XcodeBuildMCP log capture first and fallback to CLI log streaming; remove only agent-added MotionEyes instrumentation after validation.

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Implement, review, or improve localization and internationalization in iOS/macOS apps — String Catalogs (.xcstrings), LocalizedStringKey, LocalizedStringResource, pluralization, FormatStyle for numbers/dates/measurements, right-to-left layout, Dynamic Type, and locale-aware formatting. Use when adding multi-language support, setting up String Catalogs, handling plural forms, formatting dates/numbers/currencies for different locales, testing localizations, or making UI work correctly in RTL languages like Arabic and Hebrew.

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Always use when working on any iOS, macOS, Swift, SwiftUI, Xcode, or Apple platform project. Routes to the correct specialized skills based on development phase and task context. This collection contains 210 skills from 28 authors covering the full iOS lifecycle.

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。

Implement, review, or improve SwiftUI features using the iOS 26+ Liquid Glass API. Use when asked to adopt Liquid Glass in new SwiftUI UI, refactor an existing feature to Liquid Glass, or review Liquid Glass usage for correctness, performance, and design alignment.

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Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization 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。

Run convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.

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Use when: the agent captures a correction from the team lead, hits an unexpected error, discovers a capability gap, or finds a better practice. Logs to .learnings/ and promotes durable patterns to permanent project memory.

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。

Implement and review iOS 26+ SwiftUI Liquid Glass UI. Use when adopting Liquid Glass or checking its correctness, performance, and design fit.

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。

Implement, review, or improve SwiftUI features using the iOS 26+ Liquid Glass API. Use when asked to adopt Liquid Glass in new SwiftUI UI, refactor an existing feature to Liquid Glass, or review Liquid Glass usage for correctness, performance, and design alignment.

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。

Get paid from day one. Payments, subscriptions, billing portal, webhooks, metered billing, Stripe Connect. The complete guide to implementing Stripe correctly, including all the edge cases that will bite you at 3am. This isn't just API calls - it's the full payment system: handling failures, managing subscriptions, dealing with dunning, and keeping revenue flowing. Use when: stripe, payments, subscription, billing, checkout.

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An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

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Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

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Orchestrates design workflows by routing work through brainstorming, multi-agent review, and execution readiness in the correct order. Prevents premature implementation, skipped validation, and unreviewed high-risk designs.

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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

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You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.

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。

Zero-Knowledge Proofs (ZKPs) allow a prover to demonstrate Bilgi: a secret (such as a password or private key) without revealing the secret itself. bu skill implements the Schnorr identificati

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。

Integrate AFL++ coverage-guided fuzz testing into CI/CD pipelines to discover memory corruption, input handling, and logic vulnerabilities in C/C++ and compiled 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。