'systematic' 검색 결과

Per-toolkit hardening audit loop for NV oOS Pro tools. Systematically scans each addons/pro/includes/tools/<toolkit> (and its plugins/nvoos-content-graph-pro mirror) for the recurring production failure classes — unguarded shell calls that fatal on exec-disabled hosts, calls to nonexistent client methods, string-assuming parsing of provider responses that may be arrays, provider enum/service mismatches, metric/source column drift that silently returns 0, secondary-table denominator gaps, the Chart.js fixed-height loop, misleading native-source errors, and silent import truncation that reports partial writes as success — then fixes, tests (Docker PHPUnit), and validates (phpcs both standards) before moving to the next toolkit. Use when asked to audit a toolkit, review the X tools for similar issues, do the same for the other toolkits, or continue the toolkit audit loop. The first case study (document-generation) found 3 fatal bugs + 1 broken regex; the food-beverage pass added classes 5–9.

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Systematically rewrites AI-generated or AI-sounding text on web pages, documents, or pasted content to sound human. Use this skill whenever a user asks to "humanize", "de-AI", "make this sound less like AI", "rewrite to avoid AI detection", or "clean up AI writing". Also trigger when a user pastes text and asks to make it sound more natural, more human, or less robotic. The skill applies a strict deterministic rule pipeline — NOT creative rewriting — making it safe for agentic/automated use.

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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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Enable nullable reference types in a C# project and systematically resolve all warnings. USE FOR: adopting NRTs in existing codebases, file-by-file or project-wide migration, fixing CS8602/CS8618/CS86xx warnings, annotating APIs for nullability, cleaning up null-forgiving operators, upgrading dependencies with new nullable annotations. DO NOT USE FOR: projects already fully migrated with zero warnings (unless auditing suppressions), fixing a handful of nullable warnings in code that already has NRTs enabled, suppressing warnings without fixing them, C# 7.3 or earlier projects. INVOKES: Get-NullableReadiness.ps1 scanner script.

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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

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Make .NET projects compatible with Native AOT and trimming by systematically resolving IL trim/AOT analyzer warnings. USE FOR: making projects AOT-compatible, fixing trimming warnings, resolving IL warnings (IL2026, IL2070, IL2067, IL2072, IL3050), adding DynamicallyAccessedMembers annotations, enabling IsAotCompatible. DO NOT USE FOR: publishing native AOT binaries, optimizing binary size, replacing reflection-heavy libraries with alternatives. INVOKES: no tools — pure knowledge skill.

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.

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[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res

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Transform debugging from frustrating guesswork into systematic problem-solving with proven strategies, powerful tools, and methodical approaches.

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Establish build performance baselines and apply systematic optimization techniques. Only activate in MSBuild/.NET build context. USE FOR: diagnosing slow builds, establishing before/after measurements (cold, warm, no-op scenarios), applying optimization strategies like MSBuild Server, static graph builds, artifacts output, and dependency graph trimming. Start here before diving into build-perf-diagnostics, incremental-build, or build-parallelism. DO NOT USE FOR: non-MSBuild build systems, detailed bottleneck analysis (use build-perf-diagnostics after baselining).

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Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.

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Comprehensive investigation of complex multi-domain narratives (technology, theology, policy, finance). Systematically decouples factual claims from speculation through iterative verification, multi-source comparison, and contradiction resolution. Use for investigations requiring institutional knowledge, historical context, and handling of conflicting incentives.

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Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.

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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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Systematic ambiguity resolution through tiered information gathering. Use when facing unclear requirements, unknown context, uncertain implementation choices, or any situation where guessing would be risky.

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Systematic methodology for verifying that IA, BE, and FE specification layers are mutually consistent. Covers coverage matrices, field mapping checks, error code propagation, access control consistency, and data contract drift detection.

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Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.

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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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Systematically trace bugs backward through call stack to find original trigger

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Exploratory QA — web apps, bugs, evidence, reports, systematic testing

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Systematic design quality evaluation. Hierarchy, type, color, space, craft, system. Use when evaluating whether a design is ready to ship, running quality audits, or setting quality standards.

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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