'complexity' 검색 결과

Agents should invoke this skill for code reviews, linting/formatting setup, maintainability checks, complexity concerns, warning cleanup, coding standards, or quality gates in Rust, TypeScript, Python, shell, and mixed repos.

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Code simplification suggestions — clarity, unnecessary complexity, redundant abstractions. Advisory only.

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Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.

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Dynamic model selection based on task complexity scoring. Replaces static model mappings with a weighted signal system that picks Opus, Sonnet, or Haiku-class speed per task. Works with agent-assignment-matrix.md.

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Simplification audit or authorized codebase simplification whose stated objective is to remove accidental complexity. Use for evidence-backed deletion or consolidation of dead code, duplicate state, redundant APIs or layers, ownerless abstractions, obsolete compatibility or design records, and over-engineering in any language; also use for 代码简化 or 熵回收. Do not use for general code review, onboarding, style-only refactoring, or performance tuning.

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Kimi Browser Extension（Kimi 浏览器扩展，原 Kimi WebBridge）lets AI control the user's real browser — navigate, click, type, read, screenshot, and interact with any website using the user's actual login sessions. Use this skill whenever the user wants to interact with websites, automate browser tasks, scrape web content, or perform any action requiring a real browser. Also use when the user mentions "browser", "webpage", "open URL", "screenshot", asks to read/interact with any website, or wants a reusable CLI tool or script that automates a specific website. Use even for simple-sounding browser requests — the daemon handles all complexity.

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Use the open-source free `cloc` tool for line-count, language-mix, and diff statistics in .NET repositories. USE FOR: repeatable LOC reports; language-mix or branch-diff statistics; codebase size and solution-composition measurements. DO NOT USE FOR: judging developer productivity from raw LOC; replacing behavioral verification, architecture review, or complexity analysis. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Calculates CRAP (Change Risk Anti-Patterns) for a named .NET method, class, or file. USE FOR: explicit CRAP calculation or coverage-and-complexity risk within that named target, including which tests to prioritize. DO NOT USE FOR: project-wide coverage/CRAP, plateaus, or project-wide blockers/priorities (coverage-analysis); behavioral/pseudo-mutation gaps (test-gap-analysis); writing tests; test runs without CRAP context.

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Analyze task complexity and route to a mode by artifact: direct fix for clear-scope changes, or a plan file when the approach needs to be written down. Use when the user asks to "turboplan", "run turboplan", "plan this task", "turbo plan mode", "plan and implement", or "use turboplan instead of plan mode".

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Assess project-wide structural technical debt: complexity hotspots, deprecated API usage, duplication clusters, architecture rot, and low-value tests. Ranks findings by impact and refactor effort into a report at .turbo/technical-debt.md. Use when the user asks to "assess technical debt", "find technical debt", "review technical debt", "what should we refactor", "find refactoring candidates", "where is the code rot", "what's our worst code", "find low-value tests", or "which tests can we delete". Analysis-only — does not modify code.

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Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.

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Design, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.

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Medicinal chemistry filters for compound triage. Drug-likeness rules (Lipinski Ro5, Veber, Oprea, CNS, leadlike, REOS, Golden Triangle, Ro3), structural alerts (PAINS, NIBR, Lilly Demerits), chemical group detectors, complexity metrics, and filter composition query language. Built on RDKit/datamol. For hit-to-lead filtering, library design, ADMET pre-screening. For molecular I/O use rdkit-cheminformatics or datamol.

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Python toolkit for neurophysiological signal processing: ECG (HR, HRV, R-peaks), EEG (complexity, PSD), EMG (activation onset), EDA/GSR (SCR decomposition), PPG, and RSP. Includes synthetic signal simulation. Alternatives: BioSPPy (less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP).

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Top-down vs bottom-up quota models. Historical attainment, market growth assumptions, ramp periods, territory complexity.

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Detects codebase bloat via dead code, duplication, complexity, and doc bloat scans. Use when codebase feels large or before a release.

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Applies KISS, YAGNI, and SOLID principles for clean code with reduced complexity. Use when refactoring or reviewing code for over-engineering.

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Compares PIA/DPIA methodologies: CNIL PIA tool, ICO DPIA template, NIST Privacy Framework, and ISO 29134. Provides methodology selection criteria based on regulatory jurisdiction, organisation maturity, processing complexity, and resource availability. Covers regulatory acceptance, tool features, and cross-methodology mapping. Keywords: PIA methodology, CNIL, ICO, NIST Privacy Framework, ISO 29134, DPIA comparison, assessment.

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Your personal DSA & LeetCode mentor. Use for problem explanations, progressive hints, code reviews, mock interviews, pattern recognition, complexity analysis, and custom problem generation. Automatically adapts to your learning style and request type.

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Perform the repository's risk-tiered independent final review before implementation completion. Use only when explicitly invoked or when repository instructions require it after behavior-impacting implementation work; audit the complete task diff, supported contracts, lifecycle and security boundaries, complexity, and tests before final verification.

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Analyzes a repository and its test suite to generate customized mutation operators tailored to the project. Use this skill when setting up mutation testing, improving test quality, or generating project-specific mutants. Considers language constructs, business logic patterns, API calls, data types, and code complexity to produce effective mutation operators that maximize test sensitivity while avoiding trivial or equivalent mutations. Generates prioritized operator sets (high/medium/low value) with impact summaries. Triggers when users ask to generate mutation operators, customize mutation testing, analyze code for mutation opportunities, or create project-specific mutants.

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Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with actionable refactoring suggestions.

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Analyzes and optimizes code for better performance, memory usage, and efficiency. Use when code is slow, memory-intensive, or inefficient. Supports Python and Java optimization including execution speed improvements, memory reduction, database query optimization, and I/O efficiency. Provides before/after examples with detailed explanations of why optimizations work, complexity analysis, and measurable performance improvements.

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Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean.

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