'complexity' 검색 결과

Evaluate Jira ticket complexity + inconsistencies via the jira-evaluator agent. Read-only. Triggers: 'evaluate KEY', 'analyze ticket complexity for KEY', 'check ticket quality for KEY'. NOT for estimation (mk:jira-estimator); NOT for full RCA (mk:jira-analyst).

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Two-phase code quality skill: (1) review codebase for structural smells — file LOC, method LOC, accidental complexity, test readability — producing a structured report, then (2) fix all smells via provable refactorings with ACN micro commits and green-bar-at-all-times discipline. Use this skill when the user says 'sweep', 'code review', 'find code smells', 'check method lengths', 'review for complexity', 'tidy up the codebase', 'refactor smells', or asks about long methods, test readability, or accidental complexity. Also trigger when users ask to 'clean up' or 'improve code quality' across multiple files.

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Use when the operator asks for an authoring-time complexity hint or a complexity guide preview before commit.

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호출자가 익혀야 할 사용법은 줄이고 복잡한 처리는 내부에 모으는 모듈 설계를 돕습니다. 공통 용어와 판단 기준으로 사용 편의성과 테스트 용이성을 검토합니다.

• 모듈 호출 방법 간소화
• 흩어진 복잡한 처리 통합
• 구현을 교체할 위치 검토

LLM API 비용 관리를 위해 작업 복잡도에 따른 모델 선택, 예산 기록, 일시적 오류 재시도, 공통 프롬프트 캐시를 결합하는 설계 예시를 제공합니다.

• LLM 앱의 API 비용을 검토합니다
• 일괄 처리 난이도에 따라 모델을 선택합니다
• 예산 대비 누적 비용을 확인합니다

코드 작성, 검토, 수정 시 전제 확인, 최소한의 구현, 변경 범위 제한, 테스트 검증을 권장해 AI가 자주 일으키는 구현 실수를 줄이는 지침입니다.

• 구현 전에 전제와 모호한 점 정리
• 버그 수정 범위 제한
• 지나치게 복잡한 코드 검토

코드 변경과 호출부를 읽고 버그, 보안, 부하, 테스트, 속도, 불필요한 구현을 점검합니다. 구체적인 문제 사례와 최소 수정안, 미해결 시 영향을 우선순위에 따라 보고합니다.

• PR이나 최근 커밋을 검토합니다
• 보안과 데이터 손실 위험을 확인합니다
• 예상 부하의 문제와 테스트 누락을 확인합니다

동작을 바꾸지 않고 깊은 조건 분기, 긴 함수, 중복, 모호한 이름을 정리합니다. 기존 규칙과 테스트를 확인하고 가독성을 기준으로 작은 변경을 이어 갑니다.

• 작동하는 기능의 코드 가독성을 높입니다
• 리뷰에서 지적된 복잡성을 정리합니다
• 깊은 조건 분기와 긴 함수를 정리합니다

Audit a codebase for unnecessary complexity, including dead code, duplicated state, redundant interfaces, and obsolete compatibility paths. Receive evidence-backed simplification candidates, or authorize changes that remove justified targets and validate the behavior that must remain.

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• Finding code that can be safely removed
• Consolidating duplicated state or APIs
• Reviewing obsolete compatibility paths and design records

Clean up AI-generated code in branch changes or selected files while preserving behavior. Establish passing regression tests first, then remove unnecessary code and complexity, run project quality checks, and report both changes and skipped issues.

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• Cleaning up recent AI-generated changes
• Removing redundant comments and dead code
• Simplifying duplication and excessive complexity

Have agents with different roles challenge a proposed approach, exposing weak assumptions, missed interactions, and unnecessary complexity. Surviving findings go to a dedicated planning agent, which produces sequenced tasks with explicit success criteria.

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• Checking assumptions before committing to a plan
• Finding integration risks and overlooked edge cases
• Challenging unnecessary complexity in a design

Helps build and review NeuroKit2 research workflows for physiological recordings such as ECG, respiration, and skin conductance. Covers preprocessing, time alignment, event analysis, heart rate variability, and complexity measures, while recording methods and checking data quality.

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• Inspect physiological recordings before preprocessing
• Analyze ECG and heart rate variability for research
• Plan event windows and baseline correction

Build and evaluate Mamba models for long sequences using state-space models, which summarize past inputs in an internal state. Covers setup, pretrained text generation, differences between Mamba-1 and Mamba-2, and speed comparisons with Transformers.

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• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations
• Benchmark generation speed against Transformers

Analyze pull requests and source code across multiple programming languages to identify complexity, security risks, and structural problems. Produce prioritized findings and structured review reports to guide code review decisions.

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• Prioritizing files for a pull request review
• Finding structural problems in source code
• Checking changes for exposed secrets and injection risks

Review code changes for unnecessary complexity, including unused flexibility, avoidable dependencies, and abstractions with little purpose. Receive concise findings that name what to remove, what can replace it, and how many lines could be cut.

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• Check code changes for over-engineering
• Find code replaceable with standard library features
• Identify unnecessary dependencies and abstractions

Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.

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• Explore models for long sequences or streaming applications
• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations

Group bug reports by their underlying cause and plan shared fixes. This skill guides reproduction, checks for unnecessary complexity, and requires named test evidence before treating related issues as resolved.

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• Grouping accumulated bug reports by root cause
• Planning one fix for several related issues
• Checking test evidence before closing issues

Compare code structure across two commits, tags, or directories to identify security-relevant changes. Produces a report on new attack paths, privilege boundaries, complexity, and how widely a change could affect other functions.

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• Compare code structure before a release
• Investigate new attack paths after code changes
• Track untrusted data flows across a refactor

Plan and implement features with a workflow that adapts to their complexity. It connects testable requirements to tasks, tests, and small commits, then checks the finished work against the specification through an independent verification pass.

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• Define testable requirements for a new feature
• Implement a large feature through small, verified tasks
• Check whether an implementation meets its specification

Calculate change risk for a specific .NET method, class, or file by combining code complexity with measured test coverage. Get ranked results and recommendations on where to add tests or simplify logic.

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• Calculate risk scores for a specific method
• Prioritize tests within a class
• Find complex, undertested methods in a file

Interpret .NET coverage reports, which show code exercised by tests, to explain missing coverage and stalled progress. Calculate the gains needed to reach a target and, when requested, combine coverage with complexity to rank risk.

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• Explain Cobertura reports and partially covered conditions
• Find the code keeping coverage from improving
• Calculate line gains needed to reach a coverage target

Audit GraphQL APIs for exposed schemas, missing access checks, injection risks, and weak query limits. Combine automated tools with targeted checks to investigate data exposure, unauthorized operations, and resource exhaustion, then document findings with reproducible evidence.

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• Discover GraphQL schemas and hidden fields
• Check object and field access permissions
• Test batching, query depth, and complexity limits

Keep code, tests, documentation, configuration, and other changes as tight, clear, performant, and minimal as possible without weakening behavior. Use during implementation to prevent unnecessary complexity, after behavior is proven to consolidate working changes, before handoff for a full branch audit, and when asked to simplify, tighten, clean up, reduce verbosity, remove complexity, or minimize existing work.

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AI DevKit · Analyze and simplify existing implementations to reduce complexity, improve maintainability, and enhance scalability. Use when users ask to simplify code, reduce complexity, refactor for readability, clean up implementations, improve maintainability, reduce technical debt, or make code easier to understand.

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