'code analysis' 검색 결과

Review a research paper alongside its Stata, R, or Python code to check reproducibility, code quality, and agreement with the main findings. Receive a Markdown report with supporting evidence, uncertainties, strengths, and prioritized next steps.

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• Checking reproducibility before sharing research code
• Matching main tables and figures to analysis scripts
• Reviewing dependencies, file paths, and execution order

Prepares Claude Code or OpenAI Codex for in-depth research with hyperresearch, then hands the request to a structured workflow for research reports, literature reviews, and analysis using multiple sources with verified citations.

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• Preparing a research report with verified citations
• Reviewing literature on a specific topic
• Comparing evidence from multiple sources

Analyze git history to map contributors to security-sensitive files, identify reliance on too few people, and find stale areas. Export CSV and JSON results for ownership reviews, graph exploration, and comparison with CODEOWNERS.

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• Find stale security-sensitive code with few maintainers
• Check reliance on individual authentication or cryptography maintainers
• Compare CODEOWNERS with actual contribution history

Identify and analyze unused or redundant code including unused functions/methods, unused variables/imports, unreachable code, and redundant conditions. Use when cleaning up codebases, improving maintainability, reducing technical debt, or conducting code quality audits. Analyzes Python code using AST analysis and produces markdown reports listing dead code locations with line numbers, severity ratings, and recommendations. Triggers when users ask to find dead code, remove unused code, identify unused imports, find unreachable code, or clean up redundant logic.

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This skill should be used to spawn specialized OpenAI Codex CLI subagents for code review, debugging, architecture analysis, security audits, refactoring, documentation, comparative evidence adjudication, and autonomous /goal runs. It adds each persona through process-local developer instructions while preserving the project's AGENTS.md chain, including during parallel launches. Supports GPT-6-Astra across low, medium, high, xhigh, max, and ultra reasoning with default or priority service tiers, plus GPT-5.6 and GPT-5.5 models. Triggers on 'delegate to Codex', 'Codex Astra', 'Astra subagent', 'Codex subagent', 'code review agent', 'security audit', 'refactor with Codex', 'goal run', 'autonomous goal', 'have Codex weigh this'.

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コードベースのアーキテクチャを 6 つの専門エージェントチーム（Claude 5体 + Codex 1体）で 多角的に分析し、リスク・デグレしやすい箇所・改善ポイントを洗い出すスキル。 パフォーマンス、スケーラビリティ、信頼性、セキュリティ、運用、DX、データ整合性、依存関係の 8 観点を 5 グループに分担し、さらに Codex がクロスカットレビュアーとして横断分析する。 エージェント同士が反論・補足・自由な議論を行い、合意形成した結果をレポートとして出力する。 どのプロジェクトでも使える汎用スキル。 Use when: アーキテクチャを分析したい、リファクタリング前にリスクを把握したい、 デグレしやすい箇所を知りたい、コードの健全性を確認したい、大きめの実装前に注意点を洗い出したい、 技術的負債を可視化したい、コードベースの品質を監査したい。 Triggers: "arch review", "アーキテクチャ分析", "アーキテクチャレビュー", "リスク分析", "コード監査", "codebase audit", "技術的負債", "デグレしやすい", "architecture analysis", "risk analysis", "コードの健全性", "品質チェック", "実装前の確認"

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Use when working with Codescene — codeScene behavioral code analysis including code health scoring, hotspot detection, change coupling analysis, and PR integration for code quality gates. Use when configuring CodeScene for codebase visualization, technical debt prioritization, and automated code review feedback.

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Audit a codebase across nine areas, including architecture, tests, security, and documentation. Produces TECH_DEBT_AUDIT.md with source locations, severity ratings, estimated repair effort, and prioritized recommendations to help plan maintenance work.

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• Assessing a codebase before maintenance
• Prioritizing technical debt fixes
• Checking architecture and code consistency

Review code for security, performance, quality, and maintainability. Get findings organized by severity, with file locations, explanations of impact, and example fixes, plus code metrics and complexity comparisons for refactoring reviews.

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• Reviewing a pull request before merging
• Checking code for security risks
• Finding performance improvement opportunities

Investigate phishing emails that hide malicious links in QR codes. Decode destinations, analyze suspicious URLs, configure email detection rules, and plan mobile protections and awareness training for people who scan codes on their phones.

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• Investigating suspicious emails containing QR codes
• Configuring QR detection for email images and PDF attachments
• Protecting phones from malicious QR destinations

Read existing code to reconstruct system specifications, including architecture, data flows, APIs, and business rules. Produces documentation tied to code locations, separating observed behavior from inferences and questions that need clarification.

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• Understand an undocumented legacy system
• Document existing APIs and business rules
• Map module dependencies and data flows

Find other occurrences of a known bug across a codebase by tracing its root cause. Gradually broaden search patterns, assess matching candidates, assign severity, and document findings with rules to help prevent recurrence.

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• Finding similar issues after discovering a vulnerability
• Building CodeQL or Semgrep queries from a known bug
• Checking whether similar code shares the same root cause

Prepare a codebase for an external security review by defining goals, running static checks, improving test coverage, and removing unused code. Assemble the review scope, verified build instructions, diagrams, and documentation into a package for auditors.

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• Preparing a codebase for an external security audit
• Organizing fixes and missing tests before review
• Defining the review scope and a stable code version

Turn a large codebase spanning multiple repositories or services into a structured knowledge base. It documents architecture, business rules, and dependencies, links conclusions to source lines, labels confidence in relationships, and supports incremental updates.

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• Documenting architecture across multiple repositories
• Tracing service dependencies and business rules
• Updating a knowledge base after code changes

Analyze Git history to find frequently changed files, understand contributor involvement, and detect exposed credentials. Use the results to prioritize code reviews, choose reviewers, and identify areas that may need restructuring.

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• Checking commit history for exposed credentials before deployment
• Prioritizing reviews of frequently changed files
• Choosing reviewers based on contributor history

Use the open-source CodeQL ecosystem for .NET security analysis. Use when a repo needs CodeQL query packs, CLI-based analysis on open source codebases, or GitHub Action setup with explicit licensing caveats. USE FOR: the repo uses or wants CodeQL for .NET security analysis; GitHub code scanning is part of the CI plan. DO NOT USE FOR: teams that need a tool with no private-repo licensing caveat. 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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Restructure existing code without changing its behavior, through a test-gated refactoring loop: a named target, a green suite over that target before any edit, a planned sequence of small named refactorings, and the full suite re-run after every step. Use when the user wants to refactor, restructure, clean up, simplify, or improve the design of existing code, or to apply refactoring recommendations from a code-review or architectural-analysis report. This skill changes code; it does not review code (use code-review), assess architecture (use architectural-analysis), plan a multi-module restructure before executing it (use plan-a-change), map domain boundaries before restructuring along them (use ddd-analysis), or build new behavior test-first (use tdd). Do not use it on code inside an active tdd loop; the refactor step of tdd owns that cleanup. Runs its planned sequence to completion without pausing for review; to review each named refactoring as it lands, use pairing.

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Produces a human-readable, progressive-disclosure overview of unfamiliar code or a pull request's changes — why it exists (the real problem it solves or goal it serves for the business or a user), and from there what it does, how it flows, and where to start — so you can get up to speed before working on or reviewing it. Use when you want to understand, get oriented in, make sense of, explain, or get up to speed on a chunk of code, a file, a directory, a symbol, or a PR's changes. Writes the overview to a scratch file and changes no code. Does not review code quality or raise findings — use code-review for auditing changes or post-code-review-to-pr for posting them. Does not produce durable feature or system documentation — use project-documentation. Does not assess architecture or structural risk — use architectural-analysis. Does not diagnose bugs or root-cause failures — use investigate. Does not pace a person through the code one step at a time in conversation — use code-walkthrough.

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Delegates coding tasks to Codex CLI for prototyping, debugging, and code review. Use when: (1) Backend/logic implementation, (2) Algorithm design and optimization, (3) Bug analysis and debugging, (4) API/database code generation, (5) Code quality review and refactoring. Triggers: "implement algorithm", "debug", "analyze code", "backend task", "API implementation", "optimize performance", "refactor", "generate prototype", "code review". IMPORTANT: Default sandbox="read-only" (analysis/review/patch generation); use sandbox="workspace-write" only when codex must run tests/builds to verify. Always request unified diff patches only. Supports multi-turn sessions via SESSION_ID.

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Comprehensive guide for setting up and configuring CodeQL code scanning via GitHub Actions workflows and the CodeQL CLI. This skill should be used when users need help with code scanning configuration, CodeQL workflow files, CodeQL CLI commands, SARIF output, security analysis setup, or troubleshooting CodeQL analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive guide for setting up and configuring CodeQL code scanning via GitHub Actions workflows and the CodeQL CLI. This skill should be used when users need help with code scanning configuration, CodeQL workflow files, CodeQL CLI commands, SARIF output, security analysis setup, or troubleshooting CodeQL analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive guide for setting up and configuring CodeQL code scanning via GitHub Actions workflows and the CodeQL CLI. This skill should be used when users need help with code scanning configuration, CodeQL workflow files, CodeQL CLI commands, SARIF output, security analysis setup, or troubleshooting CodeQL analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Complete guide for code coverage analysis. Use when you need to analyze code coverage, generate coverage reports, or configure CI/CD coverage checks. Covers Coverlet configuration, report generation, metric interpretation, and cyclomatic complexity integration. Includes Fine Code Coverage, VS Code built-in tools, and best practices. Keywords: code coverage, coverage analysis, coverage report, Coverlet, Fine Code Coverage, dotnet-coverage, ReportGenerator, line coverage, branch coverage, cyclomatic complexity, runsettings, cobertura

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Improve existing code without changing its behavior. This skill guides analysis, test checks, identification of design problems, and a phased refactoring plan, then applies small changes with testing and review.

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• Breaking long functions into smaller parts
• Consolidating duplicated logic
• Planning cleanup of legacy code