「code analysis」の検索結果

ESLint is the standard linting tool for JavaScript and TypeScript projects. It statically analyzes your code to find problems, enforce coding conventions, and catch bugs before they reach production. ESLint 9 introduced the flat config format, replacing the legacy `.eslintrc` system with a simpler, more composable `eslint.config.js` file. With a rich plugin ecosystem covering React, TypeScript, accessibility, and import ordering, ESLint is the backbone of code quality in modern web development.

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Generate behavioral/feature documentation for web apps using code-first analysis. Reads source code via tree-sitting to produce _FEATURES.md, with optional visual verification via browser automation. Companion to tree-sitting. Use when documenting app behavior, creating feature inventories, generating behavioral ground truth for agents, or before modifying UI code. Triggers on "map features", "document app behavior", "feature inventory", "what does this app do".

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Use when indexing a code repository into Cognee's code graph or querying it (callers, impact analysis, paths, endpoints) from Antigravity.

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Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

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Expert guide for multi-pass autonomous AI code self-review — syntax validation, logic correctness, architectural conformance, security audit, and performance analysis without external tooling — enabling the agent to catch its own errors before presenting code / Panduan ahli review kode otonom multi-pass oleh AI — validasi sintaks, kebenaran logika, konformitas arsitektur, audit keamanan, dan analisis performa tanpa tooling eksternal — memungkinkan agen menangkap errornya sendiri sebelum menampilkan kode.

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Use for architecture review, dependency graphs, coupling analysis, or circular dependency detection. Establishes project module namespaces and traces imports plus Claude plugin hook, skill, script, PEP 723, and MCP boundaries. Emits partitioned Mermaid graphs with cycle, coupling, and ownership signals; verifies contract and consequence before assigning severity. Registers diagrams as codebase-analysis artifacts.

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Local codebase analysis research angle — derives behavioral contracts, coding conventions, SKILL.md flow insertion points, and agent data availability maps from actual source files. Use when the blocking question is answered by reading the repository: what does this function actually do, what pattern does the codebase use for X, where in this workflow does a new step go, or what data does this agent already have.

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Analyze code coverage and CRAP (Change Risk Anti-Patterns) scores to identify high-risk code. Use OpenCover format with ReportGenerator for Risk Hotspots showing cyclomatic complexity and untested code paths.

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Semantic code understanding with IDE-like symbol operations. Use when: (1) Large codebase analysis (>50 files), (2) Symbol-level operations (find, rename, refactor), (3) Cross-file reference tracking, (4) Project memory and session persistence, (5) Multi-language semantic navigation. Triggers: "find symbol", "rename function", "find references", "symbol overview", "project memory". IMPORTANT: Prioritize Serena's symbolic tools over file-based grep/read for code exploration.

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Analyze sales-call transcripts to extract why deals are won and lost across six dimensions — product, messaging, GTM/sales, pricing, competition, and customer context. Produces aggregated patterns with verbatim buyer quotes, frequencies, and recommendations. Writes to marketing/win-loss/win-loss.md as the evidence base under positioning, messaging, and ICP. Triggers - "win loss analysis", "why are we losing deals", "why do we win", "analyze sales calls", "churn analysis", "deal review"

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Intelligent financial management skill for Claude Code that provides comprehensive PocketSmith API integration with AI-powered analysis, transaction categorization, rule management, tax intelligence, and scenario planning. Use when working with PocketSmith data for (1) Transaction categorization and rule management, (2) Financial analysis and reporting, (3) Australian tax compliance (ATO) and deduction tracking, (4) Scenario analysis and forecasting, (5) PocketSmith setup health checks, (6) Budget optimization and spending insights.

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Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.

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Develop evidence-backed structural and architectural security hardening proposals from vulnerability disclosures, supplied findings, incident or assessment documents, source code, or a completed Codex Security scan. Use when a user asks for systemic improvements, alternatives beyond per-finding patches, before-and-after security architecture views, engineering tradeoff analysis, or an implementation-ready plan for a selected hardening option. Also use automatically after a Codex Security scan with reportable findings when the top-level scan workflow requests final-report hardening guidance.

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Produces a concise, structured analysis report of a codebase: dependencies, workflow, and context.

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Deep exploration and synthesis workflow using agent teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".

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Deep exploration and synthesis workflow using Agent Teams with dynamic planning and hub-and-spoke coordination. Use when asked for "deep analysis", "deep understanding", "analyze codebase", "explore and analyze", or "investigate codebase".

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。

Produce a structured codebase analysis report with architecture overview, critical files, patterns, and actionable recommendations.

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Run CodeQL and Semgrep static analysis with SARIF output for vulnerability detection, code quality assessment, and security compliance scanning across multiple languages.

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Programmatic code style validation using AST analysis. Complements (not replaces) code-style rules by providing automated checking and instant feedback.

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5つの専門エージェントがディスカッション型で競合のビジネスモデル・機能・価格・弱点を網羅的に分析し、 差別化ポイントと参入戦略を導出するスキル。 エージェント同士が対話・批判し合いながら調査し、ca-data-critic がライブモデレーターとして矛盾を検出・収束を判定する。 Phase 1 で競合プロファイル・レビュー・機能をディスカッション型で調査し、 Phase 2 でポジショニング戦略家が統合。 Gate 1（中間合意形成）→ Gate 2（最終検証 + Independent Validation + Devil's Advocate）の2段階ゲートで品質を担保。 Use when: 競合を分析したい、差別化ポイントを見つけたい、 市場のプレイヤーを調べたい、ポジショニングを考えたい。 Triggers: "競合分析", "competitive analysis", "競合調査", "差別化", "ポジショニング", "competitor", "競合", "市場プレイヤー", "ライバル調査", "competitive landscape"

Statically enforce a privacy/redaction boundary on an artifact tree before publication, the way an AST checker enforces a code contract. Builds a two-tier gate — a shape-based deny-list (regex over identifier shapes) plus a structure-aware tier that parses JSON/HTML/SVG/Markdown/code and asserts sensitive content cannot appear in meaningful positions — with a strict exit-code contract, label-only output that never echoes the patterns themselves, and false-positive narrowing instead of suppressions. Use when gating a public mirror, a disclosure draft, a wire-capture export, or any artifact that must pass a leak check before it ships, and when a plain line-grep is too coarse to trust.

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Create a Quarto document for reproducible reports, presentations, or websites. Covers YAML configuration, code chunk options, output formats, cross-references, and rendering. Use when creating a reproducible analysis report, building a presentation with embedded code, generating HTML, PDF, or Word documents from code, or migrating an existing R Markdown document to Quarto.

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Analyze an arbitrary codebase to auto-detect workflows, data pipelines, and file dependencies using putior's put_auto() engine. Produces an annotation plan that maps detected I/O patterns to source files across 30+ supported languages with 902 auto-detection patterns. Use when onboarding onto an unfamiliar codebase to understand data flow, starting putior integration in a project without existing annotations, auditing a project's data pipeline before documentation, or preparing an annotation plan before running annotate-source-files.

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Fix silent failures when parsing Grafema semantic IDs that are in URI format instead of legacy arrow format. Use when: (1) code splits semantic IDs by "->" but gets the whole string back because IDs are grafema:// URIs, (2) file path extraction from semantic IDs returns empty or wrong values, (3) derived edges (DEPENDS_ON, etc.) produce 0 results despite source edges existing, (4) any code that processes semantic IDs after the analysis pipeline's to_uri_format() has run. The grafema:// URI format uses # fragments with percent-encoded characters instead of -> separators.

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。