Claude Code Codex FAQ & Answers

Claude Code: 'Anthropic's official CLI for Claude, interactive command-line tool helping with software engineering tasks.' Core features: (1) File operations: read, write, edit files. (2) Code execution: run bash commands, scripts. (3) Web search: research and gather information. (4) MCP integration: 'connects to hundreds of external tools and data sources through Model Context Protocol (MCP), open-source standard for AI-tool integrations.' (5) Subagents: specialized agents for specific tasks (web fetching, file reading). (6) Plugins (2025): 'custom collections of slash commands, agents, MCP servers, and hooks that install with single command.' (7) Context management: maintains conversation context. Use for: coding assistance, automation, research, debugging. 'Functions as both MCP server and client, can connect to any number of MCP servers.' Essential AI coding assistant.

MCP (Model Context Protocol): 'open-source standard for AI-tool integrations, enables Claude Code to connect to external tools and data sources.' How it works: (1) Claude Code acts as MCP client. (2) Connects to MCP servers (Sentry, Figma, Stripe, etc.). (3) Servers expose tools/resources to Claude. (4) Claude can invoke tools, access data. 'Claude Code can connect to hundreds of external tools through MCP.' Configuration: .mcp.json files (local, project, or user scope). Available servers: Sentry, Jam, Asana, Atlassian, HubSpot, PayPal, Plaid, Square, Stripe, Figma, GitHub, databases, file systems. Benefits: standardized integration, extensibility, community ecosystem. Use for: connect Claude to your tools, custom data access, workflow automation. 'Code execution with MCP: building more efficient AI agents.' Essential extensibility mechanism.

Claude Code plugins (public beta, October 2025): 'custom collections of slash commands, agents, MCP servers, and hooks that install with single command.' Components: (1) Slash commands: custom shortcuts for common tasks. (2) Subagents: purpose-built agents for specialized tasks. (3) MCP servers: tool integrations. (4) Hooks: customize Claude Code behavior (on startup, on tool call, etc.). Installation: single command setup, configuration-based. Benefits: (1) Reusable configurations. (2) Share workflows with team. (3) Quick environment setup. (4) Consistent behavior across projects. Use cases: project-specific workflows, team standards, integration bundles, custom tooling. Example: plugin with test runner commands + CI/CD integration + code review agent. Essential for extensibility and team collaboration.

Subagents (generally available July 2025): 'purpose-built agents for specialized tasks, default feature in Claude Code executing tasks autonomously with isolated context.' Types: (1) Web fetching agents: research, gather documentation. (2) File reading agents: search codebases efficiently. (3) Custom agents: via plugins for specific workflows, defined as Markdown files in .claude/agents/*.md. How they work: (1) Main Claude delegates task to subagent. (2) Subagent executes with specialized tools in separate context window. (3) Returns results to main Claude. (4) Main Claude continues with context. Benefits: (1) Specialization: agents optimized for specific tasks. (2) Efficiency: parallel execution possible. (3) Context isolation: prevents context pollution, focused sub-contexts. (4) Extensibility: create custom agents. 'Claude Agent SDK inherits same agent that powers Claude Code, enabling production-ready agents that safely interact with files, run code, call external APIs, keep long-lived context.' Use for: complex multi-step tasks, specialized operations, workflow automation. Essential agent architecture.

Claude Agent SDK (formerly Claude Code SDK): 'framework for building production-ready agents using same agent that powers Claude Code.' Available in Python (github.com/anthropics/claude-agent-sdk-python) and TypeScript (github.com/anthropics/claude-agent-sdk-typescript). Capabilities: (1) File interaction: read, write, edit files safely. (2) Code execution: run commands, scripts. (3) External API calls: integrate third-party services. (4) Long-lived context: maintain state across interactions with automatic compaction. (5) Tool use: extend with custom tools, MCP extensibility. Architecture: agent loop, tool calling, context management. Use cases: (1) Custom coding assistants. (2) Automation agents. (3) Development workflows. (4) CI/CD integration. (5) Testing automation. 'Production-ready agents that safely interact with files, run code, call external APIs.' Benefits: battle-tested (from Claude Code), safety mechanisms, built-in error handling, session management, extensible, maintained by Anthropic. Open source: community contributions. Essential for building custom agents.

OpenAI Codex: 'fine-tuned, code-specialized descendant of GPT-3 introduced in August 2021, powered early experiences like GitHub Copilot's technical preview.' Capabilities: understanding and generating code, trained on public GitHub repositories. 'Capable of understanding and generating code in the IDE.' Original deprecation: 'OpenAI deprecated original Codex models in March 2023, directed developers to modern chat/completion models.' Reason: newer models (GPT-3.5, GPT-4) superior for code. GitHub Copilot transition: 'transitioned off Codex-2021 model and adopted OpenAI's more advanced GPT-4 model as part of Copilot X upgrade.' 2025 reuse: 'OpenAI reused "Codex" name for engineering agent powered by newer reasoning models such as o3 and o4-mini, distinct from 2021 Codex family.' Understanding shows evolution of AI coding tools.

GitHub Copilot: AI pair programmer providing code suggestions and completions in IDE. Evolution: (1) 2021: launched with Codex (GPT-3 descendant), technical preview. (2) 2023: Copilot X upgrade, switched to GPT-4. (3) 2024: multi-model support, 'users choose between different large language models, such as GPT-4o or Claude 3.5.' (4) 2025: 'GPT-4.1 default across chat, agent mode, code completions, optimized for speed, reasoning, context handling.' Agent mode (Feb 2025): 'agentic coding capabilities.' GPT-5-Codex (Sept 2025): 'rolling out in public preview for GitHub Copilot, optimized for agentic coding, available to Copilot Pro, Pro+, Business, Enterprise.' Features: code completion, chat, inline edits, agent mode. Pricing: Free (limited), Pro, Business, Enterprise. Essential modern development tool.

Claude Code vs GitHub Copilot comparison (2025): fundamentally different architectures serving complementary use cases. Claude Code: Terminal-first CLI agent with 200K token context window (largest in industry, 10x typical IDE context), autonomous multi-step task execution via agent loop (plan → execute → verify → iterate), MCP integration for 200+ external tools (Jira, Figma, Slack, databases), extensive file operations (read/write/edit across entire project), web research capabilities (fetch documentation, search APIs), plugin/hook system for customization, conversation-based interaction with persistent context across sessions. Primary use cases: Complex multi-file refactoring (architectural changes spanning 50+ files), automated workflows (CI/CD integration, code generation scripts), research + implementation (gather requirements → design → code in single session), project setup (scaffold apps, configure tooling), multi-day tasks with checkpointing. Strengths: handles tasks requiring deep codebase understanding (analyzes dependencies across modules), executes bash commands (run tests, deploy, manage git), chains operations (analyze → refactor → test → commit), maintains conversation state (resume complex work sessions). GitHub Copilot: Multi-IDE extension (VS Code, JetBrains IDEs, Visual Studio, Neovim, Xcode) with real-time inline suggestions as you type (ghost text completions), chat interface in sidebar (ask questions, generate code snippets), agent mode (Feb 2025 feature: multi-file edits with plan review), multi-model support (GPT-4.1 default, Claude 3.5 Sonnet, Gemini 1.5, o1-mini for reasoning, Codex-GPT-5 experimental), security scanning (vulnerability detection in suggestions), enterprise features (code indexing, organization-wide policies, compliance reporting). Primary use cases: Real-time coding assistance (autocomplete function implementations while typing), quick code generation (write boilerplate, generate tests), inline explanations (highlight code → ask Copilot), refactoring suggestions (improve selected code), learning new APIs (context-aware completions using workspace symbols). Strengths: zero-friction IDE integration (no context switching), sub-second latency (<100ms for completions, 1-3s for chat), learns from workspace context (indexes open files, git history), 82% enterprise adoption (2024 data, industry standard). Key architectural differences: (1) Context handling - Claude Code: 200K tokens persistent across terminal session, includes all loaded files + conversation history, manual context control (specify files with --file flag). Copilot: 4K-8K tokens per completion request, automatic context from open editor tabs + workspace symbols + git history, transparent context selection (no manual configuration). (2) Interaction model - Claude Code: conversational agent (multi-turn dialogue), executes tasks autonomously (user approves actions), returns to user for clarification/approval. Copilot: reactive assistant (responds to typing/selections), suggestions for user to accept/reject, chat for questions only (doesn't execute independently until agent mode). (3) Scope of operations - Claude Code: project-wide + system-level (file I/O across directories, bash commands, git operations, package management, deployment scripts). Copilot: editor-focused (current file edits, workspace searches, chat responses), agent mode extends to multi-file (experimental 2025). (4) Extensibility - Claude Code: MCP protocol for custom tools, plugins for workflow bundles, hooks for automation, CLAUDE.md project instructions. Copilot: GitHub Apps integration, organization-wide settings, custom prompt libraries (limited vs MCP). When to use Claude Code: (1) Architecture-level refactoring (migrate React Class components → hooks across 100+ files), (2) Multi-step automation (analyze codebase → generate migration plan → execute → run tests → commit), (3) Research-heavy tasks (gather API docs → design implementation → code → test), (4) Complex debugging (analyze logs → trace through codebase → identify root cause → fix), (5) Project initialization (scaffold Next.js app → configure TypeScript → set up testing → create components → write docs). When to use GitHub Copilot: (1) Fast-paced coding (writing functions, implementing features, typing boilerplate), (2) Learning unfamiliar APIs (explore new library with context-aware completions), (3) Quick refactors (extract function, rename variables, improve single file), (4) Code review assistance (ask Copilot about code changes in PR), (5) Inline documentation (generate JSDoc comments while coding). Complementary usage (best practice 2025): Use both simultaneously - Copilot for real-time coding (autocomplete as you type, inline suggestions), Claude Code for complex tasks in terminal (architecture planning, multi-file refactors, automated testing, deployment workflows). Example workflow: Start in Claude Code to scaffold project structure and design architecture, switch to IDE with Copilot for fast feature implementation, return to Claude Code for testing/deployment automation. Survey data (2025): 60% of developers using both tools report 40% productivity gain vs single tool, Copilot excels at speed (<100ms latency) while Claude Code excels at correctness (200K context enables better decisions). Performance comparison (2025 benchmarks): Code completion speed - Copilot: 50-100ms inline, 1-3s chat. Claude Code: 2-5s terminal response (optimized for correctness over speed). Context understanding - Claude Code: 200K tokens (entire large codebases), Copilot: 8K tokens (current files + symbols). Task success rate - Multi-file refactors: Claude Code 85% first-try success, Copilot agent mode 70% (newer, less mature). Single-file generation: Copilot 90%, Claude Code 88% (nearly equivalent). Integration ecosystem - Copilot: 10+ IDEs native, GitHub workflow integration. Claude Code: terminal-first, VS Code extension (2025), GitHub Actions support. Cost comparison (2025 pricing): GitHub Copilot Individual: $10/month or $100/year (unlimited completions, chat, agent mode). Business: $19/user/month (organization policies, audit logs). Enterprise: custom pricing (IP indemnity, dedicated support). Claude Code: Free tier (usage-based on Claude API consumption), scales with Anthropic API pricing (~$3-15/1M tokens depending on model). Heavy users: $20-50/month typical. Essential understanding: Copilot = IDE-native real-time assistant (autocomplete++), Claude Code = autonomous terminal agent (task executor). Not mutually exclusive - complementary tools serving different workflow phases (design/planning vs implementation).

GPT-5-Codex (September 2025 public preview): OpenAI's latest code-specialized model optimized for agentic coding workflows in GitHub Copilot, represents complete architectural evolution from original Codex (2021-2023). Architecture: Built on GPT-5 foundation (175B+ parameters estimated, exact count undisclosed), incorporates o-series reasoning capabilities (chain-of-thought, step-by-step problem decomposition), trained on 2025-current code repositories (includes Rust, Go, TypeScript 5.x, Python 3.11+, modern framework patterns). Available exclusively through GitHub Copilot Pro ($20/month), Pro+ ($39/month), Business ($39/user/month), Enterprise (custom pricing) tiers. Original Codex (2021): GPT-3 derivative (12B parameters Codex model, 300M Codex-Cushman smaller variant) fine-tuned on public GitHub code through 2021, powered early GitHub Copilot technical preview. Deprecated March 2023, replaced by GPT-3.5-Turbo and GPT-4 models. Fundamental differences (2025 analysis): (1) Agentic capabilities - GPT-5-Codex: Multi-step task execution with planning phase (analyze request → decompose into subtasks → execute sequentially → verify → iterate). Agent mode creates implementation plans shown to user for approval before execution, modifies multiple files autonomously, runs tests to verify changes, commits results with descriptive messages. Example workflow: User requests Add authentication to API, GPT-5-Codex plans: create User model, implement JWT middleware, add login/logout endpoints, write integration tests, update API docs, then executes all steps with user approval. Original Codex: Single-step completions only (complete this function given docstring), no planning or verification, user manually chains multiple completions for complex tasks. (2) Reasoning depth - GPT-5-Codex: Chain-of-thought reasoning (explains approach before coding, shows intermediate steps, validates logic), handles ambiguous requirements (asks clarifying questions when spec incomplete), considers edge cases proactively (null handling, error states, performance implications). Original Codex: Pattern matching (predict next tokens based on training patterns), no explicit reasoning, frequently misses edge cases requiring manual review. (3) Context window - GPT-5-Codex: 128K tokens input (equivalent to ~96K words, ~3000 lines of code with full project context), retains conversation history across Copilot chat session, indexes workspace files for semantic search. Original Codex: 2048 tokens (1500 words, ~100 lines of code), no conversation memory, context limited to open file + few surrounding lines. (4) Code understanding - GPT-5-Codex: Semantic code analysis (understands program flow across files, tracks variable usage, identifies dead code, suggests architectural improvements), cross-file refactoring (renames symbols across project, updates imports, fixes references), test-driven development (generates tests before implementation when prompted). Original Codex: Syntactic pattern matching (completes based on similar code seen in training), single-file scope, no cross-file understanding. (5) Error handling - GPT-5-Codex: Proactive error detection (validates generated code compiles, runs type checker, identifies potential runtime errors), suggests fixes when generation fails (syntax errors, type mismatches, missing imports), learns from failures in conversation (if approach fails, tries alternative automatically). Original Codex: No validation (returns code regardless of correctness), user discovers errors when compiling/running, no retry mechanism. (6) Language/framework coverage - GPT-5-Codex: Trained on 2024-2025 code (React 19 Server Actions, Next.js 15 Turbopack, Bun runtime, Rust async traits, Go generics, Python 3.12 features), understands modern idioms (TypeScript satisfies operator, Rust ? error propagation, Python match statements). Original Codex: 2020-2021 patterns (React Class components, older Next.js, pre-async Rust, no Go generics, Python 3.8), outdated for modern development. (7) Performance - GPT-5-Codex: 300-800ms latency for inline completions (slower than GPT-4.1 Turbo 50-100ms but higher quality), 3-8s for chat responses with reasoning, 15-45s for agent mode multi-file edits (includes planning, execution, validation). Original Codex: 50-150ms completions (optimized for speed over quality), no chat/agent modes. (8) Deployment status - GPT-5-Codex: Public preview September 2025 (opt-in via Copilot settings model selection menu), gradual rollout to paid tiers (Pro+ first, Business/Enterprise following), coexists with GPT-4.1 (default), Claude 3.5 Sonnet, o1-mini (reasoning-focused). Users choose model per task (GPT-5-Codex for complex implementations, GPT-4.1 for fast completions). Original Codex: Fully deprecated 2023 (API endpoints shut down), no longer accessible. Real-world performance comparison (2025 benchmarks): HumanEval (code generation benchmark, 164 programming problems): GPT-5-Codex scores 92% pass rate (generates correct solution on first try), original Codex 47%, GPT-4 85%, Claude 3.5 Sonnet 89%. MultiPL-E (multilingual code, 18 languages): GPT-5-Codex 85% average, original Codex 38%, shows 2x improvement across languages. SWE-bench (real-world GitHub issues, 2294 problems): GPT-5-Codex resolves 48% of issues autonomously (agent mode end-to-end), original Codex 12% (completion-only approach insufficient). Multi-file refactoring tasks: GPT-5-Codex 85% success rate (agent mode correctly updates imports, renames across files, maintains type safety), original Codex not applicable (single-file only). Use cases enabled by GPT-5-Codex (vs impossible with original): (1) End-to-end feature implementation - User describes feature in natural language (Add dark mode toggle to settings), GPT-5-Codex implements: theme context provider, toggle component, CSS-in-JS styles, localStorage persistence, tests. Original Codex: requires 20+ manual completion requests, user stitches together. (2) Bug diagnosis and fixing - User pastes error stack trace, GPT-5-Codex traces through codebase, identifies root cause, implements fix across affected files, adds regression test. Original Codex: suggests generic fixes, doesn't understand cross-file dependencies. (3) Code migration - Migrate React Class component to hooks: GPT-5-Codex converts lifecycle methods to useEffect, state to useState, updates prop types, fixes tests. Original Codex: completes individual hooks incorrectly, misses lifecycle nuances. (4) Documentation generation - GPT-5-Codex analyzes API routes, generates OpenAPI 3.1 spec with schemas, auth requirements, examples. Original Codex: generates incomplete specs, hallucinates endpoints. (5) Test generation - GPT-5-Codex creates comprehensive test suites (unit + integration + E2E), mocks external dependencies, achieves >85% coverage. Original Codex: writes basic happy-path tests, misses edge cases. Common pitfalls: (1) GPT-5-Codex slower than GPT-4.1 for simple tasks - use GPT-4.1 for fast autocomplete, GPT-5-Codex for complex features. (2) Agent mode requires review - always inspect multi-file changes before accepting (AI can introduce subtle bugs). (3) Preview status - GPT-5-Codex in public preview (may have bugs, API changes possible), production teams should test thoroughly before deploying AI-generated code. (4) Availability limited - requires paid Copilot tier ($20-39/month minimum), not available in free tier or via OpenAI API directly. Strategic significance: GPT-5-Codex represents industry shift from autocomplete tools (original Codex era 2021-2023) to autonomous coding agents (2025+). Enables junior developers to implement features previously requiring senior expertise, reduces time-to-implementation 50-70% for complex features (agent mode handles boilerplate, developers focus on business logic). Competes directly with Claude Code CLI, Cursor IDE, Replit Agent in agentic coding market. Essential for understanding AI coding tool evolution (completion → chat → agents) and selecting appropriate tool for task complexity (Copilot autocomplete for simple tasks, GPT-5-Codex agent mode for complex features, Claude Code for multi-day projects).

Claude Code best practices (2025 production-tested): (1) Specific task descriptions - Provide detailed requirements with acceptance criteria. Bad: Fix the bug. Good: Fix race condition in OrderProcessor where concurrent requests create duplicate orders - add pessimistic locking with SELECT FOR UPDATE, implement idempotency key checking, add integration test covering concurrent scenarios. Specificity reduces iteration cycles 60%. (2) Comprehensive context provision - Include relevant files, architecture decisions, constraints. Structure: current behavior, desired changes, why needed, success criteria. Example: This payment gateway uses Stripe 2023 API, must handle webhook retries with idempotency, timeout after 30 seconds, log to Datadog. Context reduces misunderstandings 70%. (3) Strategic subagent delegation - Use web fetching subagents for documentation research (latest framework docs, API references), file reading subagents for codebase exploration (find all database models, locate API endpoints). Subagents parallelize work, reduce main conversation token consumption 40%. Configure with /subagent command. (4) MCP integration for workflows - Connect Claude Code to company tools: GitHub (manage issues/PRs), Jira (create tickets), Slack (send notifications), PostgreSQL (query analytics), Sentry (fetch error reports). Example workflow: query Sentry for errors → analyze stack traces in codebase → fix bugs → create PR → notify team via Slack, all in single conversation. Configure MCPs in .mcp.json project root. (5) Iterative refinement strategy - Break complex tasks into numbered phases: (1) analyze current architecture, (2) propose solution with tradeoffs, (3) implement core logic, (4) add error handling, (5) write comprehensive tests, (6) update documentation. Each phase builds on previous, allows course correction. Achieves 85% first-try success vs 40% for monolithic requests. (6) Mandatory code review process - ALWAYS review generated code before accepting, even simple changes. Check: correctness (does it solve problem?), edge cases (null handling, error states), performance (algorithmic complexity, database queries), security (input validation, auth checks), style (matches codebase conventions). Run tests locally before committing. AI makes mistakes 10-15% of time requiring human oversight. (7) Version control discipline - Commit before major Claude Code sessions (easy rollback if AI changes break code), use feature branches for AI-assisted work (isolate changes from main), write descriptive commit messages (document AI-generated vs human-reviewed changes), squash commits before merging (clean git history). Integration: Claude Code can execute git commands (add, commit, push with user approval). (8) Reusable workflow plugins - Create project-specific plugins for repeated tasks. Example .claude-plugin/plugin.json: defines security-review slash command (OWASP Top 10 checklist), test-generator agent (creates Jest tests with >90% coverage target), deploy-staging hook (runs tests → builds → deploys to staging automatically). Team distributes via npm package or git submodule, ensures consistency (100 engineers using same code review standards). (9) Automated hooks for consistency - Configure lifecycle hooks in .claude/hooks.json: SessionStart hook loads project context from CLAUDE.md, PreToolUse hook validates file edits match ESLint rules before execution, PostToolUse hook runs prettier auto-formatter after code generation, Stop hook creates summary of changes for commit message. Hooks enforce quality without manual oversight. (10) Strict security practices - Never include secrets in prompts (Claude logs conversations), use environment variables for API keys (read from .env files), review credentials before accepting MCP server configurations, audit third-party plugins (inspect .claude-plugin code before install), rotate API keys quarterly, enable audit logging for compliance (SOC 2 requirement). Use /redact command to sanitize conversations before sharing with team. (11) Comprehensive documentation - Document custom slash commands in project README with usage examples and expected outputs, maintain CLAUDE.md project instructions (coding standards, architecture decisions, testing requirements), version control plugin/hook configurations, create runbooks for CI/CD Claude Code integrations. New team members onboard 50% faster with documented AI workflows. (12) Thorough testing protocol - Test AI-generated code with same rigor as human code: unit tests (Jest/Vitest for logic), integration tests (API endpoints, database interactions), E2E tests (Playwright for user flows), performance tests (benchmark latency/throughput), security tests (OWASP ZAP scans). Achieve >85% coverage before merging. Claude Code can generate tests, but humans must verify correctness. Advanced best practices (expert-level 2025): (13) Context optimization - Keep conversation focused (start new chat for unrelated tasks), use /compact to reduce token usage when context grows large (>100K tokens), reference files by path instead of pasting content (--file flag more efficient), leverage CLAUDE.md for persistent context (project conventions applied automatically). Optimized context reduces costs 40% (fewer tokens per request). (14) Performance monitoring - Track Claude Code impact with metrics: time saved (estimate hours vs manual implementation), code quality (bug rate in AI-generated vs human code), test coverage (AI generates comprehensive tests faster), velocity (features shipped per sprint). Typical results: 30-50% time savings, equivalent quality with review, 20% higher coverage. (15) Failsafe workflows - Implement approval gates for destructive operations (database migrations, production deployments, file deletions require explicit human confirmation), use read-only MCP servers for sensitive systems (query production database but can't write), run Claude Code in isolated environments for experimental changes (Docker containers, VM snapshots). Prevents accidents (AI deleted production data incidents in 2024). (16) Continuous learning - Review Claude Code conversations weekly (identify what prompts worked well vs failed), share effective slash commands with team (build organizational knowledge), update CLAUDE.md as project evolves (architecture changes, new conventions), monitor Claude Code updates (new features, model improvements). Teams that iterate on AI workflows see 10% productivity gains monthly. (17) Hybrid automation - Combine Claude Code with traditional CI/CD: Claude Code generates feature code, GitHub Actions runs automated tests, SonarQube performs static analysis, human reviews before merge. AI handles repetitive work (boilerplate, tests, docs), humans focus on architecture and business logic. Optimal division of labor. (18) Cost management - Monitor API usage with anthropic.com dashboard, set budget alerts at $50/month threshold (typical heavy user), use caching for repeated prompts (60% cost reduction), batch similar tasks in single conversation (analyze 10 files together vs 10 separate chats), choose appropriate model (Haiku for simple tasks, Sonnet for complex reasoning, Opus for critical decisions). Production teams optimize to $20-40/month per developer. Common mistakes to avoid (2025): (1) No code review - Blindly accepting all Claude suggestions (leads to bugs in production, security vulnerabilities). Always review manually. (2) Vague requests - Expecting Claude to read your mind (results in wrong implementations requiring multiple iterations). Be explicit. (3) Ignoring errors - When Claude's code doesn't compile/run, pasting error without explaining context (wastes conversation turns). Provide full error trace + relevant code. (4) Not testing - Trusting AI-generated tests without running them (tests may pass but not cover edge cases). Verify locally. (5) Committing sensitive data - Including API keys/passwords in CLAUDE.md or conversations (leaked to Anthropic logs, security breach). Use environment variables. (6) Over-reliance - Using Claude for every trivial task (formatting, renaming variables) when manual faster (context switching overhead). Reserve for complex tasks. Essential for production-grade Claude Code usage (100+ repos using AI assistance at scale).

MCP integration setup (supports remote servers since June 2025): Method 1 - CLI command (recommended for HTTP servers): claude mcp add --transport http for remote servers, or claude mcp add for local servers. Method 2 - Manual configuration: (1) Install MCP server: npm, pip, or binary depending on server. (2) Configure .mcp.json: create in project root, home directory, or /.config/claude-code/. (3) Add server configuration: { "mcpServers": { "server-name": { "command": "npx", "args": ["server-package"] } } }. (4) Restart Claude Code: reload configuration. (5) Verify: Claude can access tools from server. Example servers: @modelcontextprotocol/server-github (GitHub integration), @modelcontextprotocol/server-postgres (database), filesystem servers, API integrations. Scopes: local (.mcp.json in project), user (/.config/claude-code/.mcp.json), both merge. HTTP transport recommended for cloud-based services (remote MCP servers). Best practices: document server purpose, secure credentials, version control server list (not secrets). 'Claude Code functions as MCP client, connects to any number of MCP servers.' Essential for extensibility.

Claude Code use cases: (1) Code generation - create functions, classes, components from natural language descriptions. (2) Refactoring - modernize code, improve structure, apply design patterns. (3) Bug fixing - analyze stack traces, suggest fixes, implement solutions. (4) Documentation - generate inline comments, README files, API docs with OpenAPI specs. (5) Testing - create unit/integration tests, test data, mock objects. (6) Research - gather technical information, analyze documentation, compare implementation approaches. (7) Code review - analyze code quality, suggest improvements, security vulnerability detection. (8) Migration - upgrade dependencies, migrate frameworks (React 17→18), modernize syntax (JS→TS). (9) Automation - create bash scripts, GitHub Actions workflows, deployment automation. (10) Data processing - parse CSV/JSON, transform data formats, generate reports. (11) API integration - implement REST/GraphQL clients, MCP server connections. (12) Project setup - initialize monorepos, configure tooling (ESLint, TypeScript), generate boilerplate. Versatile coding assistant for entire development lifecycle.

Custom slash commands: define in .claude/commands/ directory (project root). Structure: create Markdown file (kebab-case-name.md), file content becomes command prompt. Example: .claude/commands/security-review.md contains "Review this code for: OWASP Top 10 vulnerabilities, SQL injection risks, XSS, CSRF, authentication issues, secret exposure." Usage: /security-review triggers command. Advanced: (1) Parameters - reference ${file} or ${selection} in command text. (2) Multi-step - complex prompts with numbered instructions. (3) Context - include file glob patterns (**/*.ts), project conventions. (4) Trigger subagents - delegate to specialized agents for research/file operations. Best practices: descriptive kebab-case names (test-generator, deploy-staging), clear instructions in Markdown, document expected inputs/outputs, version control in git, share via plugins for team distribution. Benefits: reusable workflows, consistent code review standards, team alignment, one-command common tasks. Essential for productivity and team collaboration.

GitHub Copilot Chat (2025): IDE-integrated (VS Code sidebar, inline chat, JetBrains), multi-model (GPT-4.1 default, Claude 3.5 Sonnet, o1-mini), context from open files/workspace symbols, code-focused conversations, inline suggestions as you type, agent mode for multi-file edits. Scope: current IDE workspace session. Claude Code: CLI/terminal-based, 200K token context window (largest), autonomous agents with MCP extensibility, file operations + bash + web search, plugins/hooks system, conversation persistence across sessions. Scope: entire project + system-level tasks. When Copilot Chat: quick questions while coding, explain code inline, generate function snippets, real-time autocomplete. When Claude Code: complex multi-file refactors, research + documentation gathering, CI/CD automation, project setup/configuration, multi-step workflows with checkpoints. Complementary: use both simultaneously (Copilot for IDE real-time assistance, Claude Code CLI for complex tasks). VS Code extension available for Claude Code (2025). Understanding guides tool selection.

Claude Code context management (200K token window - largest in industry): (1) Conversation context - maintains full thread across interactions, previous messages/tool calls inform responses. (2) File context - remembers all files read/edited in session, tracks modifications. (3) Task context - tracks multi-step task progress with intermediate results. (4) Project context - understands project structure via file tree analysis, package.json/requirements.txt dependencies, code conventions from existing files. (5) Subagent context - focused sub-contexts for specialized tasks (web research, codebase search). (6) Session persistence - conversations resume across terminal sessions (stored in /.claude/). Context window limits: 200K tokens input (150K words), older messages auto-summarized when approaching limit. Best practices: break very long sessions into focused conversations, provide context explicitly via file paths, use /compact command to reduce context, reference files instead of pasting large code blocks, start new chats for unrelated tasks, use MCP servers for external knowledge (databases, wikis). Long-lived context enables complex multi-day tasks.

Claude Code security considerations (2025 enterprise-grade): (1) Mandatory code review - ALWAYS review AI-generated code before accepting and committing. Claude makes mistakes 10-15% of time (logic errors, security vulnerabilities, performance issues). Review checklist: input validation (SQL injection prevention with parameterized queries, XSS prevention with escaped outputs, CSRF tokens for state-changing operations), authentication/authorization (proper permission checks, session validation, JWT expiry verification), secret exposure (no hardcoded API keys, credentials in environment variables only, .gitignore for .env files), error handling (don't leak stack traces to users, log errors to monitoring systems, graceful degradation for failures), dependency security (check for known CVEs in suggested packages, verify package authenticity, use lock files). Production incident (2024): AI agent suggested vulnerable JWT implementation allowing token forgery, caught in code review before deployment. (2) Strict credential management - Never include secrets in Claude Code prompts (conversations logged to Anthropic servers for model improvement, potential data leakage). Store sensitive credentials in: environment variables (.env files with dotenv library, export statements in shell profiles), secret managers (1Password CLI, AWS Secrets Manager, HashiCorp Vault), OS keychain (macOS Keychain Access, Windows Credential Manager). Claude Code reads environment variables with process.env.VARIABLE_NAME access, preventing credential exposure in conversation history. Rotate API keys quarterly (OpenAI, Anthropic, GitHub tokens), use scoped tokens with minimal permissions (read-only database users for analytics, repo-scoped GitHub tokens vs admin access), audit credential access logs monthly (detect compromised keys early). Use /redact command to sanitize conversations before sharing with team (removes API keys, passwords, PII from history). (3) File access control - Claude Code can read/write ANY file with user permissions (entire home directory, system files if sudo). Restrict access: run Claude Code with limited user account (not root/admin), use file permissions to protect sensitive data (chmod 600 for private keys, separate user accounts for production vs development), audit file operations in conversation history (review which files Claude touched), implement approval hooks for sensitive directories (PreToolUse hook requires confirmation before editing /etc/, production config files). Example vulnerability (2024): Claude Code accidentally deleted .git directory when asked to clean project, restored from backup but lost uncommitted work. Solution: commit before major AI sessions, use /safe-mode for experimental changes (restricted file access). (4) Command execution risks - Claude Code runs bash commands with full shell access (rm -rf, curl to external servers, package installations). Verification required before executing: destructive operations (file/directory deletion, database DROP commands, irreversible migrations), network requests (uploading data to third-party APIs, downloading unverified scripts), system modifications (installing packages, changing permissions, editing system configs), credential operations (git commits with tokens, SSH key operations). Implement approval workflow: claude config set auto-approve false requires manual confirmation for all bash commands, review command before execution (check for typos, validate arguments), use dry-run flags when available (terraform plan before apply, git diff before commit). Production incident (2024): AI ran npm install malicious-package after hallucinating package name, caught by developer review before execution. (5) MCP server vetting - Third-party MCP servers run arbitrary code with Claude's permissions (can access files, execute commands, make network requests). Security checklist before installing: verify source (official @modelcontextprotocol org vs unknown author, check GitHub stars/contributors, read source code), review permissions (what environment variables required?, which files accessed?, network endpoints called?), check dependencies (npm audit for vulnerabilities, avoid unmaintained packages), test in sandbox first (Docker container with no sensitive data, VM snapshot for easy rollback), monitor behavior (audit logs for unexpected operations, network traffic analysis). Approved MCP server list: @modelcontextprotocol/server-github (official, 10K+ stars, actively maintained), @modelcontextprotocol/server-postgres (official, read-only by default), company-internal servers only (never public servers for proprietary data). Deny list: servers requiring AWS admin credentials, servers with write access to production databases, abandoned projects (last commit >1 year ago). (6) External API integration security - Claude Code can call external APIs via MCP servers or bash (curl commands, HTTP libraries). Risks: data exfiltration (sending proprietary code to third-party analytics), quota exhaustion (runaway API calls costing thousands), credential leakage (API keys in logs, URLs). Mitigation: rate limiting (max 100 API calls/session prevents runaway costs), read-only APIs when possible (query-only endpoints for analytics, no write/delete operations), credential rotation (temporary tokens with 1-hour expiry for high-risk operations), audit logging (track all external requests with timestamp, endpoint, response status), network egress filtering (firewall rules blocking unexpected destinations, allow-list for approved APIs only). Example: MCP server for Stripe limited to read-only operations (query customers/invoices), no charge/refund capabilities, prevents accidental financial transactions. (7) Version control safety - Commit frequently before Claude Code sessions (git commit -m 'Before AI refactoring' creates recovery point), use feature branches for AI work (isolate changes from main, easy revert with git branch -D feature-branch), review git diff before pushing (catch accidental file additions, verify no secrets committed), enable pre-commit hooks (detect secrets with gitleaks, validate code with ESLint, run tests before commit), configure .gitignore properly (exclude .env files, API keys, database dumps, IDE configs). Git safety configuration: git config --global core.hooksPath .git/hooks enables automatic checks, git config commit.gpgsign true enforces signed commits (audit trail for AI vs human changes). Production incident (2024): Claude Code committed .env file with production database credentials, detected by gitleaks pre-commit hook before push. (8) Automated hook security - Hooks execute automatically on lifecycle events (SessionStart, PreToolUse, PostToolUse, Stop) with full bash access. Malicious hooks can: steal credentials (POST API keys to attacker server), modify code silently (inject backdoors during PostToolUse), delete data (run rm -rf in Stop hook). Security measures: code review all hooks before enabling (inspect .claude/hooks.json, verify bash scripts), version control hook configurations (git tracks changes, blame shows who added suspicious hooks), test in isolated environment first (Docker container, separate git clone), disable hooks from untrusted sources (company-approved hooks only, no copy-paste from internet), audit hook executions (log all hook runs with timestamp, command, output). Use type: prompt hooks vs type: command for sensitive operations (prompt hooks use Claude Haiku for intelligent decisions vs blind bash execution). (9) Plugin integrity - Plugins bundle slash commands, agents, MCP servers, hooks in single package (.claude-plugin directory). Malicious plugins can compromise entire system. Verification: inspect .claude-plugin/plugin.json manifest (lists all components, permissions required), review source code (check commands/.md files, agents/.ts logic, hooks.json configurations), verify digital signature (official plugins signed by Anthropic, third-party plugins unverified), test in sandbox (separate user account, VM snapshot), monitor resource usage (CPU/network for cryptomining, excessive API calls). Distribution: internal npm registry only (company-controlled plugins), never public npm (unverified code), peer review before team adoption (2+ engineers approve plugin). (10) Network security - Claude Code accesses external resources (web search, documentation fetching, API calls). Risks: data leakage (code snippets sent to Google Search, proprietary algorithms exposed), man-in-the-middle attacks (HTTP vs HTTPS, certificate validation), malicious injections (compromised documentation sites, supply chain attacks). Controls: HTTPS-only connections (verify TLS certificates, no self-signed certs), egress filtering (firewall rules for allowed domains, deny-by-default policy), VPN for sensitive work (route all traffic through corporate network, hide internal IPs), disable web search for confidential projects (claude config set web-search false prevents Google Search grounding). Corporate policy: Claude Code allowed for public OSS projects only, prohibited for classified/proprietary code without air-gapped environment. Best practices summary (2025 SOC 2 compliance): Principle of least privilege (minimal permissions for MCP servers, read-only database users, scoped API tokens), defense in depth (code review + pre-commit hooks + audit logging, multiple checkpoints catch issues), audit trails (log all Claude operations with timestamp, user, command, files touched), incident response plan (documented procedures for credential leakage, data exposure, accidental deletions), security training (quarterly workshops on AI security risks, share incident learnings, update policies). Monitoring and alerting: Deploy SIEM integration (Splunk/Datadog) tracking Claude Code API calls, file modifications, git commits, external network requests. Alert on anomalies: >100 API calls/hour (potential runaway automation), file modifications in /etc/ or production config (unauthorized system changes), large data transfers (>10MB exfiltration), failed authentication attempts (compromised credentials). Review audit logs weekly (identify security violations, measure compliance, improve policies). Essential for enterprise deployment (Fortune 500 companies require these controls before allowing Claude Code).

Claude Code plugin creation: (1) Directory structure: my-plugin/.claude-plugin/plugin.json (manifest), commands/ (slash commands), agents/ (specialized agents), skills/ (model-invoked capabilities), hooks/ (event handlers). (2) Manifest (.claude-plugin/plugin.json): {"name": "plugin-name", "version": "1.0.0", "description": "Brief description", "author": {"name": "Your Name"}}. (3) Installation: create .claude-plugin/marketplace.json, add marketplace via /plugin marketplace add ./path, install with /plugin install plugin-name@marketplace-name, restart Claude Code. (4) Team deployment: configure in .claude/settings.json for automatic installation. (5) Components: commands (markdown files with instructions), agents (complex workflows), skills (Claude autonomously uses based on task context), hooks (hooks.json for events), .mcp.json (external tools). Verify with /help command. Essential for team workflows and extensibility.

Hooks: automated scripts running at specific Claude Code lifecycle events. Types: (1) Command hooks (type: "command"): execute bash scripts for deterministic logic. (2) Prompt-based hooks (type: "prompt"): use LLM (Claude Haiku) for context-aware decisions, limited to Stop/SubagentStop events. Lifecycle events: PreToolUse (before tool execution), PostToolUse (after tool completion), UserPromptSubmit (before processing input), Stop/SubagentStop (when agents finish), SessionStart/SessionEnd (session boundaries), PermissionRequest (permission dialogs), Notification (Claude sends notifications). Configuration: defined in JSON settings files with matchers and patterns. Matchers: simple strings (exact match) or regex patterns (Edit|Write for flexibility). Use cases: permission automation (auto-approve/deny tools), input validation (sanitize prompts), code quality (auto-format), context injection (load dev info at start), audit logging. Essential for workflow automation.

Claude Code troubleshooting: (1) Run diagnostics: claude doctor (check installation health), /doctor (diagnostic info), /bug (report to Anthropic). (2) Verify installation: which claude (macOS/Linux/WSL) or where claude (Windows) checks executable location. (3) WSL issues: set npm config set os linux before install, or use npm install -g @anthropic-ai/claude-code --force --no-os-check. Verify which npm and which node show Linux paths (/usr/) not Windows (/mnt/c/). (4) Permission errors: use native installer curl -fsSL https://claude.ai/install.sh | bash instead of npm. (5) Auth issues: rm -rf ~/.config/claude-code/auth.json to clear. (6) Performance: /compact reduces context size, restart between major tasks. (7) Search not working: install ripgrep, set USE_BUILTIN_RIPGREP=0. (8) WSL2 firewall (JetBrains): configure Windows Firewall or use mirrored networking in .wslconfig. Essential diagnostic commands for quick resolution.

Claude API: REST API providing programmatic access to Claude models (claude-3-5-sonnet-20241022, claude-opus-4-20250514), usage-based pricing via API keys. Claude Code: terminal CLI built on Claude Agent SDK, uses Claude API internally for LLM calls, adds agent layer (file operations, bash execution, tool orchestration, context management, safety guardrails). Relationship: (1) Claude Code makes API calls - every interaction uses Messages API endpoint. (2) Agent SDK layer wraps API - adds tool use, multi-turn conversations, rollback/checkpoints, file editing with git integration. (3) Different interfaces - API for custom applications, Code for interactive development workflows. (4) Same models - both use identical Claude models (Sonnet 4.5, Opus 4). (5) Authentication - Claude Code handles API key auth automatically via ~/.claude/auth.json. When to use API: building custom chatbots, production integrations, programmatic automation, embedding Claude in applications. When to use Claude Code: interactive coding tasks, multi-file refactors, research + implementation, CI/CD workflows. Can combine: develop with Claude Code, deploy using API. Understanding clarifies when to use each tool.

Code review with Claude Code: (1) Create slash command - .claude/commands/code-review.md with "Review this code for: OWASP Top 10 vulnerabilities (SQL injection, XSS, CSRF), authentication/authorization issues, secret exposure, best practices violations (SOLID, DRY), performance bottlenecks (N+1 queries, unnecessary loops), code smells (long functions, deep nesting), missing error handling, test coverage gaps, documentation clarity, accessibility (WCAG 2.1), type safety issues." (2) Usage - /code-review path/to/file.ts or /code-review (reviews current file). (3) Git diff review - provide git diff main...feature-branch output for PR review. (4) Automated PR checks - create PreToolUse hook checking git diff before commit. (5) Specific reviews - create /security-review, /performance-review with focused prompts. (6) GitHub Actions integration - use claude-code-action@v1 triggered on pull_request events for automated review comments. Analysis includes: security vulnerabilities, architectural issues, performance analysis, test adequacy, documentation completeness, actionable fixes with code examples. Best practices: combine with human review (AI finds issues, humans make decisions), integrate into CI/CD, consistent review checklist, provide context (design decisions, constraints), address feedback systematically. Essential for maintaining code quality at scale.

Claude Code limitations (2025): (1) Context window - 200K tokens cap, extremely large monorepos (>500K LOC) may exceed, use /compact or focused sessions. (2) Code accuracy - may generate incorrect/suboptimal code, ALWAYS review before accepting, run tests. (3) Understanding - may misunderstand complex/ambiguous requirements, verify interpretation with clarifying questions. (4) Breaking changes - may introduce subtle bugs (race conditions, edge cases), test thoroughly with integration tests. (5) Security - may miss vulnerabilities (OWASP Top 10), complement with static analysis tools (SonarQube, Snyk). (6) Performance - generated code may not be optimized, profile with profilers (Chrome DevTools, py-spy), optimize hot paths. (7) Best practices - may use outdated patterns pre-2025, verify against current framework docs. (8) Dependencies - may suggest incompatible versions, check package.json/requirements.txt constraints. (9) Testing - generated tests may miss edge cases, aim for >80% coverage, add boundary tests. (10) Architecture - may not grasp large-scale constraints (microservices, scalability), provide architectural context explicitly. Best practices: human oversight ESSENTIAL, use as copilot not autopilot, code review all changes, comprehensive testing (unit + integration), combine with static analysis tools, provide explicit constraints/requirements. Understanding limitations ensures safe productive usage.

Claude Code GitHub Actions integration: (1) Setup: run /install-github-app for guided setup, or manually install GitHub app at github.com/apps/claude with Contents/Issues/Pull Requests permissions, add ANTHROPIC_API_KEY to repository secrets. (2) Interactive mode workflow: on: issue_comment (types: created), uses: anthropics/claude-code-action@v1, anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}. Responds to @claude mentions in issues/PRs. (3) Scheduled automation: on: schedule (cron: "0 9 * * *"), with: prompt: "Generate summary of yesterday's commits", claude_args: "--max-turns 5". Executes without mentions. (4) Best practices: use CLAUDE.md files for project standards, never hardcode API keys, control costs with --max-turns limits, test thoroughly before merging. (5) GitLab CI/CD: configure .gitlab-ci.yml with Bedrock/Vertex AI support. Essential for automated code review, test generation, documentation updates.

Effective Claude Code prompting (2025 best practices): (1) Be specific - detailed requirements with acceptance criteria, not vague requests. Bad: "make this faster". Good: "optimize this SQL query for <100ms response time, add composite index on (user_id, created_at), maintain transaction safety." (2) Provide context - project structure (monorepo vs multi-repo), tech stack (React 19, TypeScript 5.3, Node.js 20), coding conventions (ESLint config), relevant file paths. (3) Break down complex tasks - multi-step workflows into numbered steps (1. create model, 2. add migration, 3. update API endpoint, 4. write tests). Easier for Claude to execute systematically. (4) Show examples - input/output examples, similar existing code patterns, desired API shapes. (5) Specify constraints - performance requirements (latency <200ms), browser compatibility (Chrome 120+, Safari 17+), security considerations (OWASP compliance), accessibility (WCAG 2.1 AA). (6) Iterative refinement - start with high-level goal, refine based on Claude's questions/responses. (7) Use precise technical terminology - avoid ambiguity ("React Server Component" not "server-side rendering"). (8) Reference specific files - mention paths (src/components/Button.tsx:45-67) and line numbers for exact context. (9) Request explanations - ask "explain your approach" to understand reasoning, learn patterns. (10) Verify assumptions - confirm Claude's interpretation aligns with intent before implementation. Essential for production-quality results and efficient collaboration.

AI coding assistants comparison (2025): Claude Code: terminal-first CLI, 200K token context (largest), MCP integration (Figma/Jira/Slack), multi-file edits with checkpoints, autonomous agents, VS Code extension available. Best for: complex refactors, multi-step tasks, terminal workflows. GitHub Copilot: multi-IDE extension (VS Code/JetBrains/Visual Studio/Vim), inline suggestions as you type, chat interface, multi-model (GPT-4.1/Claude 3.5), security scanning, 82% enterprise adoption. Best for: real-time coding, quick prototyping, IDE-integrated. Cursor: standalone IDE (VS Code-based), three chat modes (Agent/Ask/Manual), multi-model (OpenAI/Claude/Gemini/Grok/DeepSeek), Team: $40/user/month. Best for: visual development, IDE-first. Complementary: use multiple tools (Copilot for IDE suggestions, Claude Code for complex tasks). Understanding guides tool selection.

Claude Code is terminal-first CLI agent with 200K token context window (10x typical IDE), best for complex multi-step tasks requiring autonomous execution. Use cases: (1) Multi-file refactoring - Architectural changes spanning 50+ files (migrate React Class → hooks across codebase). (2) Automated workflows - CI/CD integration, code generation scripts with bash command execution. (3) Research + implementation - Gather requirements from docs → design → code in single conversation session. (4) Project scaffolding - Set up Next.js app, configure TypeScript, create components, write tests. (5) Complex debugging - Analyze logs, trace dependencies, identify root cause, implement fix. Strengths: Deep codebase understanding (analyzes module dependencies), executes bash commands (run tests, git operations), chains operations (analyze → refactor → test → commit), maintains conversation state across sessions. Integration: MCP protocol for 200+ external tools (Jira, Slack, databases), plugin/hook system for custom workflows. Architecture: Agent loop (plan → execute → verify → iterate), persistent context, file operations across entire project.

GitHub Copilot is multi-IDE extension (VS Code, JetBrains, Neovim, Xcode) with real-time inline suggestions and chat interface. Use cases: (1) Fast-paced coding - Autocomplete function implementations while typing (ghost text completions). (2) Quick code generation - Write boilerplate, generate unit tests, create API endpoints. (3) Inline explanations - Highlight code → ask Copilot → get instant explanation. (4) Learning new APIs - Context-aware completions using workspace symbols and open files. (5) Refactoring single files - Extract functions, rename variables, improve selected code. Strengths: Zero-friction IDE integration (no context switching), sub-second latency (<100ms completions, 1-3s chat), learns from workspace context (indexes git history, open files), 82% enterprise adoption (industry standard 2024). Features: Multi-model support (GPT-4.1, Claude 3.5 Sonnet, Gemini 1.5), agent mode (Feb 2025: multi-file edits), security scanning (vulnerability detection). Performance: 4K-8K tokens context, reactive assistant model (responds to typing/selections), automatic context selection (transparent, no manual config).

Key differences: Context - Claude Code: 200K tokens persistent across session, manual control. Copilot: 4K-8K per request, automatic from open files. Interaction - Claude Code: Conversational agent, autonomous task execution, user approves actions. Copilot: Reactive assistant, suggestions to accept/reject, chat for questions only. Scope - Claude Code: Project-wide + system-level (file I/O, bash commands, git, deployment). Copilot: Editor-focused (current file edits, workspace searches), agent mode for multi-file (experimental 2025). Architecture - Claude Code: Terminal-first CLI, agent loop (plan → execute → verify). Copilot: IDE extension, inline completions as you type. Extensibility - Claude Code: MCP protocol for custom tools, plugins, hooks. Copilot: GitHub Apps integration, limited vs MCP. Complementary usage (best practice): Use both - Copilot for real-time coding (autocomplete, inline suggestions), Claude Code for complex tasks (architecture, multi-file refactors, automation). Survey (2025): 60% using both report 40% productivity gain vs single tool.

Critical best practices (2025 production-tested): (1) Specific task descriptions - Provide detailed requirements with acceptance criteria. Bad: "Fix the bug." Good: "Fix race condition in OrderProcessor - add SELECT FOR UPDATE locking, implement idempotency keys, add integration test." Reduces iteration cycles 60%. (2) Mandatory code review - ALWAYS review AI-generated code before accepting. Check: correctness, edge cases (null handling, error states), performance, security (input validation, auth checks), style. AI makes mistakes 10-15% of time. Run tests locally before committing. (3) Context optimization - Include relevant files, architecture decisions, constraints. Use subagents for documentation research and codebase exploration (reduces main conversation tokens 40%). Structure: current behavior, desired changes, why needed, success criteria. (4) Version control discipline - Commit before major Claude sessions (easy rollback), use feature branches for AI work (isolate from main), review git diff before pushing (catch accidental additions). Configure pre-commit hooks (gitleaks for secrets, ESLint validation). (5) Iterative refinement - Break complex tasks into phases: (1) analyze architecture, (2) propose solution, (3) implement core, (4) add error handling, (5) write tests, (6) update docs. Achieves 85% first-try success vs 40% monolithic. (6) Security discipline - Never include secrets in prompts, use environment variables for API keys, review credentials before accepting MCP configs, rotate keys quarterly. Use /redact command to sanitize conversations.