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Cleans up temporary files, caches, and resources when Claude session ends. This Stop hook provides comprehensive environment cleanup for development projects, automatically removing temporary files, build artifacts, cache directories, and system-specific files across multiple platforms.

Validates environment variables, checks for required vars, and ensures proper configuration across environments.

The official Infisical MCP server (@infisical/mcp) that lets AI assistants work with Infisical's secrets-management API through function calling — reading, creating, updating, and deleting secrets, and managing projects, environments, folders, and project members — authenticating with a Machine Identity (universal auth) or an access token against Infisical Cloud or a self-hosted instance.

Manage ConfigCat feature flags from Claude — create, update, and delete flags and targeting rules, manage environments, find and clean up stale flags, and audit change history — with the official ConfigCat MCP server and its 52 tools for the full ConfigCat Management API.

Manage LaunchDarkly feature flags, AI configs, environments, and audit logs from Claude — with the official LaunchDarkly Model Context Protocol server backed by the LaunchDarkly REST API.

Manage Docker and Kubernetes infrastructure from Claude — list environments, inspect containers, manage GitOps workflows, troubleshoot resources, and proxy Docker/K8s API calls — with the official Portainer MCP server generated from the Portainer OpenAPI spec.

Manage Railway projects, services, deployments, and environments from Claude — create projects, deploy templates, pull variables, trigger redeployments, and more — with the official Railway MCP server built into the Railway CLI.

Create and manage Unleash feature flags from Claude — evaluate whether a change needs a flag, detect existing flags to prevent duplicates, set rollout strategies, toggle environments, and get cleanup guidance — with the official Unleash MCP server following Unleash best practices.

Expert capability pack for reviewing Claude Code autoMode settings blocks, trusted infrastructure prose, classifier rule overrides, and documented claude auto-mode CLI inspection before enabling permission-free auto mode.

Expert desktop scheduled maintenance capability pack applying documented Desktop scheduled task setup, triggers, environment scoping, and safety review from official desktop scheduled tasks documentation.

Expert Claude Code web remote environment capability pack applying documented cloud session isolation, network controls, and credential proxy patterns from official Claude Code on the web documentation.

Open-source infrastructure for securely running AI-generated code in isolated sandboxes that start in milliseconds, with SDKs for Python, TypeScript, and other languages, persistent snapshots, and an optional managed cloud.

MCP server for connecting Claude to MySQL databases with schema inspection, SQL query execution, SSH tunnel support, multi-database mode, and configurable write-operation controls.

Claude Code statusline that surfaces sensitive environment-file risk and can optionally run git-secrets as a local pre-commit-style scanner.

Official Postman MCP Server for connecting Claude, Codex, Cursor, VS Code, Gemini CLI, and other MCP clients to Postman workspaces, collections, specifications, mocks, monitors, environments, API definitions, and code generation workflows.

Define sandbox environment boundaries for Claude Code: managed settings, network egress, filesystem scope, worktree isolation, and when to fail closed.

A reusable agent prompt that reviews a Claude Code auto mode setup: whether autoMode.environment trusts the right repos, buckets, and domains, how the allow, soft_deny, and hard_deny tiers resolve, and whether $defaults is kept across settings scopes.

Claude Code statusline that shows the active kubectl context and namespace with a deployment-environment risk label.

How to deploy Claude Code in a security- and compliance-sensitive financial-services environment, using its documented data-handling, ZDR, network, IAM, and sandboxing controls.

Set Claude Code environment variables correctly and debug auth, model, and config issues using only documented variables and the /doctor diagnostics.

The Claude Code data-handling controls that matter for teams working near PHI: no-training commercial policy, retention windows, Zero Data Retention, encryption, local transcript handling, telemetry opt-outs, and security model.

Full-featured backend development environment combining architecture planning, database design, and cloud services integration. Perfect for building scalable server-side applications.

PreToolUse Write and Edit guardrail combining the hooks guide protected-file pattern with a local scan for common hardcoded credential shapes called out by GitHub secret scanning guidance before content is written.

Source-backed rules for AI coding assistants that must avoid exposing, copying, logging, committing, or normalizing secrets while editing code, configs, tests, prompts, documentation, and CI workflows.

A practical guide for handling secrets when connecting MCP servers and authoring Agent SDK tools in Claude Code: env expansion in .mcp.json, OAuth scope pins, keychain storage, local scope, and redaction before tool arguments reach the model.

Ship smart contracts on Base with secure deployment, verification, environment management, and production-readiness checklists.

Harden OpenClaw agent environments with secure defaults, policy boundaries, tool governance, and incident response playbooks.

Open-source visual AI programming environment from Ironclad for building, debugging, and embedding complex LLM agents and prompt-chaining graphs, with a desktop app and a TypeScript library.

The official Convex MCP server, built into the Convex CLI, that lets AI agents introspect and query a Convex deployment — listing tables, paginating data, running deployed functions and read-only one-off queries, reading logs and function specs, and managing environment variables — with production deployments blocked by default as a safety measure.

The official Paddle MCP server (@paddle/paddle-mcp) that lets AI assistants work with Paddle Billing — managing the product catalog, prices, and discounts, customers and businesses, subscriptions, transactions and adjustments, and financial reports — over the Paddle API, with sandbox or production environments and a read-only / non-destructive / all tool-permission filter.