# God-Mode Prompt Research Corpus

> **Deep research conducted: February 14, 2026**
> **Purpose**: Build an "Agentic God Mode" prompt that manages authentication, access, and orchestration for AI coding agents across all external services.

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## Table of Contents

1. [MCP (Model Context Protocol) Servers](#1-mcp-model-context-protocol-servers)
2. [AI Agent Credential Management Best Practices](#2-ai-agent-credential-management-best-practices-2025-2026)
3. [GitHub CLI and API Automation for AI Agents](#3-github-cli-and-api-automation-for-ai-agents)
4. [AI Agent Orchestration Platforms and Patterns](#4-ai-agent-orchestration-platforms-and-patterns)
5. [Claude Code CLI Capabilities](#5-claude-code-cli-capabilities)
6. [Cursor IDE Agent Capabilities](#6-cursor-ide-agent-capabilities)
7. [Infrastructure-as-Code for AI Dev Environments](#7-infrastructure-as-code-for-ai-dev-environments)
8. [Platform Integration Patterns for Multi-Tool AI Agents](#8-platform-integration-patterns-for-multi-tool-ai-agents)

---

## 1. MCP (Model Context Protocol) Servers

### Source 1.1: Anthropic — Introducing the Model Context Protocol

- **URL**: https://anthropic.com/news/model-context-protocol
- **Key Findings**:
  1. MCP is an open standard launched by Anthropic (November 2024) that provides a universal protocol for AI assistants to connect to data sources and tools, replacing fragmented custom integrations.
  2. MCP servers expose three building blocks: **Tools** (functions AI models invoke to perform actions), **Resources** (read-only data sources for context), and **Prompts** (pre-built instruction templates).
  3. Early adopters include Block, Apollo, Zed, Replit, Codeium, and Sourcegraph — all integrating MCP for enhanced AI coding capabilities.
  4. MCP became a founding project of the **Agentic AI Foundation** under the Linux Foundation (December 2025), signaling industry-wide adoption.
- **God-Mode Applicability**: The God-Mode prompt must configure MCP as the primary tool-calling interface. Every external service (GitHub, databases, cloud providers, Docker registries) should be exposed as an MCP server that the agent connects to via a standardized protocol. This eliminates custom integration code and makes the prompt portable across Claude, Cursor, Copilot, and VS Code.

### Source 1.2: MCP Server Directories (PulseMCP, FindMCP, MCP Directory)

- **URL**: https://www.pulsemcp.com/servers / https://findmcp.dev/ / https://mcpserverdirectory.org/
- **Key Findings**:
  1. **8,230+ MCP servers** indexed on PulseMCP alone, with daily updates. FindMCP tracks 8,000+, MCP Directory 2,500+.
  2. Most popular servers by weekly installs: **GitHub** (58k/wk), **Filesystem** (52k/wk), **Slack** (41k/wk), **PostgreSQL** (34k/wk), **Puppeteer** (28k/wk).
  3. Categories span 50+ domains: Databases (420+), Code & Git (380+), Search & Web (340+), Communication (310+), Cloud & Infrastructure (290+), Files & Storage (250+).
  4. Over **97 million monthly SDK downloads** and 10,000+ active servers across the ecosystem.
  5. First-class MCP support in: ChatGPT, Claude, Cursor, Gemini, Microsoft Copilot, VS Code.
- **God-Mode Applicability**: The prompt should include a curated list of essential MCP servers to enable for a full-stack dev environment: `@modelcontextprotocol/server-filesystem`, `@modelcontextprotocol/server-github`, `@modelcontextprotocol/server-postgres`, Slack, Puppeteer/Playwright (browser), and Docker. The directories serve as a discovery mechanism for expanding agent capabilities on demand.

### Source 1.3: Docker MCP Toolkit & Gateway

- **URL**: https://docs.docker.com/ai/mcp-catalog-and-toolkit/mcp-gateway/
- **Key Findings**:
  1. Docker MCP Gateway is an open-source orchestration layer that manages MCP server lifecycles in **isolated Docker containers**, routes tool requests, handles credential injection, and provides logging/call-tracing for governance.
  2. Docker MCP Toolkit (in Docker Desktop) provides 270+ curated servers with zero manual setup, OAuth handling, and multi-client connectivity (Claude, Cursor, Copilot).
  3. **Dynamic MCP capabilities**: Agents can autonomously discover, add, and compose MCP servers during conversations using `mcp-find` and `mcp-add` tools — no manual configuration needed.
  4. Security: image signing/attestation, runtime resource limits (1 CPU, 2GB RAM per container), filesystem restrictions, secrets mounted only to target containers, supply-chain verification via Docker Scout.
- **God-Mode Applicability**: Docker MCP Gateway should be the centralized orchestration layer for the God-Mode prompt. It solves credential injection (secrets only go to the containers that need them), provides governance logging, and enables dynamic tool discovery. The prompt should bootstrap the gateway as the primary MCP infrastructure, then register all external services through it.

---

## 2. AI Agent Credential Management Best Practices (2025-2026)

### Source 2.1: HashiCorp Vault — Secure AI Agent Authentication Using Dynamic Secrets

- **URL**: https://developer.hashicorp.com/validated-patterns/vault/ai-agent-identity-with-hashicorp-vault
- **Key Findings**:
  1. Vault uses **dynamic secrets with just-in-time (JIT) provisioning** — credentials are generated on demand, scoped to the agent's specific task, and automatically expire/rotate.
  2. Individual AI agents are mapped as **"entities"** in Vault's identity secrets engine, each with multiple credential aliases for different services.
  3. Authentication uses OAuth 2.0 standard token exchange protocols with role-based access controls (RBAC) scoped to specific agent actions.
  4. Provides complete audit trails of every secret request and access for GDPR, HIPAA, and EU AI Act compliance.
  5. Eliminates static, hardcoded secrets — reduces credential sprawl and exposure windows.
- **God-Mode Applicability**: For production/enterprise God-Mode configurations, Vault should be the secrets backend. The prompt should include Vault integration where: (1) each agent session gets a unique identity, (2) credentials for GitHub, AWS, Docker Hub, etc. are provisioned dynamically with short TTLs, and (3) all access is logged. For simpler setups, the pattern of "short-lived, scoped, auto-rotating" still applies via simpler mechanisms.

### Source 2.2: FINOS — Agentic System Credential Protection Framework

- **URL**: https://air-governance-framework.finos.org/mitigations/mi-23_agentic-system-credential-protection-framework.html
- **Key Findings**:
  1. Framework defines 5 pillars: **credential environment isolation, dynamic credential injection, zero-trust authentication, behavioral monitoring, access minimization**.
  2. Deploy dedicated credential vaults in network segments **isolated from agent execution environments** — agents should never have direct vault access.
  3. Build **credential-free agent images** — no embedded secrets, API keys, or credentials in the agent's base environment.
  4. Prevent agents from accessing environment variables containing credentials (environment variable scrubbing).
  5. Implement **behavioral monitoring** to detect credential discovery or harvesting activities by compromised agents.
- **God-Mode Applicability**: The God-Mode prompt must enforce credential isolation as a core principle. Secrets should never appear in environment variables accessible to the agent. Instead, the prompt should route all authenticated operations through MCP servers or API wrappers that handle auth internally. The agent sees tool interfaces, not raw credentials.

### Source 2.3: Process-Scoped Credentials for AI Agents (dreamiurg.net, Feb 2026)

- **URL**: https://dreamiurg.net/2026/02/11/reducing-attack-surface-for-ai-agents-process-scoped-credentials.html
- **Key Findings**:
  1. AI agents inherit the shell environment, gaining access to **all credentials visible via `env`** — AWS keys, database passwords, API tokens — creating a massive attack surface.
  2. Real-world incidents: 30+ vulnerabilities across Cursor, GitHub Copilot, and Windsurf (December 2025 "IDEsaster" disclosure); compromised Amazon Q extensions that could wipe files and disable infrastructure.
  3. Prompt injection attacks embedded in GitHub issues, docstrings, or package READMEs can hijack agents to steal credentials.
  4. Defense layers: (a) run agents in sandboxed containers, (b) use short-lived, least-privilege credentials (GitHub fine-grained PATs, AWS STS tokens), (c) restrict agent permissions at the tool level with deny rules.
  5. GitHub fine-grained PATs should be scoped to **single repos** with read-only access as default; AWS STS session tokens with narrow IAM policies.
- **God-Mode Applicability**: CRITICAL for the God-Mode prompt. The prompt must: (1) never expose raw credentials in env vars, (2) use GitHub fine-grained PATs scoped per-repo, (3) use AWS STS session tokens not long-lived IAM keys, (4) configure tool-level deny rules to block credential exfiltration commands, (5) run agents in sandboxed containers where possible.

### Source 2.4: 1Password Service Accounts & SDKs for Agentic AI

- **URL**: https://blog.1password.com/service-accounts-sdks-agentic-ai
- **Key Findings**:
  1. 1Password Service Accounts provide **full programmatic item management** — create, fetch, edit, delete, share secrets at runtime.
  2. Recommended pattern: create **dedicated vaults** containing only the secrets the AI agent needs, with read-only access and service account tokens for authentication.
  3. Service account usage reports enable auditing of what items agents access.
  4. Python and JavaScript SDKs available for programmatic integration.
  5. Key warning: avoid exposing raw credentials directly to AI models; use short-lived, tightly scoped tokens instead.
- **God-Mode Applicability**: 1Password is a practical, accessible secrets management solution for individual/small-team God-Mode setups. The prompt could define a 1Password vault per project, with a service account token injected at runtime. The agent accesses credentials through the SDK/CLI rather than raw environment variables. Lower barrier than Vault but same principle: never give the agent direct credential access.

---

## 3. GitHub CLI and API Automation for AI Agents

### Source 3.1: GitHub Blog — Automate Repository Tasks with GitHub Agentic Workflows

- **URL**: https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows
- **Key Findings**:
  1. **GitHub Agentic Workflows** (technical preview, Feb 2026): automated, intent-driven repository workflows authored in **plain Markdown**, running in GitHub Actions.
  2. Capabilities: issue triage/labeling, CI failure investigation with proposed fixes, documentation updates, PR generation — all driven by AI coding agents.
  3. Strong guardrails: automation decisions stay under user control, designed to scale across millions of repos.
  4. Real-world examples: Home Assistant uses it for high-volume issue analysis; CNCF for documentation automation.
  5. GitHub CLI includes preview `gh agent-task` commands for creating, listing, and viewing agent task sessions.
- **God-Mode Applicability**: The God-Mode prompt should leverage Agentic Workflows for CI/CD-triggered automation (issue triage, CI fix proposals) while using `gh` CLI for direct agent operations. The `gh agent-task` commands represent GitHub's native support for agent-driven development. The prompt should include instructions for setting up these workflows alongside direct CLI access.

### Source 3.2: GitHub Docs — Fine-Grained Personal Access Tokens Permissions

- **URL**: https://docs.github.com/en/rest/authentication/permissions-required-for-fine-grained-personal-access-tokens
- **Key Findings**:
  1. Fine-grained tokens offer **50+ specific permissions** (vs. broad scopes in classic tokens), each settable to 'no access', 'read', or 'read and write'.
  2. Each token is restricted to a **single user or organization** and can be limited to **specific repositories**.
  3. Organization owners can enforce: approval requirements, maximum lifetime policies (default 366 days), and access restrictions.
  4. Compromised token impact is minimized — cannot access all resources a user could access (unlike classic tokens).
  5. Fine-grained tokens are the recommended approach for AI agent automation due to explicit access boundaries.
- **God-Mode Applicability**: The God-Mode prompt MUST use fine-grained PATs, not classic tokens. Recommended scopes for a coding agent: `contents:write` (push code), `pull_requests:write` (create/merge PRs), `issues:write` (manage issues), `actions:read` (check CI status), `metadata:read`. Scoped to specific repos only. Token rotation schedule should be defined (e.g., 30-day expiry).

### Source 3.3: GitHub CLI Reference — Full Command Capabilities

- **URL**: https://cli.github.com/manual/gh_help_reference
- **Key Findings**:
  1. Complete `gh` CLI supports: `repo` (create, clone, fork, delete, archive), `issue` (create, list, close, edit, transfer), `pr` (create, review, merge, checkout, diff), `release` (create, upload, download), `run` (list, view, watch, rerun), `workflow` (enable, disable, run).
  2. `gh api` command provides direct REST and GraphQL API access with authentication, pagination (`--paginate`), and jq filtering.
  3. `gh auth` manages authentication with support for multiple accounts and token-based login.
  4. `gh codespace`, `gh ssh-key`, `gh gpg-key`, `gh secret` for managing dev environments and secrets.
  5. `gh extension` system allows installing community-built extensions for additional capabilities.
- **God-Mode Applicability**: The God-Mode prompt should configure `gh` CLI as the primary GitHub interface for agents. Key commands to expose: `gh pr create`, `gh issue create`, `gh release create`, `gh api` (for advanced queries), `gh run view` (CI monitoring), `gh secret set` (secrets management). The prompt should include `gh auth login --with-token` using a fine-grained PAT injected at session start.

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## 4. AI Agent Orchestration Platforms and Patterns

### Source 4.1: AI Workflow Lab — How to Build Multi-Agent AI Systems in 2026

- **URL**: https://aiworkflowlab.dev/article/building-multi-agent-ai-systems-2026-architecture-patterns-mcp-production-orchestration
- **Key Findings**:
  1. Multi-agent AI systems market: **$8.5 billion in 2026**, projected $35 billion by 2030. 57% of companies now deploy AI agents in production.
  2. Three core patterns: **Sequential Pipeline** (linear chain), **Supervisor/Coordinator** (central orchestrator + specialists), **Handoffs** (peer delegation with context preservation).
  3. Critical rule for Supervisor pattern: **exactly one agent must be the orchestrator** to prevent coordination conflicts.
  4. MCP serves as the standardized interface for both agent-to-tool and **agent-to-agent** communication.
  5. Gartner estimates 40% of enterprise apps will feature task-specific agents by end of 2025.
- **God-Mode Applicability**: The God-Mode prompt should use the **Supervisor/Coordinator** pattern where one master prompt orchestrates specialized sub-agents (coding agent, deployment agent, testing agent, infrastructure agent). MCP serves as the communication bus. The God-Mode prompt IS the supervisor — it sets up auth, configures tools, and delegates to specialist agents.

### Source 4.2: GetKnit — Advanced MCP: Agent Orchestration, Chaining, and Handoffs

- **URL**: https://www.getknit.dev/blog/advanced-mcp-agent-orchestration-chaining-and-handoffs
- **Key Findings**:
  1. **Agent-to-agent handoffs** via MCP: agents delegate specialized sub-tasks to domain-expert agents while maintaining shared context.
  2. MCP enables multi-agent orchestration by exposing **agent functionality as callable tools** with consistent interfaces — an agent can call another agent just like it calls any MCP tool.
  3. Context preservation across handoffs is critical: the receiving agent needs task description, relevant state, and constraints.
  4. Chaining pattern: output of one tool/agent becomes input to the next, enabling complex multi-step workflows.
  5. Error handling and fallback strategies are essential — if a specialist agent fails, the orchestrator must retry or route to an alternative.
- **God-Mode Applicability**: The God-Mode prompt should define handoff protocols: when the coding agent needs deployment, it hands off to a deployment agent (configured with Vercel/Cloudflare/AWS credentials via MCP). Each agent gets only the credentials and tools it needs. The prompt defines the handoff interface (what context to pass, what results to expect).

### Source 4.3: Orchestral AI Framework (arXiv 2601.02577)

- **URL**: https://arxiv.org/abs/2601.02577
- **Key Findings**:
  1. Unified, type-safe MCP integration across multiple LLM providers with automatic tool schema generation from Python type hints.
  2. Features: rich tool calling, context compaction, workspace sandboxing, memory management, and sub-agents.
  3. Supports multi-provider orchestration — agents can use different LLM backends (Claude, GPT, Gemini) for different tasks.
  4. Deterministic orchestration: separates what must be predictable (auth, routing, credential injection) from what can be flexible (LLM reasoning, tool selection).
- **God-Mode Applicability**: Reinforces that the God-Mode prompt should separate deterministic operations (auth setup, credential injection, tool registration) from agentic operations (code generation, debugging, testing). The prompt handles the deterministic layer; the agent handles the creative layer. Type-safe MCP schemas ensure agents can't misuse tools.

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## 5. Claude Code CLI Capabilities

### Source 5.1: Claude Code Docs — Run Claude Code Programmatically (Headless Mode)

- **URL**: https://docs.anthropic.com/en/docs/claude-code/headless
- **Key Findings**:
  1. Claude Code runs programmatically via CLI with `-p` (print) flag — non-interactive execution with full tool access and agent loop.
  2. Output formats: **text** (default), **JSON** (structured with session ID/metadata), **stream-JSON** (newline-delimited for real-time streaming).
  3. `--json-schema` flag enforces specific JSON Schema definitions on output — enables structured data extraction.
  4. `--allowedTools` parameter restricts which tools the agent can use — critical for least-privilege.
  5. `--continue` flag resumes previous sessions, preserving context across invocations.
  6. Agent SDK available as CLI + Python and TypeScript packages for programmatic orchestration.
- **God-Mode Applicability**: The God-Mode prompt can use Claude Code in headless mode as an execution backend. `-p` enables scripted agent invocations where the God-Mode orchestrator sends specific tasks to Claude Code instances. `--allowedTools` enforces the principle of least privilege — each invocation gets only the tools it needs. `--json-schema` enables structured outputs for pipeline chaining.

### Source 5.2: Claude Code Docs — MCP Integration

- **URL**: https://code.claude.com/docs/en/mcp
- **Key Findings**:
  1. Three MCP transport methods: **HTTP** (recommended for remote/cloud services), **SSE** (deprecated), **Stdio** (local processes with direct system access).
  2. Configuration via `claude mcp add` command: `claude mcp add --transport http notion https://mcp.notion.com/mcp`.
  3. Popular integrations: Notion, Asana, Sentry, Statsig, Figma, Slack, PostgreSQL.
  4. Environment variables can be passed to MCP servers during configuration.
  5. MCP server connections persist across sessions — configure once, use always.
- **God-Mode Applicability**: The God-Mode prompt should include `claude mcp add` commands for all required services as part of bootstrap. HTTP transport for cloud services (GitHub, Slack, Sentry), Stdio for local tools (filesystem, Docker, databases). The prompt acts as the MCP configuration manifest.

### Source 5.3: Claude Code Docs — Hooks, Skills, Subagents, Agent Teams

- **URL**: https://code.claude.com/docs/en/features-overview / https://code.claude.com/docs/en/hooks / https://code.claude.com/docs/en/agent-teams
- **Key Findings**:
  1. **Skills**: Reusable markdown files loaded automatically or via slash commands (`/deploy`). Can encode workflows, knowledge, and instructions.
  2. **Hooks**: Deterministic shell commands or LLM prompts at 14+ lifecycle events (`PreToolUse`, `PostToolUse`, `SessionStart`, etc.). Enable automation without LLM involvement — e.g., auto-run linters, block destructive commands.
  3. **Subagents**: Isolated execution contexts for parallel tasks. `Task` tool must be in `allowedTools`. Each subagent gets custom system prompts and tool restrictions.
  4. **Agent Teams** (experimental): Multiple independent Claude Code instances with direct inter-agent messaging. Higher token cost but enables parallel research, debugging, cross-layer coordination. Enabled via `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS` env var.
  5. Hooks can fire on MCP tool naming events — enabling custom auth checks before tool execution.
- **God-Mode Applicability**: The God-Mode prompt is essentially a **Skill** + **Hook configuration** + **MCP manifest**. Skills encode the "how to deploy", "how to publish to PyPI", etc. Hooks enforce security (block credential exfiltration commands via `PreToolUse`). Subagents handle parallel tasks (run tests while deploying). Agent teams enable distributed workflows (one agent codes, another reviews, another deploys).

---

## 6. Cursor IDE Agent Capabilities

### Source 6.1: Cursor Docs — Model Context Protocol (MCP)

- **URL**: https://cursor.com/docs/context/mcp
- **Key Findings**:
  1. MCP tools available only in **Agent mode within Composer** — not available in other Cursor features.
  2. Configuration: Settings > Features > MCP. Supports **stdio** and **SSE** transport types.
  3. Agent **automatically uses relevant MCP tools** when available — no explicit prompting required (but can be prompted by name).
  4. Approval workflow: agent displays message requesting user approval before executing tools, with expandable arguments.
  5. Supports arbitrary number of MCP servers simultaneously.
  6. For environment variables: create wrapper scripts that set variables before running the server.
- **God-Mode Applicability**: Cursor's MCP integration is the primary tool-calling mechanism for the God-Mode prompt when used in Cursor. The prompt should document which MCP servers to configure and how. Since Cursor auto-detects relevant tools, the prompt focuses on ensuring the right tools are available rather than explicitly instructing their use. Wrapper scripts for credential injection keep secrets out of Cursor's configuration files.

### Source 6.2: Cursor Directory — Browser Automation MCP Servers

- **URL**: https://cursor.directory/mcp/browser-automation / https://cursor.directory/mcp/playwright / https://cursor.directory/mcp/puppeteer
- **Key Findings**:
  1. **Playwright MCP**: Full browser automation — navigation, clicking, typing, screenshots, PDF generation, accessibility snapshots. Ideal for testing and UI verification.
  2. **Puppeteer MCP**: Browser automation with screenshot capture, JavaScript execution, and page interaction.
  3. **Browser Tools MCP** (AgentDeskAI): Chrome extension-based with screenshot capture, form filling, console log capturing, network request monitoring.
  4. **cursor-ide-browser**: Built-in Cursor MCP server for browser interaction — navigate, snapshot, click, type, scroll, lock/unlock workflow.
  5. All browser automation MCP servers support element targeting via accessibility refs or CSS selectors.
- **God-Mode Applicability**: The God-Mode prompt should configure browser automation for: (1) testing deployed applications, (2) interacting with web dashboards (Vercel, Cloudflare, AWS Console) when CLI isn't sufficient, (3) visual regression testing. The `cursor-ide-browser` is already built-in; Playwright MCP adds more power. The prompt should specify when browser automation is appropriate vs. CLI.

### Source 6.3: Cursor Agents Overview

- **URL**: https://cursor.com/agents
- **Key Findings**:
  1. Cursor Agent mode provides autonomous coding capabilities: build features, fix bugs, explore code.
  2. Tool calling includes: file reading/writing, terminal execution, web search, semantic search, grep/glob, linting, browser automation.
  3. Agent can manage git operations, run tests, install dependencies, and interact with running services.
  4. On-demand usage required — cloud infrastructure processes code.
  5. Supports custom rules via `.cursor/rules/` directory for project-specific instructions.
- **God-Mode Applicability**: Cursor's agent is the primary execution environment for the God-Mode prompt. The prompt should be structured as a `.cursor/rules/` file that configures: (1) MCP servers for external service access, (2) security constraints (deny rules for credential access), (3) workflow instructions (how to deploy, publish, manage infrastructure). The rules system is the native God-Mode delivery mechanism for Cursor.

---

## 7. Infrastructure-as-Code for AI Dev Environments

### Source 7.1: Development Container Specification

- **URL**: https://containers.dev/implementors/spec / https://github.com/devcontainers/spec
- **Key Findings**:
  1. Dev Container Specification defines repeatable development environments via `devcontainer.json` — stored in `.devcontainer/devcontainer.json`.
  2. Supports **features** (reusable environment components), **lifecycle scripts** (postCreate, postStart, postAttach), and **customizations** (tool-specific settings).
  3. SSH key mounting: `"mounts": ["source=${localEnv:HOME}/.ssh,target=/home/vscode/.ssh,type=bind"]` — secure credential forwarding without embedding.
  4. Declarative secrets management as an advanced feature for handling credentials.
  5. Active development (latest push August 2025) with growing AI agent tooling support.
- **God-Mode Applicability**: The God-Mode prompt should include a `devcontainer.json` specification for the agent's execution environment. This ensures consistent tool availability (gh, docker, aws, vercel CLI), correct credential mounting (SSH keys, tokens via bind mounts), and reproducible environments. The devcontainer IS the God-Mode execution environment.

### Source 7.2: Docker Sandboxes for AI Coding Agents

- **URL**: https://docs.docker.com/ai/sandboxes/ / https://docs.docker.com/ai/sandboxes/architecture/
- **Key Findings**:
  1. Docker Sandboxes run AI coding agents in **lightweight microVMs** with private Docker daemons — hypervisor-level isolation (virtualization.framework on macOS, Hyper-V on Windows).
  2. Agents can execute commands, install packages, test code, and run Docker Compose stacks **safely** without compromising the host system.
  3. Agents **cannot access host containers, images, or credentials** stored outside their workspace.
  4. Each sandbox gets its own isolated filesystem, network, and process space.
  5. Supports Claude Code as a first-class agent with dedicated configuration documentation.
- **God-Mode Applicability**: Docker Sandboxes represent the gold standard for God-Mode execution isolation. The prompt should detect if running in a sandbox environment and adjust its security posture accordingly. In sandbox mode, the agent can be granted more autonomy (auto-approve file writes, terminal commands) because the isolation boundary protects the host. The prompt should specify sandbox configuration requirements.

### Source 7.3: AI Agent Sandboxing Guide — Firecracker, gVisor, and Isolation Strategies (2026)

- **URL**: https://manveerc.substack.com/p/ai-agent-sandboxing-guide
- **Key Findings**:
  1. Standard Docker containers with shared kernels are **no longer considered sufficient** for untrusted AI agent code in 2026.
  2. Spectrum of options: **Primitives** (Firecracker MicroVMs, gVisor, LiteBox), **Embeddable runtimes** (E2B, microsandbox), **Managed platforms** (Daytona, Modal, Northflank), **Hybrid** (Google Agent Sandbox for K8s).
  3. E2B provides cloud-based sandboxes with millisecond startup times — popular for quick agent execution contexts.
  4. Firecracker (used by AWS Lambda/Fargate) provides the strongest isolation with minimal overhead.
  5. GPU/data-heavy workloads need managed platforms like Modal or Northflank for resource allocation.
- **God-Mode Applicability**: The God-Mode prompt should be sandbox-runtime-aware. For development: Docker containers or Docker Sandboxes are sufficient. For production/untrusted inputs: Firecracker MicroVMs or E2B sandboxes provide stronger isolation. The prompt should detect the execution environment and apply appropriate security constraints.

---

## 8. Platform Integration Patterns for Multi-Tool AI Agents

### Source 8.1: Vercel — AI Agent Integration and AI Gateway

- **URL**: https://vercel.com/docs/agent-resources/integrations-for-agents / https://vercel.com/ai-gateway
- **Key Findings**:
  1. **AI Gateway**: Centralized endpoint for 100+ AI models with unified billing, automatic failover, and intelligent routing — eliminates per-provider API key management.
  2. Authentication: API Key or **OIDC Token** (auto-generated, valid 12 hours, obtained via `vercel env pull` after `vercel link`).
  3. AI SDK (2M+ weekly downloads): supports sequential/parallel processing, evaluation loops, orchestration, and MCP server integration via Next.js or Vercel Functions.
  4. Agent deployment via Vercel SDK, REST API, and CLI for programmatic management.
  5. Claude Code integration: configure `ANTHROPIC_BASE_URL` and `ANTHROPIC_AUTH_TOKEN` to route through Vercel AI Gateway.
- **God-Mode Applicability**: The God-Mode prompt should configure Vercel access for frontend/fullstack deployment workflows. Authentication via OIDC tokens (short-lived, auto-rotating) is preferred over static API keys. The prompt should include `vercel link` + `vercel env pull` as part of project bootstrap. Vercel's AI Gateway can serve as a unified model routing layer if multi-model access is needed.

### Source 8.2: Cloudflare — Agents SDK and Workers Deployment

- **URL**: https://developers.cloudflare.com/agents/ / https://developers.cloudflare.com/workers/ci-cd/external-cicd/github-actions/
- **Key Findings**:
  1. Cloudflare Agents SDK: stateful AI agents on Durable Objects with built-in SQLite state management, WebSocket connections, and HTTP request handling.
  2. Deployment via Wrangler CLI: `wrangler deploy` with `CLOUDFLARE_API_TOKEN` and `CLOUDFLARE_ACCOUNT_ID` as environment credentials.
  3. CI/CD integration: official `wrangler-action` for GitHub Actions with scoped API token authentication.
  4. Workers AI provides agentic-capable models including OpenAI GPT-OSS (120B), Llama 4 Scout with function calling.
  5. `wrangler login` for local dev, `auth token` commands for credential management in automation contexts.
- **God-Mode Applicability**: The God-Mode prompt should configure Cloudflare access for edge deployment, DNS management, and Workers/Pages publishing. Credentials: Cloudflare API Token (scoped to specific zones/accounts) stored in secrets vault, injected at runtime. The prompt should include Wrangler CLI setup and common deployment commands.

### Source 8.3: npm/PyPI — Trusted Publishing and Token Authentication

- **URL**: https://docs.npmjs.com/using-private-packages-in-a-ci-cd-workflow / https://docs.pypi.org/trusted-publishers/
- **Key Findings**:
  1. **PyPI Trusted Publishers**: Use OIDC to exchange short-lived identity tokens between CI services and PyPI. Tokens auto-expire after **15 minutes** — eliminating long-lived token risk.
  2. **npm Trusted Publishing**: Supported via GitHub Actions and GitLab CI/CD — eliminates token management entirely.
  3. Fallback: npm granular access tokens with read-only for installs, read/write for publishing. CIDR whitelisting available for IP restrictions.
  4. PyPI project-scoped API tokens for automated publishing when trusted publishing isn't available.
  5. `pypi-publish` GitHub Action automates Python package uploads to PyPI within CI/CD workflows.
- **God-Mode Applicability**: The God-Mode prompt should use **Trusted Publishing** for PyPI and npm wherever possible (OIDC-based, no long-lived tokens). For direct agent publishing (outside CI/CD), use project-scoped tokens with short expiry. The prompt should include publishing workflows: `python -m build && twine upload` for PyPI, `npm publish` for npm, with appropriate token injection.

### Source 8.4: OWASP — Top 10 for Agentic Applications (December 2025)

- **URL**: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/
- **Key Findings**:
  1. First industry-standard threat model for AI agents: **ASI01 (Agent Goal Hijack)**, ASI02 (Tool Misuse), **ASI03 (Identity & Privilege Abuse)**, ASI04 (Supply Chain Vulnerabilities), ASI05 (Unexpected Code Execution).
  2. ASI06 (Memory & Context Poisoning), **ASI07 (Insecure Inter-Agent Communication)**, ASI08 (Cascading Failures), ASI09 (Human-Agent Trust Exploitation), ASI10 (Rogue Agents).
  3. Real-world CVEs cited: EchoLeak (CVE-2025-32711) affecting Microsoft 365 Copilot, GitHub Copilot YOLO Mode (CVE-2025-53773).
  4. Prompt injection in agentic systems is uniquely dangerous: untrusted content in retrieved documents can contain hidden instructions that influence agent behavior.
  5. Developed with input from 100+ industry leaders — represents consensus on agentic security.
- **God-Mode Applicability**: CRITICAL security reference for the God-Mode prompt. Every God-Mode design decision should be validated against this threat model. Specific mitigations to include: (1) input validation on all external content before agent processing, (2) tool-level permissions with deny lists, (3) inter-agent communication authentication, (4) cascading failure circuit breakers, (5) human approval gates for destructive operations.

---

## Cross-Cutting Synthesis: God-Mode Prompt Architecture

Based on all 24 sources across 8 research topics, the "Agentic God-Mode" prompt should implement the following architecture:

### Layer 1: Identity & Credentials (Sources: 2.1, 2.2, 2.3, 2.4, 3.2, 8.4)
- **Never expose raw credentials** to the agent — use MCP servers, wrapper scripts, or SDK calls that handle auth internally
- **GitHub**: Fine-grained PATs scoped per-repo, 30-day expiry, `contents:write` + `pull_requests:write` + `issues:write` + `actions:read`
- **Cloud providers**: Short-lived session tokens (AWS STS, Cloudflare scoped API tokens, Vercel OIDC tokens)
- **Package registries**: Trusted Publishing (OIDC) for PyPI/npm, project-scoped tokens as fallback
- **Secrets backend**: 1Password Service Accounts (simple) or HashiCorp Vault (enterprise) with dedicated agent vaults
- **Process isolation**: Credential-free agent images, environment variable scrubbing, sandbox execution

### Layer 2: Tool Access via MCP (Sources: 1.1, 1.2, 1.3, 5.2, 6.1)
- **Docker MCP Gateway** as the orchestration layer — manages server lifecycles, credential injection, and governance logging
- Essential MCP servers: GitHub, Filesystem, PostgreSQL, Slack, Playwright/Puppeteer, Docker
- Dynamic server discovery via `mcp-find`/`mcp-add` for on-demand capability expansion
- Both Cursor (Settings > MCP) and Claude Code (`claude mcp add`) as primary agent hosts
- Standardized tool schemas prevent misuse (typed inputs/outputs, not freeform)

### Layer 3: Agent Orchestration (Sources: 4.1, 4.2, 4.3, 5.3)
- **Supervisor/Coordinator pattern**: God-Mode prompt IS the supervisor
- Specialist agents: coding (Claude Code / Cursor Agent), deployment (Vercel/Cloudflare CLI), testing (Playwright), infrastructure (Docker/Terraform)
- MCP as agent-to-agent communication bus — agents are tools to other agents
- Claude Code subagents for parallel execution, agent teams for distributed workflows
- Deterministic layer (auth, routing, credential injection) separated from agentic layer (reasoning, code generation)

### Layer 4: Execution Environment (Sources: 7.1, 7.2, 7.3)
- **devcontainer.json** defines the reproducible execution environment
- Docker Sandboxes (microVMs) for strong isolation in production
- SSH key mounting via bind mounts, not embedding
- Tool availability: gh, docker, aws, vercel, wrangler, npm, pip, twine pre-installed
- Lifecycle scripts for bootstrap: authenticate services, configure MCP servers, verify connectivity

### Layer 5: Security & Governance (Sources: 2.2, 2.3, 5.3, 8.4)
- OWASP Agentic Top 10 as the threat model baseline
- Hooks (Claude Code) / Rules (Cursor) for: blocking credential exfiltration, enforcing approval for destructive operations, auto-running security checks
- Behavioral monitoring for credential discovery/harvesting
- Audit trails: every tool call logged, every secret access recorded
- Human approval gates for: production deployments, public package publishing, infrastructure changes

---

## Source Index

| # | Topic | Source | URL |
|---|-------|--------|-----|
| 1.1 | MCP | Anthropic — Introducing MCP | https://anthropic.com/news/model-context-protocol |
| 1.2 | MCP | PulseMCP / FindMCP / MCP Directory | https://www.pulsemcp.com/servers |
| 1.3 | MCP | Docker MCP Toolkit & Gateway | https://docs.docker.com/ai/mcp-catalog-and-toolkit/mcp-gateway/ |
| 2.1 | Credentials | HashiCorp Vault AI Agent Auth | https://developer.hashicorp.com/validated-patterns/vault/ai-agent-identity-with-hashicorp-vault |
| 2.2 | Credentials | FINOS Credential Protection Framework | https://air-governance-framework.finos.org/mitigations/mi-23_agentic-system-credential-protection-framework.html |
| 2.3 | Credentials | Process-Scoped Credentials (dreamiurg.net) | https://dreamiurg.net/2026/02/11/reducing-attack-surface-for-ai-agents-process-scoped-credentials.html |
| 2.4 | Credentials | 1Password Service Accounts for AI | https://blog.1password.com/service-accounts-sdks-agentic-ai |
| 3.1 | GitHub | GitHub Agentic Workflows | https://github.blog/ai-and-ml/automate-repository-tasks-with-github-agentic-workflows |
| 3.2 | GitHub | Fine-Grained PAT Permissions | https://docs.github.com/en/rest/authentication/permissions-required-for-fine-grained-personal-access-tokens |
| 3.3 | GitHub | GitHub CLI Reference | https://cli.github.com/manual/gh_help_reference |
| 4.1 | Orchestration | Multi-Agent AI Systems 2026 | https://aiworkflowlab.dev/article/building-multi-agent-ai-systems-2026-architecture-patterns-mcp-production-orchestration |
| 4.2 | Orchestration | Advanced MCP Orchestration | https://www.getknit.dev/blog/advanced-mcp-agent-orchestration-chaining-and-handoffs |
| 4.3 | Orchestration | Orchestral AI Framework | https://arxiv.org/abs/2601.02577 |
| 5.1 | Claude Code | Headless/Programmatic Mode | https://docs.anthropic.com/en/docs/claude-code/headless |
| 5.2 | Claude Code | MCP Integration | https://code.claude.com/docs/en/mcp |
| 5.3 | Claude Code | Hooks, Skills, Subagents, Teams | https://code.claude.com/docs/en/features-overview |
| 6.1 | Cursor | MCP Configuration | https://cursor.com/docs/context/mcp |
| 6.2 | Cursor | Browser Automation MCP | https://cursor.directory/mcp/browser-automation |
| 6.3 | Cursor | Agent Overview | https://cursor.com/agents |
| 7.1 | Infrastructure | Dev Container Specification | https://containers.dev/implementors/spec |
| 7.2 | Infrastructure | Docker Sandboxes | https://docs.docker.com/ai/sandboxes/ |
| 7.3 | Infrastructure | Sandboxing Guide 2026 | https://manveerc.substack.com/p/ai-agent-sandboxing-guide |
| 8.1 | Platforms | Vercel AI Gateway & Agent Integration | https://vercel.com/docs/agent-resources/integrations-for-agents |
| 8.2 | Platforms | Cloudflare Agents SDK & Workers | https://developers.cloudflare.com/agents/ |
| 8.3 | Platforms | npm/PyPI Trusted Publishing | https://docs.pypi.org/trusted-publishers/ |
| 8.4 | Security | OWASP Agentic AI Top 10 | https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ |
