A deterministic, cross-platform engineering suite and operating framework for leading AI coding assistants (OpenAI Codex, Antigravity / Gemini CLI, Claude Code, and Modern Cursor IDE (.mdc)) across Linux, macOS, and Windows.
The bundle unifies 16 ironclad operational rules (Prime Directives), 36 modular skills (agentskills.io standard), and an autonomous Two-Tier Cognitive Architecture powered by a custom FastMCP Sub-Worker Delegation Bridge (chinese-worker) and Aider Headless Engine. Expensive frontier models (Gemini 3.7 Pro, Claude 3.7 Sonnet) act as high-level architects and orchestrators, delegating repetitive bulk coding tasks (scaffolding 2500+ LOC, TDD unit test generation, JSDoc/docstrings, strict type migrations) to 100% free, mega-context models (MiniMax M3 1M Context, NVIDIA Nemotron 550B MoE, Zhipu GLM-5.2) in sandboxed Git Worktrees with Zero Context Bleed and Zero Token Cost ($0.00).
If you are using an AI coding assistant (Claude Code, OpenAI Codex, Cursor Composer, Gemini CLI / Antigravity, Cline, Roo Code, OpenCode, Aider), simply paste this prompt:
Clone https://github.com/Gzyms69/agent-setup-bundle.git and install the full AI engineering operating system for me following AGENTS.md in the repo.
Sklonuj https://github.com/Gzyms69/agent-setup-bundle.git i zainstaluj całe środowisko inżynieryjne według instrukcji w AGENTS.md.
Your AI assistant will read AGENTS.md, auto-detect your operating system (Windows, macOS, or Linux), execute the native installer, validate suite integrity, and immediately adopt the Senior AI Pair Programmer persona.
Traditional single-model AI pair programming ("Just write this feature") fails on complex production codebases due to four fundamental problems:
- Economic Inefficiency: Burning expensive reasoning tokens on repetitive boilerplate, large mock suites, or syntax formatting.
- Context Window Degradation: Dumping thousands of lines of raw logs and intermediate diffs into the primary reasoning context.
- Simulation & Mocking Traps: Writing fake stubs or untested boilerplate without running verification suites.
- Monolithic Spaghetti Code: Coupling business logic to UI frameworks and creating unmaintainable "god files".
agent-setup-bundle solves this through a Two-Tier Cognitive Architecture:
flowchart TD
subgraph Tier1_Brain ["Tier 1: High-Reasoning Brain & Orchestrator ($$)"]
Gemini["Gemini 3.7 Pro / Antigravity CLI"]
Claude["Claude 3.7 Sonnet / Claude Code"]
Cursor["Cursor IDE / Composer"]
Codex["OpenAI Codex CLI"]
SpecGate["Spec-Driven Development & 5-Axis Code Review Gate"]
Gemini & Claude & Cursor & Codex --> SpecGate
end
subgraph Tier2_FastMCP ["Tier 2: FastMCP Sub-Worker Bridge (chinese-worker) ($0.00)"]
Router["Intelligent Task Router (Keyword & Task Affinity)"]
SkillsInj["Dynamic Skill Injector (--read ~/.agents/skills/*/SKILL.md)"]
WorktreeMgr["Git Worktree Sandbox (.git/worktrees_active/<task-id>)"]
AiderEngine["Aider Headless Engine (Diff Mode)"]
SelfHealing["Self-Healing Quality Loop (--auto-test)"]
SpecGate -->|MCP Tool: worker_run_task / worker_generate_tests| Router
Router --> WorktreeMgr --> AiderEngine
Router --> SkillsInj --> AiderEngine
AiderEngine --> SelfHealing
end
subgraph Model_Pool ["Free Tier & High-Throughput Model Pool"]
M3["MiniMax M3 (1M Ctx, 65k Out) -> Mega Scaffolding & Fullstack"]
N550["NVIDIA Nemotron 550B MoE -> Low-Level, C++, ASM & Math"]
GLM5["Zhipu GLM-5.2 (LiveCodeBench 74-85%) -> Bugfix & Refactor"]
NLight["Nemotron 3.5 Lightning -> Rapid TDD Unit Tests"]
GLM4["Zhipu GLM-4-Flash PAAS -> 100% Guaranteed Direct Fallback"]
Router --> Model_Pool
Model_Pool --> AiderEngine
end
SelfHealing -->|Zero Context Bleed: 3-line Structured Report| SpecGate
SpecGate -->|Inspection & Approval| Merge["worker_merge_branch"]
- Tier 1 (Brain & Orchestrator): Frontier models (Gemini 3.7, Claude 3.7) plan architecture, conduct Spec-Driven Development, and enforce the 5-Axis Code Review Gate.
- Tier 2 (Grunt Worker & Token Factory): FastMCP (
chinese-worker) delegates bulk implementation tasks to free, specialized models operating inside sandboxed Git Worktrees with an automated Self-Healing Quality Loop (--auto-test).
Before executing file discovery (grep_search, find_by_name, view_file) or making any code changes, agents operating under this system MUST evaluate and activate skills through the 4-Phase Pre-Flight Skill Gate:
flowchart LR
P0["Phase 0: Cartography Gate"] --> P1["Phase 1: Planning & Orchestration"]
P1 --> P2["Phase 2: Domain Specialists"]
P2 --> P3["Phase 3: QA & Review Gate"]
| Phase | Skill Name | Trigger & Responsibility |
|---|---|---|
| Phase 0 | skill-codebase-onboarding |
Mandatory first step for exploring or onboarding any unmapped repository. |
| Phase 0 | spec-miner |
Reverse-engineering legacy, undocumented, or poorly structured codebases. |
| Phase 1 | spec-driven-development |
Tasks >15 min, >3 files, architectural decisions, or /plan invocation. |
| Phase 1 | skill-context-engineering |
Attention budget curation, log offloading to ./scratch/, context compaction. |
| Phase 1 | skill-master-orchestrator |
Multi-agent swarm coordination, Task DAG decomposition, barrier synchronization. |
| Phase 1 | skill-monorepo-architect |
Monorepo structure management (PNPM, Turborepo, UV workspaces). |
| Phase 1 | skill-plugin-architecture |
Extensible microkernel systems, dynamic toolkits, plugin discovery. |
| Phase 1 | skill-web-architecture |
Full-stack web architectural standards, module boundaries, API contracts. |
| Phase 2 | skill-frontend-architect |
Next.js 15+ App Router, RSC, Client Island boundaries, WCAG 2.1/2.2 AA. |
| Phase 2 | skill-design-engineering |
Motion animations (motion.dev), CSS Subgrid, Container Queries, 21st.dev UI. |
| Phase 2 | skill-creative-design |
Art direction, aesthetics, Fontjoy typography math, OKLCH color physics. |
| Phase 2 | skill-backend-architect |
Backend architecture, database schemas, API contracts, zero-downtime migrations. |
| Phase 2 | skill-mcp-builder |
Model Context Protocol server development (FastMCP, TypeScript SDK, stdio/SSE). |
| Phase 2 | skill-low-level-programming |
C/C++, Rust, Assembly, byte manipulation, memory layout, bitwise arithmetic. |
| Phase 2 | c-cpp-systems |
Low-level C/C++ memory safety, pointers, manual RAII, ASan/UBSan sanitizers. |
| Phase 2 | wasm-emscripten |
C/C++ to WebAssembly compilation, Emscripten runtime bridging, HEAP views. |
| Phase 2 | retro-emulation-engineering |
Retro emulator architecture, hardware coprocessor simulation (CPU/RSP/RDP). |
| Phase 2 | skill-emulator-wasm |
WebAssembly retro emulators, WebGL rendering, Web Audio sync, save states. |
| Phase 2 | skill-ai-ml |
LLM integrations (Gemini, OpenAI, Anthropic), RAG pipelines, vector DBs. |
| Phase 2 | skill-data-science |
Data science workflows, exploratory data analysis (EDA), ingestion pipelines. |
| Phase 2 | skill-data-analysis |
Statistical methodology, hypothesis testing, anomaly detection, claim validation. |
| Phase 2 | skill-graph-analytics |
Graph databases (Neo4j), Cypher queries, topology analysis, Graph Data Science. |
| Phase 2 | skill-graphics-webgl |
2D/3D graphics, Three.js, WebGL shader optimization, Canvas rendering. |
| Phase 2 | skill-stealth-scraping |
Anti-bot evasion, stealth automation, TLS/JA3 fingerprints, reverse API engineering. |
| Phase 2 | skill-osint-engineering |
OSINT intelligence pipelines, standardized entity graphs, pivoting engines. |
| Phase 2 | skill-system-diagnostics |
Hardware/OS/kernel diagnostics, log analysis, SRE root-cause debugging. |
| Phase 2 | skill-devops-cloud |
Docker containerization, CI/CD pipelines, Cloud Run checklists, Kubernetes. |
| Phase 2 | skill-research |
Academic and technical literature research with multi-source verification. |
| Phase 2 | skill-resume-tailor |
AI developer resume/CV architect (Google XYZ formula, Harvard Tech standard). |
| Phase 2 | marketing-copywriting |
Conversion-focused copywriting, value propositions, CTA engineering. |
| Phase 2 | avoid-ai-writing |
Strict audit and rewriting protocol eliminating AI writing clichés and fluff. |
| Phase 2 | seo-optimization-and-audit |
SEO audit, metadata, head tags, Core Web Vitals optimization. |
| Phase 2 | skill-web-performance |
Universal web performance engineering, Lighthouse 100/100, runtime tracing. |
| Phase 3 | skill-qa-engineer |
Mandatory Phase 3 QA Gate, TDD Red-Green discipline, TypeScript Safety Gate. |
| Phase 3 | skill-code-review |
Mandatory 5-axis review (Correctness, Readability, Architecture, Security, Perf). |
| Phase 3 | doubt-driven-development |
Adversarial verification gate challenging false confidence before assertions. |
All operational rules reside in ~/.agents/rules/ and are enforced across all platform manifests:
zero-speculation.md: Total ban on guessing hardware specs, package versions, API endpoints, or error causes. Verify via live commands or web search.command-verification.md: Mandatory verification of CLI tool outcomes before proceeding.env-integrity.md: Pre-flight environment audit before modifying codebase configuration.error-triage.md: Strict diagnostic triage sequence: Docs -> Web -> Source Code.full-log-reporting.md: Zero truncated logs when diagnosing failures.problem-isolation.md: Surgical problem isolation without collateral workspace mutation.subagent-economy.md: Subagent model routing economy (flash_lite->flash->pro) and Chinese worker delegation.system-identity.md: Real-time hardware identity and OS verification template.systemic-excellence.md: Prohibition of symptomatic patches, workarounds, or defensive masking.context-engineering.md: Strict 100-line / 5 KB offloading to./scratch/and Attention U-Curve protection.modular-architecture.md: Clean/Hexagonal architecture boundaries and anti-god-file constraints.planning-and-document-integrity.md: Stateful 3-state planning machine,Iteration Delta, and Discovered Facts Lock.session-handoff.md: Lossless session transition viaNEXT_SESSION_PLAN.mdand clean SSOT handoff prompts.skill-orchestration.md: Universal 4-Phase Pre-Flight Skill Gate activation protocol.mcp-master-playbook.md: Standardized Model Context Protocol tool invocation guidelines.mempalace-discovery.md: Knowledge graph querying and memory retrieval protocol.
The bundle includes a native, high-throughput Model Context Protocol server implemented in Python using FastMCP (scripts/worker_mcp.py).
| Tool Name | Parameters | Description |
|---|---|---|
worker_run_task |
instruction, editable_files, readonly_files, skills, task_type, profile, auto_test, test_cmd, use_worktree |
Executes an autonomous coding task in a dedicated Git Worktree with dynamic skill injection and self-healing test loop. |
worker_generate_tests |
target_file, test_file, test_framework, skills, profile |
Generates comprehensive TDD unit tests (pytest, vitest, jest, cargo) with edge case mocking. |
worker_generate_docs |
target_files, doc_type, profile |
Generates JSDoc, docstrings, or markdown guides preserving exact code functionality. |
worker_batch_refactor |
target_files, instruction, readonly_files, skills, profile |
Executes mass refactoring or strict type safety upgrades across multiple files. |
worker_continue_task |
task_id, feedback |
Continues refining changes within an existing active worktree sandbox. |
worker_get_diff |
task_id |
Returns the clean unified git diff generated by the worker for inspection. |
worker_merge_branch |
task_id, target_branch |
Merges the verified task worktree into the main working tree and cleans up. |
worker_discard_branch |
task_id |
Deletes and cleans up a rejected task worktree sandbox. |
worker_status |
(none) | Lists all active worker worktrees and recent task logs. |
{
"task_affinity": {
"scaffold": "minimax-m3",
"fullstack": "minimax-m3",
"low_level": "nemotron-550b",
"binary": "nemotron-550b",
"algorithms": "nemotron-550b",
"bugfix": "glm-5.2",
"refactor": "glm-5.2",
"tests": "nemotron-lightning",
"unit_tests": "nemotron-lightning",
"docs": "glm-5.2",
"fast": "cohere-code"
}
}- MiniMax M3 Free (
openrouter/minimax/minimax-m3:free): Tier 0 Chinese Flagship with 1,048,576 Context and 65,536 Max Output Tokens. Best for fullstack scaffolding and large multi-file codebases. - NVIDIA Nemotron 3 Ultra 550B MoE (
openrouter/nvidia/nemotron-3-ultra-550b-a55b:free): 550 Billion parameter MoE with 1M context. Specialized in low-level systems (C/C++, Rust, Assembly, byte manipulation, and mathematical algorithms). - Zhipu GLM-5.2 Free (
openrouter/z-ai/glm-5.2:free): Frontier coding model with LiveCodeBench 74-85% and SWE-bench 68.2%. Specialized in bugfixes, refactoring, and diff generation. - NVIDIA Nemotron 3.5 Lightning (
openrouter/nvidia/nemotron-3.5-lightning:free): Ultra-fast MoE for rapid TDD unit test creation. - Zhipu GLM-4-Flash PAAS (
openai/glm-4-flash): Direct BigModel PAAS integration serving as 100% guaranteed fallback if OpenRouter free endpoints hit temporary rate limits.
The installer provisions a terminal CLI symlink at ~/.local/bin/worker:
# Diagnostic health check (validates packages, profiles, API keys)
worker check
# Interactive coding session with MiniMax M3
worker chat minimax-m3 --skills skill-frontend-architect src/App.tsx
# Batch instruction execution with automatic model routing
worker run "Refactor database queries to use parameterized prepared statements" --skills skill-backend-architect -f src/db.tsThe bundle provisions unified MCP server configurations across Gemini CLI (config/mcp_config.json), Cursor (config/cursor_mcp.json), and Claude Code:
| MCP Server | Provider / Package | Purpose |
|---|---|---|
chinese-worker |
scripts/worker_mcp.py (FastMCP) |
High-throughput autonomous sub-worker delegation engine ($0.00). |
github |
@modelcontextprotocol/server-github |
Remote GitHub API operations (PRs, issues, code search, reviews). |
chrome-devtools |
chrome-devtools-mcp@latest |
Headless Chrome browser automation and DOM inspection. |
puppeteer |
@modelcontextprotocol/server-puppeteer |
End-to-end web testing and screenshot capture. |
lighthouse-mcp |
@danielsogl/lighthouse-mcp |
Web performance, Core Web Vitals, and accessibility audits. |
postgres |
@modelcontextprotocol/server-postgres |
PostgreSQL schema introspection and query analysis. |
sqlite |
@modelcontextprotocol/server-sqlite |
Local SQLite database inspection. |
docker |
@modelcontextprotocol/server-docker |
Container lifecycle management and log inspection. |
firecrawl |
firecrawl-mcp |
Web scraping, crawling, and clean Markdown extraction. |
ast-grep |
@ast-grep/mcp |
Structural AST search and code pattern matching. |
mempalace |
mempalace |
Long-term memory palace, AAAK knowledge graph and diary storage. |
git clone https://github.com/Gzyms69/agent-setup-bundle.git
cd agent-setup-bundle
chmod +x install.sh
./install.sh --allgit clone https://github.com/Gzyms69/agent-setup-bundle.git
cd agent-setup-bundle
powershell -ExecutionPolicy Bypass -File .\install.ps1 -Allpython3 install.py --all--codex/-Codex: Install only OpenAI Codex environment (~/.codex/).--gemini/-Gemini: Install only Antigravity / Gemini CLI environment (~/.gemini/).--claude/-Claude: Install only Claude Code environment (~/.claude/).--cursor/-Cursor: Install only Cursor IDE rules (~/.cursor/rules/).
Every component in this repository is strictly validated by automated test suites before deployment:
# 1. Run the master suite integrity validator (16 rules, 36 skills, 4 platforms, worker configs)
python3 scripts/validate_suite.py
# 2. Run the Worker MCP & CLI unit test suite
python3 scripts/tests/test_worker.py
# 3. Run the Sub-Worker environment diagnostics
python3 scripts/worker_cli.py checkagent-setup-bundle/
├── AGENTS.md # Master repository blueprint & AI installer instructions
├── README.md # Master technical documentation & cross-platform guide
├── CAREER_KNOWLEDGE_BANK.md # Master SSOT for career portfolios, metrics & STAR+R cases
├── PROMPT_FOR_AI.md # Universal bootstrap prompts
├── llms.txt # Semantic summary for web-enabled LLM agents
├── install.sh # Native Bash installer (Linux / macOS)
├── install.ps1 # Native PowerShell installer (Windows)
├── install.py # Universal Python 3 installer (All OSes)
├── core/ # Platform manifests (CODEX.md, GEMINI.md, CLAUDE.md, cursor)
├── rules/ # 16 Universal Operational Rules (~/.agents/rules/)
├── skills/ # 36 Modular Skills (~/.agents/skills/)
├── templates/
│ ├── AGENTS.md # Project-level starter template
│ ├── CONVENTIONS.md # Universal coding conventions for sub-workers
│ └── .aider.conf.yml.template # Universal Aider configuration template
├── config/
│ ├── worker_profiles.json # Sub-worker model routing profiles & context bounds
│ ├── .aider.model.settings.yml # Aider model behavioral settings & diff formats
│ ├── .aider.model.metadata.json # Aider token limit overrides (1M context / 65k output)
│ ├── mcp_config.json # Gemini CLI MCP configuration template
│ ├── settings.json # Gemini CLI general settings
│ ├── codex_config.toml # OpenAI Codex configuration template
│ └── cursor_mcp.json # Cursor IDE MCP configuration template
├── policies/ # MCP tool planning policies
└── scripts/
├── worker_mcp.py # FastMCP server for autonomous sub-worker delegation
├── worker_cli.py # Developer CLI companion (worker)
├── tests/
│ └── test_worker.py # Unit test suite for worker ecosystem
└── validate_suite.py # Quality assurance test suite
MIT License. Designed and maintained by Gzymson for autonomous, deterministic AI pair programming.