Project Title Autonomous AI Infrastructure Operating System with Multi-LLM Cross-Check, Skill Governance & Real-Time Dashboard ⸻ Project Summary We are building a fully autonomous AI Infrastructure Control System for RTX (Ubuntu), controlled from macOS, using OpenClaw Skill architecture and strict multi-LLM governance. This system must: • Operate entirely via Skills (no raw bash outside framework) • Generate multiple solution strategies before execution • Cross-check each strategy and script using GPT and DeepSeek • Perform deterministic safety validation before execution • Execute via secure sandbox • Auto-repair failures • Stream terminal output in real time • Integrate with Kestra, Codex CLI, and Xcode • Provide a governance-level real-time dashboard This is not a script. This is an AI Infrastructure Control Plane. ⸻ Mandatory Multi-LLM Cross-Check Engine Before any execution: 1. Generate minimum 3 solution strategies 2. Validate each via: • GPT (reasoning validation) • DeepSeek (technical validation) 3. Apply deterministic rule engine 4. Compute risk model per strategy 5. Select optimal strategy via weighted consensus 6. Execute only after approval threshold met After execution: • If failure detected: • Parse logs • Classify error • Generate repair strategy • Re-run full multi-LLM cross-check • Retry (configurable cycle limit) No blind execution allowed. ⸻ Solution Strategy Engine The system must: • Generate multiple solution approaches • Simulate infrastructure impact • Calculate: • Risk score • Downtime probability • Reversibility index • Present rejected strategies • Adapt strategy if execution fails This engine must be integrated into Task Tree and Dashboard. ⸻ OpenClaw Skill Architecture (JSON/YAML Based) All actions must be implemented as Skills. Each Skill must include: • JSON or YAML contract • Risk profile • Permission declaration • Input/output schema • Dry-run support • Rollback support • Consensus requirement flag • Simulation requirement flag System must support: • Skill generation engine • Skill installer/remover • Skill dependency graph • Skill registry with versioning • Sandbox testing before activation ⸻ Docker & Infrastructure Skills Required skills: • Docker orchestration • Port conflict detection & auto-resolution • Service repair • Stack deploy/rollback • Network recovery • Volume backup & restore Must support self-healing behavior. ⸻ Codex CLI Integration Codex must be integrated as an autonomous Skill: Capabilities: • Code generation • Refactoring • CI error fixing • Script improvement • Repo-aware modifications Codex must also pass multi-LLM validation before deployment. ⸻ Xcode Integration System must include an Xcode Skill capable of: • Creating/modifying Swift projects • Generating files • Parsing build logs • Classifying compile errors • Applying fixes via strategy engine • Running tests • Revalidating builds All fixes must pass GPT + DeepSeek consensus before applying. ⸻ Kestra Integration System must integrate with Kestra orchestration: • Create / update / delete flows • Monitor execution • Trigger flows from Skills • Insert AI decision logic into Kestra workflows • Sync Task Tree with Kestra execution state Kestra must become part of AI decision DAG. ⸻ Persistent Terminal Control (Mac RTX) System must: • Maintain persistent SSH sessions • Stream stdout/stderr in real time • Capture structured logs • Provide bi-directional command channel • Eliminate manual copy-paste of logs Terminal execution must be observable inside Dashboard. ⸻ Governance Dashboard (Mandatory) Web-based real-time AI Control Console. Must include: 1. Global System Panel • Health score • Active jobs • Repair cycles • Risk index • Consensus stability indicator 2. DAG-Based Task Tree Each job visualized as dependency graph: • Snapshot • Strategy generation • Scenario cross-check • Execution • Repair loop • Validation • Final state Each node displays: • Status • Risk score • LLM confidence • Execution time • Rollback availability 3. Multi-LLM Consensus Viewer Per action: • GPT verdict • DeepSeek verdict • Risk engine result • Final decision • Confidence % 4. Repair Loop Monitor • Error classification • Repair attempt count • Strategy switching history 5. Digital Twin Viewer Before/after diff: • Containers • Ports • Services • Env vars • Config files 6. Policy & Governance Panel Adjust: • Risk thresholds • Consensus level (2/3 vs 3/3) • Dry-run mode • Strict security mode No raw log reading should be required to understand state. ⸻ Digital Infrastructure Twin Before and after execution: • Docker graph snapshot • Port map • Service dependency map • Process tree • Environment diff Unexpected changes → rollback trigger. ⸻ Chaos Testing & Self-Healing System must survive: • Broken docker network • Port conflicts • Killed containers • Permission denied • Corrupted configs And auto-repair via strategy engine. ⸻ Technical Stack Requirements • Advanced Ubuntu systems knowledge • Docker / Compose mastery • SSH automation • Python orchestration • LLM API orchestration • WebSocket real-time dashboard • DAG workflow modeling • Security-first execution sandbox design • DevOps + ML hybrid experience Senior / Architect level only. ⸻ Deliverables • Complete autonomous system • One-shot installer • Skill registry framework • Strategy engine • Multi-LLM consensus engine • Dashboard • Kestra integration • Codex integration • Xcode integration • Chaos test suite • Documentation • Demo video • Source repository ⸻ Timeline 8–12 weeks realistic ⸻ Budget- 800$