Verifies repository baseline test passing state and available memory/GPU resources to ensure safe mission execution.
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Before accepting or starting a large architectural mission, you MUST run this health check to confirm you are not iterating on top of an already broken baseline.
Identify the primary test command for this workspace. Use the command matching the ecosystem:
npm run test or pnpm test:allcargo testpytestRule: If the tests FAIL, immediately halt your mission and inform the user. Do not proceed until the existing codebase compiles and tests pass.
Ensure the host machine has adequate capacity to compile your work or run complex LLMs.
Windows PowerShell Examples: Check available memory and CPU cores:
systeminfo | findstr /C:"Total Physical Memory" /C:"Available Physical Memory"
wmic cpu get NumberOfCores,NumberOfLogicalProcessors /format:list
If your mission explicitly governs a machine learning or GPU-heavy task, check for an NVIDIA GPU via:
nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader
If resources look extremely constrained (e.g., <2GB Free RAM), halt and warn the user before proceeding to file modifications.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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