Integrate AegisAI gateway before tool side effects (notify, publish, deploy). Use when adding Slack/Telegram/WhatsApp notify, content publish, or any irreversible external action in VAP, AegisLoop, or ai-content-factory.
Apply Karpathy agentic engineering discipline: think before coding, minimal diffs, surgical changes, test-verified completion. Use for any implementation task in vpeetla-ai repos when quality bar matters.
Maps tasks to the vpeetla-ai 6-layer reference stack (VAP, AegisAI, Enterprise RAG, AegisLoop, Content Factory, LoopForge). Use when choosing which repo to change, designing integrations, or explaining architecture.
Interview the user about a plan or design until decisions are resolved, while updating CONTEXT.md terms and noting ADR candidates. Use before large features, cross-repo integrations, or when requirements feel vague.
Implement ODAEU harness loops, RAG evolve tuning, and procedural memory in LoopForge or similar systems. Use when building self-improving agents, eval gates, MCP tool bridges, or RAG version trees.
Write ADRs, case studies, and portfolio copy for ai-architecture-portfolio and venkat-ai-portfolio. Use when documenting decisions, updating ecosystem pages, or syncing GitHub profile README with live demos.
Implement access-aware RAG in Enterprise RAG or VAP: hybrid retrieval, rerank, citations, AegisAI HITL for sensitive chunks. Use when tuning retrieval, adding Qdrant adapter, or wiring enterpriseragplatform.
Run or extend LoopForge repo-fix workflow: clone GitHub repo, pytest scan, LangGraph patch loop, branch loopforge/fix-{id}, open PR never push main. Use when fixing bugs across repos, extending workspace/git tools, or debugging /api/repo-fix.
Bootstrap a vpeetla-ai repo for org skills: issue tracker, triage labels, docs paths, and CONTEXT.md placement. Use when onboarding a new repo, first clone, or when the user says setup skills or configure agent workflow.
Test-driven development for agent systems: red-green-refactor on graphs, mocked LLM fixtures, pytest-asyncio, trace assertions. Use when adding agent nodes, fixing loop bugs, or building pattern repos.
Integrate AegisAI gateway before tool side effects (notify, publish, deploy). Use when adding Slack/Telegram/WhatsApp notify, content publish, or any irreversible external action in VAP, AegisLoop, or ai-content-factory.
Apply Karpathy agentic engineering discipline: think before coding, minimal diffs, surgical changes, test-verified completion. Use for any implementation task in vpeetla-ai repos when quality bar matters.
Maps tasks to the vpeetla-ai 6-layer reference stack (VAP, AegisAI, Enterprise RAG, AegisLoop, Content Factory, LoopForge). Use when choosing which repo to change, designing integrations, or explaining architecture.
Interview the user about a plan or design until decisions are resolved, while updating CONTEXT.md terms and noting ADR candidates. Use before large features, cross-repo integrations, or when requirements feel vague.
Implement ODAEU harness loops, RAG evolve tuning, and procedural memory in LoopForge or similar systems. Use when building self-improving agents, eval gates, MCP tool bridges, or RAG version trees.
Write ADRs, case studies, and portfolio copy for ai-architecture-portfolio and venkat-ai-portfolio. Use when documenting decisions, updating ecosystem pages, or syncing GitHub profile README with live demos.
Implement access-aware RAG in Enterprise RAG or VAP: hybrid retrieval, rerank, citations, AegisAI HITL for sensitive chunks. Use when tuning retrieval, adding Qdrant adapter, or wiring enterpriseragplatform.