backend-principle-eng-python-ml-pro-max
Backend Principle Eng Python ML Pro Max
Principal-level guidance for Python AI/ML backends, training pipelines, and inference services. Emphasizes data integrity, reproducibility, and production reliability.
When to Apply
- Designing or refactoring ML training or inference systems
- Reviewing ML code for data leakage, evaluation quality, and reliability
- Building feature pipelines, batch scoring, or real-time serving
- Incident response for model regressions or data drift
Priority Model (highest to lowest)
More from prakharmnnit/skills-and-personas
backend-principle-eng-cpp-pro-max
Principal backend engineering intelligence for C++ systems and performance-critical services. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, memory safety, latency, reliability, observability, scalability, operability.
88backend-principle-eng-java-pro-max
Principal backend engineering intelligence for Java services and distributed systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.
14lecture-alchemist
Transform raw lecture transcripts (Zoom, YouTube, etc.) into structured, retention-optimized study notes. Use when the user provides a lecture transcript, class recording text, or asks to process/convert lecture notes. Handles WebDev, AI/ML, Web3, DSA, and general tech domains. Produces hierarchical topic breakdowns, cleaned code artifacts, intuition builders, flashcards, spaced repetition plans, and actionable study materials. Trigger phrases: 'process this transcript', 'convert lecture to notes', 'lecture notes', 'transcript to study material', 'Lecture Alchemist'.
14backend-pe
Distinguished Principal Engineer backend/system architecture skill. Use when the user demands "BackendPE", "Supermode", "Antigravity", or requests high-performance, unlimited-context, world-class backend and distributed systems design. This skill maximizes depth, rigor, and production readiness.
13constellation-team
Coordinate a cross-functional star-team workflow (Product Manager, Principal Engineer, Backend, Frontend, QA/Security, DevOps) with mandatory architecture and code-review checkpoints. Use when a request needs end-to-end product delivery, multi-role collaboration, or explicit role-based outputs (PM/PE/Backend/Frontend/QA/DevOps), or when the user asks for "star team", "cross-functional", "full lifecycle", or "multi-role" planning.
13transcribe-refiner
Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
13