ayush488-glitch

Anti-slop agentic engineering co-pilot. Teaches the Research-Plan-Implement (RPI) workflow, context management, quality gates, per-agent isolation, and anti-slop patterns for building software with AI coding agents. Produces agent-workflow.md or project…

Full software engineering and ML-specific code review co-pilot. Reviews Python code for quality, security, testing, type safety, and ML-specific issues including data leakage, training-serving skew, feature engineering smells, and reproducibility. Produces…

System design co-pilot covering both general distributed systems and ML-specific infrastructure. Guides users through API design, database design, scalability, reliability, ML serving patterns, feature stores, training pipelines, and ML platform architecture.…

Production-grade MLOps co-pilot for tabular data. Guides users end-to-end from business problem through system design, implementation, deployment, and monitoring. Adapts dynamically to the user's specific problem, dataset, constraints, and chosen…

Deep-dive MLOps architecture design for tabular data. Walks through all 9 sub-phases of system design: full pipeline explanation (10 stages, 5 pipelines, maturity levels), data plan, feature plan, training plan, deployment plan, monitoring plan, versioning…

Deep-dive data foundation and feature engineering for tabular ML. Covers project setup, data loading with validation, EDA, and preprocessing (null handling, scaling with formulas, categorical encoding with target encoding smoothing, training-serving skew…

Deep-dive deployment, monitoring, and production hardening for tabular ML. Covers drift detection (data vs concept drift, KS/Chi-squared/PSI/Wasserstein with thresholds), deployment strategies (shadow/canary/blue-green/A-B), four-layer monitoring ladder,…

Deep-dive problem framing for tabular ML. Guides users through the six-word ML suitability test, three legitimate paths (Build ML / Rules / Not Now), problem statement template, metric ladder, seven discovery questions, and six forcing questions. Produces…

Use when designing, building, evaluating, or operating AI agent systems in production — agent architecture, orchestration patterns, RAG pipelines, evaluation, observability, guardrails, and domain-specific deployment.

Use when starting, reviewing, or building any production-grade software or AI-native system. Orchestrates the full 20-phase production lifecycle, routes to the right domain skills at each phase, and guides the agent through every engineering layer from…

Use when designing, evaluating, or debugging data-intensive systems — storage engines, replication, partitioning, transactions, distributed consistency, batch/stream processing, or encoding.

Use when designing, implementing, or debugging distributed services — architecture choices, consistency trade-offs, fault tolerance, coordination, naming, and security for multi-node systems.

Use when a coding agent must make architectural, quality, or design decisions during implementation — calibrating trade-offs, communicating honestly, and maintaining integrity across iterations.

Use when making dependency management decisions, drawing component boundaries, applying SOLID principles, designing layered architectures, or evaluating whether a structural decision keeps options open vs. locks them in.

Use when designing, reviewing, or building production systems — architecture decisions, integration points, stability patterns, capacity planning, observability, and operations.

Security engineering principles: threat modeling, cryptography, access control, privacy, secure development, physical/operational security, and economics of trust. Covers adversarial thinking, protocol design, tamper resistance, and assurance.

Walks a student through designing a complete 5-layer intelligent system for any real business problem. Use this skill whenever a student wants to go beyond building a model and design the full system around it — the decision layer, the policy, the action…

Generates a complete thinking document for any unsupervised ML algorithm in the exact style and depth of the Session 2 "Unsupervised Learning + K-Means" teaching document. Use this skill whenever a student wants to deeply understand an unsupervised ML…

Generates a complete thinking document for any supervised ML algorithm in the exact style of the "Regression & Supervised Learning: The Evolutionary Thinking Framework" session document. Use this skill whenever a student wants to deeply understand a…

Create a rich, self-contained interactive HTML explanation of a code change, diff, branch, or pull request. Use when the user wants to understand the background, intuition, implementation, data flow, diagrams, or quiz-based reinforcement for a software…

Use when starting a brand-new project, adopting an unfamiliar repo, or setting up AI-native loop-based development. Scaffolds the .genesis/ spine (context graph, definition-of-done, plan, rolling state, loops), seeds it from the agentic-swe-kit, and primes…

Step-by-step coach for the Week 2 AI-Native project (Probabilistic View of AI Agents). Works out where the student currently stands, then walks them through hidden states, belief updates, entropy, information value, cost, decision thresholds, the experiment,…

Pre-deliverable interview for the Week 2 AI-Native project (Probabilistic View of AI Agents). Diagnoses where a student actually stands — their problem, their Week 1 artifacts, which concepts they can genuinely explain, their coding and data situation, and…

Personalized 6-month action plan to land a $100K AI job or equivalent income. Runs a 3-round intake (15 questions), then generates a fully personalized plan covering: architecture learning roadmap, skill value audit, 10 LinkedIn post drafts, outreach…

Scaffold and configure an LLMWiki-style AI-maintained second brain. Use when the user wants to create, install, clone, set up, initialize, or automate a personal Obsidian/Claude/Codex knowledge vault; convert the LLMWiki playbook into an agent-run workflow;…

Adversarial evaluation skill for Session 1 (Regression & Supervised Learning). Acts as a misguided AI partner across 10 scenarios — student must say "no", name the technical flaw, and tie it to business outcome. Skill never teaches mid-session. At the end,…