Marketplace

Design AI code review systems: PR analysis, finding prioritization, false positive suppression, and developer feedback.

Design complex agentic workflows: DAG-based task graphs, conditional branching, human-in-the-loop checkpoints, and error recovery paths.

Design first-run experiences and onboarding flows for AI agents: capability discovery, permission grants, and initial task routing.

Implement tracing, logging, and monitoring for AI agent systems: LLM call traces, tool execution logs, latency tracking, and replay debugging.

Design seamless handoff protocols between specialized agents: context transfer, state serialization, and recovery on failure.

Design self-improving agent systems: outcome tracking, reward signal extraction, prompt refinement cycles, and human preference collection.

Use when an agent workflow needs measurable quality: build task-based evals with graders, baselines, and A/B comparisons before shipping changes.

Design and run comprehensive eval suites for AI agents: task completion rates, tool call accuracy, cost tracking, and regression testing.

Curate and clean training datasets: deduplication, quality scoring, bias detection, and data augmentation strategies.

Validate and test tool/function calling schemas: JSON Schema compliance, edge case generation, parameter boundary testing, and mock servers.

Create production-grade tools/functions that AI agents can call: define schemas, implement handlers, add validation and error handling.

Use when designing or changing a feature and you need a STRIDE threat model: map the system, enumerate threats per element, and rank mitigations.

Use when auditing a codebase or diff for security defects: map attack surface, hunt OWASP-class bugs, and rate each finding by real exploit path.

Use when credentials may be exposed in code, config, logs, or git history: find them, judge real exposure, and drive rotation and cleanup.

Design recommendation systems: collaborative filtering, content-based, hybrid approaches, cold start, and ranking.

Build retrieval-augmented generation pipelines: chunking strategies, embedding models, vector stores, and reranking for accurate grounding.

Establish versioned prompt management: semantic versioning, A/B testing, rollback, and changelog practices for production prompt systems.

Design comprehensive observability: metrics, logs, traces, and profiles with SLO-driven alerting and dashboard design.

Design multi-region active-active and active-passive architectures: data replication, failover, DNS routing, and consistency.

Build comprehensive model evaluation: benchmark selection, statistical significance, human evaluation protocols, and safety testing.

Plan ML model deployment: serving patterns, A/B testing, canary releases, monitoring, and rollback for production ML.

Design zero trust network architectures: identity-based perimeters, microsegmentation, and continuous verification.

Design and implement ML feature stores: online/offline serving, feature freshness, training-serving skew prevention, and feature discovery.

Design ML experiment tracking: hyperparameter logging, model versioning, reproducibility, and experiment comparison workflows.