Marketplace

Design data quality monitoring: anomaly detection, schema validation, freshness checks, and data contracts between teams.

Design robust data pipelines: ingestion patterns, transformation stages, schema evolution, and data quality monitoring.

Systematically clean a messy dataset with profiling, ordered fixes, validation, and a provenance log; use before analyzing or merging untrusted data.

Review cryptographic implementations: algorithm selection, key management, random number generation, and protocol-level flaws.

Audit cloud infrastructure for cost optimization: right-sizing, reserved capacity, storage tiers, and waste elimination.

Design chaos engineering experiments: hypothesis formation, blast radius control, steady-state metrics, and game day execution.

Design canary and progressive deployment strategies: traffic splitting, health metrics, automatic rollback, and analysis windows.

Design AI writing assistant features: tone control, suggestion timing, feedback loops, and human-in-the-loop editing.

Design AI-powered search experiences: semantic search, conversational interfaces, result ranking, and relevance tuning.

Design AI content moderation pipelines: multi-layer classification, appeal workflows, edge case handling, and trust safety.

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.