Marketplace

Design reusable Infrastructure as Code modules: Terraform, Pulumi, and CloudFormation patterns for maintainable infrastructure.

Drive headless browsers (Playwright/Puppeteer) to navigate pages, auto-screenshot at each step, fill forms, click elements, and capture visual evidence of workflows.

Design and scaffold idempotent, observable extract-transform-load pipelines with safe retries, backfills, and quality gates; use when building data pipelines.

Choose embedding models and strategies: dimensionality, domain-specific models, multi-modal embeddings, and evaluation.

Design document processing pipelines: OCR, layout analysis, information extraction, classification, and human review.

Use when a Dockerfile builds slowly, produces oversized images, or needs hardening; rewrites it for smaller, faster, and more secure builds.

Use when a failure's cause is unknown: a hypothesis-driven protocol from symptom to confirmed root cause, with evidence trail and regression test.

Design data warehouse schemas: star/snowflake schemas, slowly changing dimensions, materialized views, and query optimization.

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.