Marketplace

Diagnose visual bugs in Godot 4 — invisible objects, blurry sprites, black materials, transparency sorting, shadow and shader faults — using measured evidence.

Use when building or hardening a GCP organization: folder/project hierarchy, Shared VPC, org policies, least-privilege IAM, log sinks, billing guardrails.

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.

Invoke when an AI system touches the EU market and you must classify its EU AI Act risk tier and output the obligations and article citations that follow.

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

Use when onboarding, transferring, disabling or offboarding Microsoft Entra ID users, their group memberships and their licenses through Microsoft Graph.

Use when a task needs Microsoft Graph access: register an Entra app, pick delegated vs application permissions, grant admin consent, authenticate.

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.

Design disaster recovery strategies: RTO/RPO targets, backup automation, failover procedures, and DR testing.

Deploy and manage DigitalOcean infrastructure: Droplets, App Platform, Kubernetes, managed databases, spaces, and networking via doctl CLI.

Use when adding, upgrading, or auditing third-party packages: judge vulnerabilities, maintenance health, and supply-chain risk before they ship.

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 conversational analytics interfaces: NL-to-SQL, query disambiguation, result narration, and data storytelling.

Optimize how context is assembled for LLM calls: system prompt design, few-shot example selection, context pruning, and token budget management.

Scan and harden container images: base image vulnerabilities, runtime permissions, distroless patterns, and admission policies.