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

Deploy and manage Vultr cloud infrastructure: instances, Kubernetes, block storage, object storage, DNS, and firewalls via vultr-cli.

Design voice assistant experiences: intent design, dialogue management, error recovery, and multi-modal handoff.

Optimize vector databases: index selection (HNSW, IVF), parameter tuning, metadata filtering, and cost-performance tradeoffs.

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.

Generate synthetic training data: schema preservation, distribution matching, privacy guarantees, and quality validation.

Audit software supply chain security: SBOM generation, dependency provenance, artifact signing, and CI/CD hardening.

Design real-time stream processing: windowing, watermarking, exactly-once semantics, and state management.

Write production SRE runbooks: alert response procedures, diagnostic steps, escalation paths, and post-action verification.

Diagnose slow SQL with execution-plan analysis and rewrite queries and indexes; use when a query is slow, times out, or burns database CPU.

Use when creating, improving, or reviewing an agent skill: scope one job, write routing-grade descriptions, and verify with fresh-agent tests.

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.

Scan codebases for leaked credentials, API keys, and tokens: pattern matching, entropy analysis, and safe remediation.

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.

Use when a prompt underperforms and needs systematic diagnosis, single-variable experiments, and A/B evidence instead of guess-and-check rewrites.

Use to adversarially test an LLM app, agent, or tool pipeline for prompt-injection/jailbreak weakness and produce a scored hardening report.

Audit systems for privacy compliance: data minimization, purpose limitation, consent management, GDPR/CCPA alignment.

Plan and scope penetration tests: attack surface mapping, test methodology selection, and finding documentation.