Skills

Drafts tailored, concise cover letters that connect a candidate's experience to a specific job description. Extracts the role's key requirements, maps them to the applicant's most relevant achievements, and produces a focused letter in the requested tone without generic filler.

Generates JSON Schema (Draft 7 through 2020-12) from example payloads, TypeScript types, or a natural-language description of a data shape. Infers types, required fields, formats, and constraints, and can add descriptions and enums to produce a validation-ready schema.

Diagnoses failing Kubernetes workloads from symptoms, events, logs, and manifests. Works through a systematic checklist (pod status, events, resource limits, probes, scheduling, networking) to identify the root cause of issues like CrashLoopBackOff, ImagePullBackOff, OOMKilled, and pending pods, then recommends concrete fixes and verification steps.

Guides a git bisect session to isolate the commit that introduced a regression. Helps define a reliable good/bad test, drives the bisect steps, interprets results, and can suggest an automated test script for `git bisect run`, then explains the offending change once the culprit commit is found.

Analyzes Terraform plan output and state to find configuration drift between declared infrastructure and what actually exists. Explains each drifted resource in plain language, flags risky or destructive changes, and recommends whether to reconcile in code, import, or apply, so teams can keep state and reality aligned safely.

Designs a customer churn prediction approach end to end: framing the target and prediction window, engineering behavioral and tenure features, choosing suitable models and evaluation metrics for imbalanced data, and planning how predictions feed retention actions. Focuses on leakage-free design and metrics that match the business goal.

Finds, validates, and fixes security vulnerabilities end to end, going beyond audit-only review to produce verified patches. Reproduces the issue, proposes a minimal fix, and confirms the vulnerability is closed without breaking existing behavior. Models a CodeMender-style workflow where multiple agents detect, validate, and patch code security issues at scale.

Reviews cloud and AI infrastructure spend to identify measurable savings while preserving reliability, performance, security, ownership, and product requirements.

Defines versioned data contracts between producers and consumers, with ownership, schemas, quality expectations, compatibility rules, and operational change management.

Simulates realistic adversarial behavior against AI features to uncover prompt injection, data exfiltration, unsafe tool use, jailbreak, and authorization failures before launch.

Tests AI agent tool use with realistic fixtures, malformed inputs, permission boundaries, deterministic assertions, and recovery checks for failed or partially completed actions.

Designs durable memory for AI agents, including what to retain, how to retrieve it, privacy boundaries, expiration policies, and evaluation criteria for useful recall.

Guides containment, evidence collection, root-cause analysis, customer communication, and remediation when an AI agent takes an unsafe, incorrect, or unauthorized action.

Evaluates retrieval quality for search and RAG systems using grounded test sets, relevance metrics, failure taxonomy, chunking experiments, and actionable remediation.

Bilinmeyen bir repository'yi mimari, kurallar, yerel kurulum, önemli çalışma yolları ve güvenli ilk değişiklikler hakkında kısa bir mühendislik rehberine dönüştürür.

Designs reliable JSON and typed outputs for LLM features, including schemas, validation, recovery paths, versioning, examples, and contracts for downstream consumers.

Diagnoses failing browser and end-to-end tests by correlating assertions, traces, screenshots, network activity, timing, and application state into reproducible fixes.

Converts a design specification or screenshot into accessible, responsive production UI while preserving component boundaries, design tokens, semantic structure, and states.

Defines repeatable quality evaluation for LLM outputs using representative datasets, scoring rubrics, model-graded checks, human review sampling, and regression thresholds.

Ürün arayüzlerini semantik yapı, klavye kullanımı, odak davranışı, renk kontrastı, ekran okuyucular ve formlar dahil olmak üzere pratik erişilebilirlik gereksinimlerine göre denetler.

Turns merged work, pull requests, issues, and user-visible behavior changes into accurate, audience-specific release notes with breaking-change and upgrade guidance.