Skills

Designs and implements visual regression testing for web UIs. Recommends a tooling approach (Playwright snapshots, Storybook + a diffing service, or a dedicated platform), writes screenshot tests with stable selectors and masked dynamic regions, sets sensible diff thresholds, and integrates the suite into CI with baseline management to reduce flaky failures.

Designs caching strategies across the stack to cut latency and load. Chooses cache layers (browser, CDN, application, database), picks patterns (cache-aside, read-through, write-through, write-behind), sets TTLs and eviction policies, and plans invalidation to avoid staleness and stampedes. Produces a layered plan with keys, TTLs, and invalidation rules.

Defines data contracts between producers and consumers to prevent breaking changes in pipelines. Covers schema definitions, semantic types, freshness and quality SLAs, ownership, versioning, and backward/forward compatibility rules. Generates contract specs (e.g., ODCS-style) and CI checks that fail builds when a producer violates the contract.

Generates consumer-driven contract tests between services so that API providers and consumers stay compatible as they evolve independently. Produces Pact-style contracts, provider verification stubs, and CI wiring, and flags breaking changes before they reach production.

Develops a distinctive, consistent brand voice and tone system. Defines voice attributes, do/don't guidance, vocabulary and grammar rules, tone shifts by context (marketing, support, errors), and worked before/after examples. Produces a practical style guide teams and AI assistants can apply across every touchpoint.

Writes reusable, well-structured Terraform modules with clean input/output interfaces, sensible defaults, validation rules, and examples. Covers module composition, variable typing and validation, remote state and backends, provider version pinning, and testing with terraform validate/plan and tools like Terratest. Emphasizes least-privilege IAM and safe defaults.

Models complex application logic as explicit finite state machines and statecharts. Identifies states, events, guards, and side effects; prevents impossible states; and generates implementations (e.g., XState-style) with diagrams. Ideal for wizards, checkout flows, connection lifecycles, and any feature where implicit boolean flags cause bugs.

Advises on how to evolve APIs without breaking clients. Compares versioning strategies (URI, header, media-type), classifies changes as breaking or non-breaking, and produces deprecation timelines, migration guides, and compatibility shims so teams can ship changes safely.

Designs chaos engineering experiments to validate system resilience. Defines steady-state hypotheses, blast-radius limits, fault injections (latency, errors, instance/zone loss, resource exhaustion), abort conditions, and observability checks. Produces a safe, incremental experiment plan and success criteria for game days and automated chaos.

Turns merged pull requests, commits, and issue references into clear, audience-appropriate release notes. Groups changes into features, fixes, and breaking changes, translates technical detail into user-facing value, and produces both a concise highlights section and a complete changelog.

Designs and implements real-time services using WebSockets and Server-Sent Events. Covers connection lifecycle, heartbeats, reconnection with backoff, room/channel fan-out, backpressure, authentication on upgrade, and horizontal scaling with a pub/sub backplane. Produces production patterns for chat, live dashboards, collaborative editing, and streaming updates.

Builds cross-browser extensions on Manifest V3 with background service workers, content scripts, popup and options UIs, and message passing. Covers permissions scoping, storage sync, context menus, and store submission requirements for Chrome, Edge, and Firefox. Emphasizes least-privilege permissions and secure content-script isolation.

Builds robust webhook producers and consumers. Covers signature verification, idempotency keys, retry with exponential backoff, dead-letter handling, event ordering, and replay endpoints, and generates handler code plus tests so integrations survive duplicates and outages.

Modernizes legacy codebases incrementally and safely. Establishes characterization tests to lock in current behavior, then applies the strangler-fig pattern, dependency updates, and idiomatic refactors in small verifiable steps, producing a migration plan that avoids big-bang rewrites.

Scans code, configuration, and git history for leaked credentials such as API keys, tokens, private keys, and connection strings. Classifies findings by severity and false-positive likelihood, and provides safe remediation steps including rotation, history scrubbing, and pre-commit prevention.

Generates property-based tests that assert invariants across randomly generated inputs using frameworks like Hypothesis, fast-check, or jqwik. Identifies properties (round-trip, idempotence, invariants, oracle comparison), defines generators and shrinking, and sets up stateful testing for complex APIs. Surfaces edge cases that example-based tests miss.

Writes end-to-end UI tests with Playwright or Cypress that mirror real user journeys. Covers resilient selectors (roles/test-ids), network stubbing, auth setup, fixtures, waiting strategies that avoid flakiness, visual and accessibility assertions, and CI integration. Produces maintainable specs and a page-object structure.

Helps researchers and nonprofits draft compelling grant proposals. Structures the significance, aims, methodology, timeline, and budget justification to match a funder's priorities and review criteria, tightens the narrative, and flags gaps a reviewer would penalize before submission.

Diagnoses and fixes flaky tests by analyzing test code and CI failure history for common sources of nondeterminism such as time and timezone dependence, order dependence, shared mutable state, race conditions, and unmocked network calls. Proposes targeted fixes and quarantine strategies to keep the suite trustworthy.

Helps design and implement features for machine learning models from raw tabular, time-series, or text data. Suggests transformations, encodings, aggregations, and leakage-safe splits, explains the rationale, and generates reproducible feature pipeline code with validation.

Analyzes LLM usage and reduces inference cost without sacrificing quality. Covers prompt compression, context trimming, caching (prompt and semantic), model routing by task difficulty, batching, structured output to cut retries, and token accounting. Produces a concrete plan with estimated savings and quality guardrails.