timwukp

Run chaos security engineering tests — AI agent attempts to hack the Sprint output and reports findings to the team

Validate Java and TypeScript code against banking naming conventions, style rules, quality standards, and secure coding practices

Perform OWASP Top 10 security assessments, vulnerability scanning, and PCI-DSS compliance checks

Design test strategies including security testing, write test plans, and identify test scenarios for banking features with DevSecOps integration

Perform STRIDE threat modeling during Sprint Planning for new features and APIs

Run UX review checklist for banking user flows, validate WCAG accessibility compliance, assess security UX patterns, and review customer interface design

Design, review, and validate API contracts and OpenAPI specifications for banking services

Create ADR documents, run architecture review checklists, and assess compliance impact for banking system designs

Build production-ready AWS Bedrock AgentCore Harness agents end to end — declarative model + system prompt, managed or BYO Memory, built-in Browser and Code Interpreter, Gateway/MCP tools, inline functions, Skills, versioning + endpoints (prod rollout/rollback), advanced config (truncation, limits, lifecycle, network, inbound auth), Observability (log delivery + tracing), Evaluations, Optimizations, Identity (outbound auth, Token Vault, credential providers), Policy guardrails, Payments, and the Agent Registry. Use whenever the user wants to create, configure, deploy, version, wire, harden, invoke, or troubleshoot an AgentCore Harness — or asks about AgentCore best practices, harness.json, CreateHarness/UpdateHarness/InvokeHarness, harness endpoints/qualifiers, attaching Memory, wiring browser/code-interpreter, adding skills, observability/log delivery, or A/B-testing prompts. Trigger even when the user describes a managed, declarative Bedrock agent with tools/memory/skills without saying "harness".

Build, extend, or debug GitLab CI/CD pipelines (.gitlab-ci.yml) that run Kiro CLI in headless mode for AI-powered merge-request review, security scanning, test-failure analysis, config-drift detection, code-duplication sync checks, and change-impact/risk analysis — including integrating the customer's MCP servers (SonarQube SAST, Jira, internal tools) into CI jobs. Use this skill whenever the user mentions GitLab CI, .gitlab-ci.yml, GitLab pipelines, merge-request automation, AI code review in CI, Kiro CLI in CI/CD, kiro-cli headless or non-interactive mode, MCP servers in pipelines, or wants agentic/LLM checks in a pipeline — even if they don't say "Kiro" or "GitLab CI" explicitly (e.g. "add an AI reviewer to my MRs", "hook SonarQube into the AI review", "make the pipeline catch config drift").

Maps code, architecture, and infrastructure changes to specific control IDs in financial services compliance frameworks - PCI-DSS v4.0 for payment card data and MAS TRM for Singapore-regulated institutions - producing an audit-traceable findings report with per-control remediation. Use this instead of a general security review whenever a compliance framework (PCI-DSS, MAS TRM), regulator, audit, or cardholder-data scope is mentioned, even if the request is phrased as a code review or a yes/no compliance question. Triggers on: "PCI-DSS check", "MAS TRM", "is this compliant", "compliance review", "audit this change for banking regulations", "does logging this violate PCI", "cardholder data handling review".

Reviews AWS architectures, IaC, and design docs against the AWS Well-Architected Framework's six pillars, producing a findings report with pillar-mapped risks (High/Medium) and concrete remediation. Loads only the pillars relevant to the change. Use for AWS architecture reviews, not generic code review. Triggers on: "well-architected review", "review this AWS architecture", "WAR review", "check this design against AWS best practices", "review my Terraform/CDK for AWS pitfalls", "is this architecture production-ready".

Infers a codebase's implicit coding conventions (naming, structure, imports, comments, error handling) by analyzing the existing code, then makes them explicit: generates linter/formatter configs that match current reality, a conventions document, and agent steering rules so AI-written code blends in. Use when adopting AI coding tools on an existing codebase or onboarding to an unfamiliar team style. Triggers on: "match our coding style", "infer our conventions", "generate lint config from this codebase", "make the AI write code like our team", "extract our code standards", "set up steering rules from existing code".

Adds tests to legacy code that has none, safely: characterization tests that pin current behavior (including bugs) before any refactoring, seam identification for breaking untestable dependencies, and a risk-ranked coverage strategy. Use for untested or inherited codebases. Triggers on: "add tests to legacy code", "this code has no tests", "characterization tests", "make this testable", "safe to refactor?", "pin down current behavior", "test this old module before we change it".

Writes security user stories and security-aware acceptance criteria that fit a Scrum backlog, converting threats, scan findings, and compliance requirements into INVEST-compliant stories with Given/When/Then criteria and regression tests. Use this (not general story writing) whenever the story or criteria concern a security control, vulnerability, or compliance requirement. Triggers on: "write a security story", "security acceptance criteria", "acceptance criteria for rate limiting/auth/validation", "Given/When/Then for this security control", "turn this vulnerability into a backlog item", "convert these scan findings to stories".

Facilitates sprint planning with security debt baked in: capacity planning from rolling velocity, risk-weighted prioritization balancing feature value against security debt, commitment sizing, and a DevSecOps definition of done. For writing or refining the stories themselves, use user-story-writing instead. Triggers on: "plan the sprint", "sprint planning", "prioritize these stories", "how much can we commit", "balance security debt against features", "sprint capacity".

Bug-Fix Agent skill — minimal-diff patches, severity-aware fixes (rollback for CRITICAL, escalate for sensitive paths), patch generation rules, and anti-patterns to avoid

UI testing methodology for web applications — severity classification, interaction patterns, edge case handling, and report structure for the UI Test Agent