AI Agent Workflow Collection

Use when a framed and root-caused problem needs its assumptions made explicit before design or planning begins — explicit Assumptions, Unknowns, and a Validation plan. This should trigger when an issue's Assumption Analysis point of view needs evaluation, or when a maintainer directly asks to surface hidden assumptions and unknowns before committing to an approach.

Use when reviewing whether an OpenSpec change's execution goal, acceptance criteria, and implementation or verification tasks are aligned. This should trigger for requests such as Review this OpenSpec change with ATDD; Check acceptance criteria against tasks; Find acceptance criteria without task coverage; Detect tasks that diverge from the execution goal; Explain what is missing, vague, ambiguous, partial, absent, or divergent in this OpenSpec change.

Use when you need to review a plan, OpenSpec change, specification, or implementation proposal for breaking-change risk across commands, skills, generated outputs, XML sources, README/docs, tests, CI, APIs, schemas, configuration, data, migration, and release guidance. This should trigger for requests such as Review breaking changes in this spec; Check compatibility risks; Avoid breaking changes in this OpenSpec change; Review migration impact before release; Assess command and skill compatibility.

Use when a Java change needs independent Behavior-Driven Development guidance from trusted behavior facts through concrete examples and observable scenarios, with focused clarification when behavior is pending or ambiguous. This should trigger for requests such as Apply BDD; Facilitate behavior examples; Discover scenarios with Given When Then; Review these examples for shared domain language; Complete BDD discovery as a self-contained interaction.

Use when designing, implementing, reviewing, testing, or cleaning up feature toggles, feature flags, kill switches, runtime configuration gates, canary controls, staged rollouts, experiments, or temporary compatibility switches in Java enterprise systems. This should trigger for requests such as Design a feature toggle strategy; Review this feature flag; Add a kill switch safely; Test toggle on and off paths; Clean up an expired feature toggle; Plan controlled rollout and rollback behavior.

Use when a large feature, story, plan, or spec idea needs to be split into small vertical slices with the Hamburger Method. This should trigger for requests such as Split this oversized story; Find the smallest useful slice; Break this feature into vertical slices; Apply the Hamburger Method; Turn this broad plan into tracked slices.

Use when a database schema or data-meaning change needs Parallel Change, including expand, migrate, and contract sequencing for column renames, type or data reinterpretation, large-table backfills, relationship-table changes, enum/status transitions, timezone/default changes, and index or uniqueness changes. This should trigger before framework-specific Flyway implementation guidance when deciding whether a migration needs a compatibility window or whether a simpler migration is sufficient.

Use when Java design, refactoring, or implementation tradeoffs should be evaluated with Kent Beck's simple design rules, including passes the tests, reveals intention, has no duplication, and has the fewest elements. This should trigger for requests such as Apply simple design rules; Review this design with Beck's rules; Choose between these refactoring options; Keep this Java design simple.

Use when Java implementation work should be guided by Test-Driven Development, including maintaining a test list, choosing the next behavior, writing a failing test first, implementing only enough production code to pass, and refactoring while keeping tests green. This should trigger for requests such as Apply TDD; Use test-driven development; Drive this Java change with tests; Write the failing test first; Red-green-refactor this feature.

Use when a complex or risky code change should be split into Kent Beck's two-step method by first making the change easy through behavior-preserving preparatory refactoring, then making the intended behavior change once the design supports it. This should trigger for requests such as Apply two-step change; Make this risky change safer; Refactor before changing behavior; Separate preparation from behavior change.

Use when Java concurrency with ExecutorService, CompletableFuture, and virtual threads. Use when building concurrent applications.

Use when Java generics including type parameters, wildcards, and type bounds. Use when writing type-safe reusable code.

Use when you need Azure DevOps CLI guidance to verify installation, configure organization and project context, discover work item IDs by optional WIQL filters, and execute safe work-item create/update operations. Uses an interactive install gate - if az or the Azure DevOps extension is missing, ask whether to show installation guidance before any work item commands. This should trigger for requests such as Azure DevOps work item list; List Azure Boards work item IDs; Azure DevOps WIQL query; Azure DevOps CLI planning workflow.

Use when a problem under exploration needs its quality attributes (non-functional requirements) identified and prioritized before architecture and design begin. This should trigger when an issue's Quality Attribute Discovery point of view needs evaluation, or when a maintainer directly asks to discover and prioritize candidate quality attributes for a problem, before any ADR or design work starts.

Use when reviewing, designing, or modifying Java enterprise systems that may support EU Market Abuse Regulation concerns, market surveillance, suspicious order and…

Use when reviewing Java enterprise evidence for MiFID II investment services, investment activities, client classification, suitability, appropriateness, order-handling evidence, best-execution evidence, algorithmic-trading governance evidence, market-access governance evidence, transaction evidence, record keeping, monitoring, or compliance-owner handoff. This should trigger for requests such as Review a Java investment-service platform for MiFID II evidence; Assess suitability, appropriateness, order-handling evidence, or best-execution evidence; Document audit, monitoring, or clock-synchronisation gaps for algorithmic-trading governance; Assess investment-service engineering evidence before production release.

Use when reviewing, designing, or modifying Java enterprise software products, AI-enabled products, RAG assistants, AI agents, generated instructions, related services, automated updates, vulnerability handling, corrective updates, warnings, instructions, or product-safety evidence under Directive (EU) 2024/2853, the EU Product Liability Directive.

Use when reviewing, designing, or modifying Java enterprise systems that use GenAI, LLMs, AI-assisted coding, RAG, AI agents, generated code, generated dependencies, prompt workflows, external model providers, or AI-enabled business logic and need ISO/IEC 42001 AI management system-aware engineering guidance.

Use when you need framework-agnostic Hexagonal architecture guidance for Java projects - ports and adapters boundaries, application core independence, driving and driven adapters, dependency direction, infrastructure leakage detection, optional architecture tests, and evidence-backed remediation. This should trigger for requests such as Review Hexagonal architecture; Review ports and adapters boundaries; Enforce dependency direction; Add ArchUnit architecture tests; Detect infrastructure leakage in domain code; Improve ports and adapters boundaries.