Skills
Use these skill folders with the tutorial.
MCAF lists only the skills bundled in this repository. .NET skills live in the dedicated Managed Code Skills catalog.
Create or update an ADR under `docs/ADR/` for architectural decisions, dependency changes, data-model changes, or cross-cutting policy shifts. Use when the user asks to write, update, or document an ADR, record a design decision, capture architecture trade-offs, or justify a repo-wide technical policy.
Shape delivery workflow around backlog quality, roles, ceremonies, and engineering feedback. Use when defining how the team plans, tracks work, and turns feedback into durable improvements.
Create or update `docs/Architecture.md` as the global architecture map for a solution. Use when bootstrapping a repo, onboarding, or changing modules, boundaries, or contracts. Keep it navigational and use `references/overview-template.md` for scaffolding.
Design or refine CI/CD workflows, quality gates, release flow, and safe AI-assisted pipeline authoring. Use when adding or changing build pipelines, release stages, IaC-driven environments, or deployment rollback policy.
Prepare for, perform, or tighten code review workflow: PR scope, review checklist, reviewer expectations, and merge hygiene. Use when shaping pull requests, defining review policy, or auditing whether a change is review-ready.
Improve developer experience for multi-component solutions: onboarding, F5 contract, cross-platform tasks, local inner loop, and reproducible setup. Use when the repo is hard to run, debug, test, or onboard into.
Create or refine durable engineering documentation: docs structure, navigation, source-of-truth placement, and writing quality. Use when a repo’s docs are missing, stale, duplicated, or hard to navigate, or when adding new durable engineering guidance.
Create or update a feature spec under `docs/Features/` with business rules, user flows, system behaviour, verification, and Definition of Done. Use when the user asks for a feature spec, executable requirements, acceptance criteria, behaviour documentation, or a pre-implementation plan for non-trivial behaviour changes.
Plan a human review for a large AI-generated code drop by reading the target area, tracing the natural user and system flows, identifying the riskiest boundaries, and prioritizing the files a human should inspect first. Use when the codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list.
Apply ML/AI project delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems. Use when the repo includes model training, inference, data science workflows, or ML-specific delivery planning.
Capture or refine non-functional requirements such as accessibility, reliability, scalability, maintainability, performance, and compliance. Use when a feature or architecture change needs explicit quality attributes and trade-offs.
Design or improve observability for application and delivery flows: logs, metrics, traces, correlation, alerts, and operational diagnostics. Use when a change affects runtime visibility, failure diagnosis, SLOs, or alerting.
Apply baseline engineering security guidance: secrets handling, secure defaults, threat modelling references, and review checkpoints for auth, data flow, pipelines, and external integrations. Use when a change has security impact but does not require a full standalone AppSec engagement.
Apply SOLID, SRP, cohesion, composition-over-inheritance, and small-file discipline to code changes. Use when refactoring large files or classes, setting maintainability limits in `AGENTS.md`, documenting justified exceptions, or reviewing design quality.
Set up or refine solution-level governance for MCAF repositories: root and project-local `AGENTS.md`, rule precedence, solution topology, skill routing, and maintainability-limit policy placement. Use when bootstrapping a repo, restructuring a multi-project solution, or tightening agent rules.
Set or refine source-control policy for repository structure, branch naming, merge strategy, commit hygiene, and secrets-in-git discipline. Use when bootstrapping a repo, tightening PR flow, or documenting branch and release policy.
Add or update automated tests for a change using the repository’s verification rules in `AGENTS.md`. Use when implementing a feature, bugfix, refactor, or regression test; prefer stable integration/API/UI coverage and pull deeper test strategy from the bundled references.