Flockion_AI_Engineering

Reviews Flockion code, diffs, pull requests, generated files, and refactors for correctness, simplicity, maintainability, file size, type safety, accessibility, security, observability, and overengineering. Use for TypeScript, React, Python, APIs, agents, YAML, Bicep, GitHub Actions, and cloud automation code.

Forces the simplest, shortest, safest AI solution that actually works. Lazy senior-engineer pragmatism for LLM features, agents, RAG, tool-calling, and prompts. Use a prompt before a chain, retrieval before fine-tuning, deterministic code before an agent, and one tool before a tool framework. Keep critical business rules in code, not in the model. Never lazy about evaluation, prompt-injection safety, or human approval for risky actions. Supports intensity levels: lite, full, ultra. Use whenever the user says "flockion", "flockion ai", "be lazy", "lazy mode", "simplest solution", "minimal solution", "YAGNI", "do less", "shortest path", "avoid overengineering", or complains about agent bloat, prompt sprawl, needless fine-tuning, or multi-agent architecture for imaginary requirements.

Forces the simplest, shortest, safest data solution that actually works. Lazy senior-engineer pragmatism for SQL, databases, schema design, migrations, and data pipelines. Use the database before application code, a plain query before an ORM dance, an index before a cache, and one reversible migration before a schema framework. Never lazy about data correctness, constraints, or irreversible changes. Supports intensity levels: lite, full, ultra. Use whenever the user says "flockion", "flockion data", "be lazy", "lazy mode", "simplest solution", "minimal solution", "YAGNI", "do less", "shortest path", "avoid overengineering", or complains about query bloat, premature denormalization, ORM magic, or pipelines built for imaginary volume.

Forces the simplest, shortest, safest infrastructure that actually works. Lazy senior-DevOps pragmatism for IaC, CI/CD, containers, and cloud automation: Bicep, Terraform, GitHub Actions, Docker, Kubernetes, shell. Use managed platform features before custom infra, official actions before bespoke pipelines, least privilege before convenience, and one workflow per real lifecycle before a pipeline framework. Supports intensity levels: lite, full, ultra. Use whenever the user says "flockion", "flockion infra", "be lazy", "lazy mode", "simplest solution", "minimal solution", "YAGNI", "do less", "shortest path", "avoid overengineering", or complains about pipeline bloat, YAML sprawl, over-permissioned roles, or infra for imaginary scale.

Forces the simplest, shortest, safest solution that actually works. Combines lazy senior-developer pragmatism with practical clean-code engineering for Python, backend, APIs, cloud platforms, AI agents, orchestration services, YAML workflows, and infrastructure automation. Use existing code before new code, standard library before custom code, native platform features before dependencies, deletion before addition, and focused design before architecture. Supports intensity levels: lite, full, ultra. Use whenever the user says "flockion", "be lazy", "lazy mode", "simplest solution", "minimal solution", "YAGNI", "do less", "shortest path", "avoid overengineering", "clean but simple", or complains about bloat, boilerplate, unnecessary dependencies, oversized files, or architecture for imaginary requirements.

Forces the simplest, shortest, safest solution that actually works for Flockion engineering work. Combines lazy senior-developer pragmatism with practical clean-code standards for TypeScript, React, frontend, Python, backend, APIs, cloud platforms, AI agents, orchestration services, YAML workflows, and infrastructure automation. Use existing code before new code, standard library and native platform features before dependencies, deletion before addition, local state before global state, focused components before architecture, and boring production-safe design before clever abstractions. Supports intensity levels: lite, full, ultra. Use whenever the user says "flockion engineering", "ponytail", "lazy mode", "simplest solution", "minimal solution", "YAGNI", "do less", "shortest path", "avoid overengineering", "clean but simple", or complains about bloat, boilerplate, unnecessary dependencies, oversized files, messy components, or architecture for imaginary requirements.

Base engineering standard for Flockion. Forces the simplest, shortest, safest solution that actually works across TypeScript, React, Python, backend, APIs, cloud platforms, AI agents, orchestration services, YAML workflows, and infrastructure automation. Use existing code before new code, native platform features before dependencies, deletion before addition, local state before global state, focused components before architecture, and boring production-safe design before clever abstractions.

Maps Flockion features, code, agents, APIs, cloud resources, and workflows to ISO/IEC 27001:2022 Annex A controls. Use when building or reviewing anything that touches access control, secrets, logging, data retention, encryption, supplier/third-party risk, change management, incident handling, or evidence for an audit. Turns "is this compliant?" into the smallest set of controls that actually hold, plus the evidence to prove it.

Picks the smallest cloud design pattern that solves a named problem, and refuses to add one without a named constraint. Covers the full Azure Well-Architected catalog (43 patterns: Circuit Breaker, Retry, Bulkhead, CQRS, Event Sourcing, Saga, Sidecar, Ambassador, Gateway Routing/Aggregation/ Offloading, Quarantine, Valet Key, Sharding, Cache-Aside, Strangler Fig, and more) mapped to the five WAF pillars. Use when choosing or reviewing resilience, messaging, data, gateway, deployment, or security patterns for a distributed system — and to challenge patterns applied without a real risk.

Breaks a monolith into services the lazy, safe way — only when there is real pressure, one reversible seam at a time, using incremental patterns (strangler fig, branch by abstraction, parallel run, expand–migrate–contract data migrations, anti-corruption layer, event interception) instead of a big-bang rewrite. Defaults to "extract a module, not a service" and a modular monolith until distribution is genuinely justified. Use when planning or executing monolith decomposition, service extraction, seam-finding, refactoring toward microservices, or strangling a legacy system.

Designs production-grade Flockion agents and agent teams with clear purpose, input/output contracts, narrow tools, memory rules, handoffs, guardrails, observability, cost control, and human approval for risky actions. Use when creating, reviewing, or improving agents, teams, prompts, tools, skills, orchestration flows, and marketplace-ready agent products.

Reviews proposed architecture for Flockion using practical engineering: simplest working design, clear boundaries, secure defaults, observability, cost awareness, and no speculative infrastructure. Use for Azure, FastAPI, React, PostgreSQL, Service Bus, storage, agent orchestration, RAG, and event-driven designs.

Reviews and improves AI, token, cloud, database, storage, queue, logging, and execution costs for Flockion. Use when designing agents, teams, RAG, model usage, Azure resources, GitHub Actions, background jobs, streaming, caching, batching, or usage-based pricing.

Turns a product idea into the smallest useful Flockion feature that can be shipped, tested, and improved. Forces MVP thinking, user-value clarity, minimum data model, minimum API, minimum UI, and clear success metrics. Use when defining new features, agent marketplace concepts, MVPs, user flows, monetization ideas, or product experiments.

Reviews Flockion features, code, agents, tools, APIs, cloud resources, authentication, authorization, tenant isolation, secrets, prompt injection, data access, audit logging, and risky workflows. Use before exposing functionality to users, production systems, external tools, or marketplace agents.