Use when designing AI Agent product capabilities, competition judging evidence, or production agent safety. Creates judgment → action → verification/improvement loops with retrieval, rule checks, approval, logging, and feedback.
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Use after PRD/domain model and before implementation. Designs system architecture, component boundaries, dataflow, integration points, security boundaries, logging, audit, failure handling, and demo-vs-production separation.
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Use before asking coding agents to implement features, especially when outputs must converge across Claude, Codex, Gemini, or human maintainers. Creates deterministic code-generation rules for naming, imports, exports, folders, APIs, UI states, tests, lint…
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Use before CPS, PRD, pitch, or competition submission when a product needs a sharp data-driven business model. Creates the 11-block DDBM canvas, data asset assumptions, risk linkage, cost/revenue model, and judge-facing business thesis.
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Use before presentation, judging, client delivery, sprint closeout, or team handoff. Prepares final demo script, runbook, handoff document, known limitations, change history, maintainer notes, and submission-readiness package.
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Use before architecture, database design, code generation, or AI Agent implementation. Builds an implementation-ready domain model with entities, relationships, states, transitions, events, permissions, and audit relationships.
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محللو ضمان جودة البرمجيات والمختبرون Use when a project needs proof that agent outputs remain useful, grounded, safe, and maintainable over time. Creates organization-specific evals: golden cases, rubrics, failure modes, regression schedule, cross-agent normal-form comparison, and safety gates.
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