القائمة
Deep codebase and architecture research playbook. Use for architecture, repository, system-design, migration, or implementation-blueprint research.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Screenshot-first visual end-to-end validation policy for browser-rendered UI and webview jobs after verifier approval.
Default engineering workflow. Classify a technical request, route it through the native T1/T2/T3 engineering fleet, and return only independently verified completion to the orchestrator.
Safely operate Claude Code's opt-in advanced capabilities—sessions, worktrees, agent teams, Skills, MCP, plugins, hooks, Channels, schedules, goals, and Agent SDK integrations—when the user explicitly asks for one. Do not use for an ordinary build request.
Routes selective local Codex plan, code, verification, and visual judgment with evidence-backed blocking and strict cost caps.
Shared evidence, accessibility, context, and handoff rules for the local design-agent fleet.
Produces contextual creative theses and bounded visual directions without generic model defaults.
| name | research-architecture |
| description | Deep codebase and architecture research playbook. Use for architecture, repository, system-design, migration, or implementation-blueprint research. |
| user-invocable | false |
For deep architecture work, write all nine sections in this order: Executive Summary; Repository & Dependency Topology; Architectural Views & Paradigms; Data & State Flow; Performance & Scalability Analysis; Security, Privacy & Supply-Chain Risks; Developer Experience & Extensibility; Implementation Roadmap; Edge Cases, Failure Modes & Recovery Strategies.
Ground topology, dependency direction, synchrony/async boundaries, state ownership, deployment units, critical flows, contracts, bottlenecks, trust boundaries, and operational failure paths in observable repository/configuration evidence. Separate observed design from inference. Make each recommendation actionable, staged, reversible where possible, and advisory to the user’s scope.
