materialofair

Professional Codex-native multi-agent code review with confidence scoring

架构规划专家 - 使用 Codex 本地上下文、仓库证据和只读 child agent 进行大型系统架构设计与规划。

Deprecated compatibility stub. Prefer Codex-native multi-agent research, consensus, thinkdeep, or architect-planner.

根据开发任务自动选择最适合的MBTI人格，让AI以专业人格状态处理对应任务

Use when the user explicitly asks for Conductor, wants track/spec/plan/review artifacts on disk, or needs long-lived project context management. Prefer start-dev for small or one-off tasks. If Conductor is not configured in the project, start with setup.

多视角共识决策 - 使用 Codex 主线与只读 child agent 获取架构、实现和风险视角并综合决策。

深度调试分析 - 使用 Codex 本地证据检索、命令验证和只读 child agent 进行系统性根因诊断。

Designer-developer workflow for UI/UX work using Codex-native frontend design, local project conventions, and optional read-only review agents.

Use when writing, reviewing, or refactoring SwiftUI code for iOS or macOS, including state management, view composition, performance, Liquid Glass adoption, or Instruments `.trace` capture/analysis for hangs, hitches, CPU hotspots, or excessive view updates.

Convert an H5 / web app's source into a native SwiftUI iOS app by native rewrite (NOT a WebView shell, NOT a transpiler). Use when the user wants to port, re-implement, or migrate a web/H5 frontend to native SwiftUI with high visual fidelity, asks to "turn this web app into a real iOS app", or wants a measured render-diff convergence loop against a browser baseline. Auto-detects the web stack (v1: vanilla + React; other stacks are detected then gated, not guessed), extracts design tokens, calibrates a cross-renderer fidelity floor, rewrites per component, and drives a bounded render→diff→correct loop that reports a quantified visual residual plus an independent judge verdict. It does NOT promise literal pixel-identity: cross-renderer differences impose a measured floor it reports honestly. Triages canvas/WebGL/complex-animation/3rd-party-SDK/backend surfaces instead of silently emitting wrong code.

Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". Specially handles short Chinese bug reports (the dominant real-world use case): runs Bug Report Triage to extract repro/expected/actual/environment, asks up to 3 clarifying questions if ≤ 2 fields are present, and inserts systematic-debugging as a hard prerequisite before any code change. DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization (unless the user exp

Distill methodology from superpowers and BMAD-METHOD docs into the active conductor track's spec.md / plan.md / review.md as intent-layer overlays. Use when user asks to "distill superpowers into conductor", "把 superpowers/bmad 提炼到 conductor", "import method docs to track", "seed conductor with brainstorming/TDD/verification gates", or "feed BMAD PRD/architecture/story/QA principles into the track". Writes only inside marker blocks so it never competes with code-derived facts and re-runs cleanly. DO NOT use for general superpowers invocation, code refactors, or to summarize this codebase.

Humanize PROSE drafts — articles, blog posts, public-account 公众号 copy, marketing copy, sermons, technical write-ups — that read like an LLM produced them. Chinese-first, supports zh/en mixed text. Cross-checks output against the Wikipedia "Signs of AI writing" taxonomy before delivery. TRIGGER when user asks to remove AI tone or humanize TEXT/DRAFT/ARTICLE/COPY/稿/文章/帖子: "去 AI 味", "去ai味", "降低机器感", "改得像真人写的", "不要 AI 腔", "humanize this draft", "humanize this article", "remove AI tone", "de-ai this writing". DO NOT TRIGGER for code cleanup, dead-code removal, AI-generated source code, or "deslop" of codebases — those belong to ai-slop-cleaner. DO NOT TRIGGER for prompt optimization — that is prompt-optimizer.

Maintainer-only workflow for semantically optimizing OMC guideline sources into a concise canonical .local/guidelines/CLAUDE.md output. Use when curating external prompt-guideline repos such as andrej-karpathy-skills.

AI-native code annotation protocol that encodes intent, risk, dependencies, constraints, and test expectations in machine-parseable comments.

Deep analysis and investigation for bugs, architecture issues, and risk hotspots. Use when the user asks to analyze, investigate, debug, or explain why something is failing.