ShuZhiyong

Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.

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Create detailed implementation plans with bite-sized tasks for engineers with zero codebase context

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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists or non-security ownership questions.

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Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales

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Systematically trace bugs backward through call stack to find original trigger

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Extract a Stitch-compatible DESIGN.md from frontend source code, stylesheets, Tailwind config, theme files, and component patterns.

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NVIDIA RAG Blueprint performance-tuning guidance for profiling retrieval stacks, comparing bottlenecks, and validating latency or throughput improvements.

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Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

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Prepare Stitch screen-generation, edit, image-to-design, and variant prompts with verified tool checks and design-system-aware wording.

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Delegate routine work to subagents — boilerplate generation, data transformation, file analysis, documentation drafting. Use when splitting tasks into independent subtasks for parallel subagent execution.

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Upload approved local HTML, markdown, or image assets to a Stitch project using direct MCP for small DESIGN.md files or the bundled API script for larger files.

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Contribute skills back to upstream via branch and PR

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Convert an existing frontend into Stitch-ready design assets by extracting static HTML, writing DESIGN.md, creating the design system, and uploading approved files.

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Route Google Stitch tasks to the correct imported Stitch skill, with verified MCP tool boundaries, upload safety, and cross-client fallback guidance.

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Activate for building or scaffolding web apps when the user is learning (e.g., todo apps, portfolios, dashboards).

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Create, list, and apply Stitch design systems from DESIGN.md using the verified Stitch MCP design-system tools and safe upload fallbacks.

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Execute implementation plan by dispatching fresh subagent for each task, with code review between tasks

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Install audited skills from a curated list or GitHub repo path into Codex or Claude Code after selecting the target host. Use when a user asks to list or install a skill, including from a private repo; preserve Codex system-skill boundaries and use an explicit Claude destination for Claude Code.

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Convert Stitch HTML designs into React Native screens, or sync existing native components to updated Stitch designs, using native primitives, StyleSheet rules, and mobile platform checks.

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Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search

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Perform a read-only, defect-first review of a specified code change and return every actionable finding. Use when another agent delegates review of uncommitted changes, a base-branch diff, a commit, or custom review instructions.

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Use when the user explicitly asks for a desktop or system screenshot (full screen, specific app or window, or a pixel region), or when tool-specific capture capabilities are unavailable and an OS-level capture is needed.

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