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Found 1,183 Skills
Conduct multi-dimensional comparative analysis based on user-input technical options or project requirements, and output structured technology selection reports. Applicable scenarios: front-end framework selection, back-end technology comparison, database selection, deployment solution evaluation
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates.
Turn rough ideas into structured, validated idea documents through collaborative dialogue. Explores context, asks clarifying questions one at a time, proposes alternative approaches with feasibility evaluation, and produces documents ready for requirements definition. Use when: "ideation", "brainstorm", "new idea", "explore an idea", "I want to build", "what if we", "let's think about", "propose approaches", "evaluate this idea", "idea document", "アイデア出し", "案出し", "ブレスト", "アイデアを整理", "検討したい".
Evaluate Clojure code via nREPL using clj-nrepl-eval. Use this when you need to test code, check if edited files compile, verify function behavior, or interact with a running REPL session.
Evaluate and improve user experience of interfaces (CLI, web, mobile)
This skill is used when users explicitly request "review NSFC proposals", "simulate expert review", or "evaluate NSFC applications". It simulates the perspective of domain experts to conduct multi-dimensional reviews of NSFC proposals, outputting graded issues and actionable modification suggestions. ⚠️ Not applicable: when users only want to write/modify a specific section of a proposal (use the nsfc-*-writer series skills instead), only want to understand review criteria (answer directly), or have no clear "review/evaluate" intent.
Score how well a creator fits a brand's niche on a 1-10 scale with detailed written rationale. This skill should be used when evaluating creator-brand fit, scoring niche alignment, checking if an influencer matches a brand, assessing creator relevance, rating a creator's fit for a campaign, vetting a creator for niche match, deciding whether a creator is right for a brand, comparing creators by brand fit, or reviewing an influencer's profile against campaign requirements. For full creator vetting beyond niche fit (brand safety, rates, compliance), see creator-vetting-scorecard. For writing outreach to creators who pass vetting, see outreach-writer.
Automatically collect hot topics in the AI field or complete AI technical article writing in the writing style of 'Second Brother' according to specified topics. It focuses on actual tests of AI Coding tools (Claude Code, Qoder, Cursor, TRAE, etc.), engineering implementation of large models (SpringAI, LangChain, RAG, etc.), AI Agent and workflow orchestration, evaluation of domestic large models (GLM, Tongyi Qianwen, DeepSeek, MiniMax, Kimi, etc.), and evaluation of various AI tools and Agent tools. Trigger keywords: write an AI article, AI technical article, large model evaluation, AI tool actual test, GLM, Claude Code, Qoder, Cursor, TRAE, SpringAI, RAG, Agent, workflow, domestic large model, collect AI hot topics, AI topic, etc.
Benchmark compensation against market data. Trigger with "what should we pay", "comp benchmark", "market rate for", "salary range for", "is this offer competitive", or when the user needs help evaluating or setting compensation levels.
Teaches learners to extract transferable design lessons from real-world codebases through critical evaluation and systematic exploration. Use when a learner wants to study existing code to learn patterns, architecture, or design decisions—not just understand what it does. Guides through navigation, pattern recognition, critical evaluation (deliberate choice vs. compromise), and lesson extraction. Triggers on phrases like "learn from this codebase", "study how X is implemented", "understand design patterns in Y", or when a learner wants to improve by reading real code.
Critically review terminal user interfaces for UX quality, responsiveness, visual design, and interactivity. Use when asked to "review my TUI", "test my TUI UX", "audit my terminal UI", "check TUI responsiveness", "review TUI keybindings", "check interactivity", or any request to evaluate the user experience quality of a ratatui/crossterm/ncurses-based terminal application. Launches the TUI in tmux, systematically tests 10 dimensions (responsiveness, input conflicts, visual clarity, navigation, feedback loops, error states, layout, keyboard design, permission flows, visual design & color), and produces a graded report with screenshots and specific findings. Benchmarks against Claude Code, OpenCode, and Codex — the three best-in-class AI terminal UIs.
Use only when the user explicitly requests brainstorming, evaluating architecture choices, or comparing options where no single concern dominates