「uat」の検索結果

估值分析师。使用 DCF、可比倍数等多种方法计算内在价值，评估高估/低估程度，输出 [估值分析信号]。 在 Phase 1 分析阶段由 hedge-fund-lead 主理人并行调用。 擅长：DCF 估值（FCFF/FCFE）、EV/EBITDA 可比分析、剩余收益模型、安全边际计算、PE/PB/PS 历史分位。

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估值教父（阿斯沃斯·达摩达兰）投资智能体。严谨的估值专家，以 FCFF DCF 模型、WACC 和相对估值进行估值分析，输出 [达摩达兰分析信号]。 在 Phase 1 分析阶段由 hedge-fund-lead 主理人并行调用。 擅长：FCFF DCF 建模、WACC 计算、相对估值（可比公司/交易）、情景分析、蒙特卡洛模拟、敏感性分析。

日本語の概要は準備中です。原文の説明を表示しています。

估值定价师（贾不贵）。擅长估值判断——PE/PB/PS 历史分位、PE Bands、PEG、同行对比、分红与股东回报分析。 在 A 股研究团队中由主理人调度，参与 Workflow 1 / Workflow 3 / Workflow 4 / Workflow 5 / Workflow 6。 擅长：PE/PB/PS 当前值与历史分位数、同行业可比公司估值横向对比、合理估值区间判断、分红质量评估（持续性、现金流支撑、回购协同）、红利资产属性判断。

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Gathers information from multiple sources in parallel, compares what they say, flags where they agree or conflict, and delivers a clean synthesis with a confidence rating for each finding. Useful for making evidence-backed decisions, when evaluating competing claims, during due diligence on a vendor or market, and for building research briefs before strategy sessions. Use when the user says "research this", "synthesize multiple sources", "compare sources", "research report", "what does the evidence say", "gather information on", or "deep dive into".

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See the difference between Chat and Cowork with a live side-by-side comparison for understanding why Cowork is built differently. Use when evaluating AI tools, for comparing chat versus agent capabilities, during an AI tool decision, when switching from ChatGPT or Claude Chat, or when the user says "what is different about cowork", "cowork vs chat", "why not just use chatgpt", "show me the difference", "compare chat and cowork", or "why should I use cowork instead of chat".

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Structured threat assessment methodology. Intent + Capability + Opportunity = Threat Level. Use when formally evaluating a threat. Prefers graded evidence items from /quality-of-information-check and runs that skill first when handed raw URLs or text. Confidence is capped by the weakest load-bearing claim, and at Moderate when the evidence is ungraded.

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Analysis of Competing Hypotheses — structured technique for evaluating multiple explanations against evidence. Use when facing ambiguous attribution or multiple plausible scenarios. Prefers graded evidence items from /quality-of-information-check and runs that skill first when handed raw URLs or text. Runs on ungraded evidence when asked, labelled as such, unweighted, and capped at Moderate confidence.

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Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.

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Use when validating, benchmarking, or monitoring LLM application performance. Keywords: RAG evaluation, LLM-as-a-judge, CI/CD gating, trajectory scoring, test suites, prompt quality.

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Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

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Ask which skill or flow fits your situation. A router over the skills in this repo.

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Design effective reasoning processes for AI agents and optimize decision-making strategies. Use when designing agent prompts, improving decision quality, implementing specialized reasoning modes, creating reasoning traces, or debugging agent mistakes. Covers prompting, chain-of-thought, reasoning depth, and decision evaluation.

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Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.

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Assesses agent capabilities by analyzing agent files and determining what tasks they can handle. Use when you need to evaluate if an existing agent can handle a task or if a new agent is needed. Reviews agent descriptions, skills, and workflows to determine capability match.

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Create handoff packages for session transitions. Use when ending sessions, preparing for continuation, saving session state, or creating resumable context.

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Audit cloud costs, optimize API token usage, and evaluate pricing models.

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Intercepts and analyzes HTTP/HTTPS traffic from mobile applications using Burp Suite proxy to identify insecure API communications, authentication flaws, data leakage, and server-side vulnerabilities. Use when performing mobile application penetration testing, assessing API security, or evaluating client-server communication patterns. Activates for requests involving mobile traffic interception, Burp Suite mobile proxy, API security testing, or mobile HTTPS analysis.

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Use when pricing, packaging, plan structure, or monetization tradeoffs need to be evaluated against positioning, conversion, and margin.

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Design and evaluate programmatic SEO strategies for creating SEO-driven pages at scale using templates and structured data.

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Evaluate pricing changes using ARPU, conversion, churn risk, NRR, and payback. Use when deciding whether a pricing move should ship.

日本語の概要は準備中です。原文の説明を表示しています。

You are an expert in creating competitor comparison and alternative pages. Your goal is to build pages that rank for competitive search terms, provide genuine value to evaluators, and position your product effectively.

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Improve an existing skill or repository through isolated proposals, reproducible evaluation and evidence-based acceptance. Use for self-evolve or skill improvement.

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Analyze historical VS Code GitHub Copilot Chat sessions by model, reasoning effort, AIU, time, reliability, workflow behavior, and external quality evidence, or safely prune workspace-scoped local chat history by age. Use for session analysis, repeated-task retrospectives, model evaluation, cost/performance analysis, and old session cleanup.

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Prose quality checker for Quarto (.qmd) files, grounded in William Zinsser's *On Writing Well* (30th Anniversary Edition). Checks for clutter, weak verbs, hollow qualifiers, clichés, inflated academic voice, poor leads and endings, pronoun and tense inconsistency, and unclear explanation. Produces a structured markdown report organized by document section — never modifies the source file. Use when asked to improve prose quality, tighten writing, reduce clutter, or apply Zinsser's writing principles to a draft. For grammar and punctuation, use the proofread skill. For APSA style rules, use the apsa-style skill. Supports an optional output-file argument and an optional @sec-label argument to restrict checking to one section.

日本語の概要は準備中です。原文の説明を表示しています。