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"M&A integration playbook covering eight modules: strategic rationale, target screening, due diligence (financial/legal/commercial), valuation with valuation bridge, synergy analysis, deal structuring (stock vs. asset, cash vs. equity, earn-out), SPA key clauses, and post-merger integration (PMI). Use for deal evaluation, valuation disputes, structure design, earn-out design, synergy breakdown, integration risk, or hostile takeover defense. Triggers: 『併購』『收購』『M&A』『盡職調查』『DD』『估值橋』『綜效』『earn-out』『換股比例』『PMI』『買殼』『借殼上市』『敵意併購』『交易結構』. For Taiwan EMBA 財管組 case studies and term reports (台大／政大／陽交). Complements Asgard `biz-dcf` and `fin-modeling` (valuation tools) plus `biz-corporate-governance` (governance layer) by providing the transaction-layer framework.".

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Use when testing, evaluating, or building regression suites for Agentforce agents: conversation testing in Agent Builder, topic (now subagent) coverage and utterance testing, Testing API and AiEvaluationDefinition metadata, Agentforce DX CLI test runs (sf agent generate test-spec, sf agent test create/run/resume/results/list), evaluation metrics (containment rate, escalation rate, CSAT, topic activation accuracy), and post-deploy analytics via Enhanced Event Logs. Triggers: 'how do I test my Agentforce agent', 'agent routes to wrong subagent', 'write utterance tests', 'regression test after topic change', 'measure agent quality', 'agent containment rate', 'run agent tests from the CLI'. NOT for agent creation, topic design, or action contract design — use agentforce/agentforce-agent-creation, agentforce/agent-topic-design, or agentforce/agent-actions respectively.

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Evaluate recommender system quality for the CSX4207 Vinyl Record Store. Use whenever you must compute or report recommender metrics (Precision@k, Recall@k, HitRate@k, MRR, MAP@k, NDCG@k, coverage, diversity, novelty, serendipity, personalization), design an evaluation protocol/split, run a baseline comparison, or write the evaluation section of a course deliverable. Covers formulas, JavaScript reference implementations, and the reporting checklist.

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Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO/RLHF.

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使用统一标准审阅论文和研究计划，检查研究方法、证据、引用及文章结构，并说明评分依据、修改优先级和下一步需要修改的内容。

• 投稿前审阅论文
• 检查引用是否支持论点
• 评估研究方法与局限

将应用开发分配给规划、实现和评估代理，检查运行中的界面并按质量标准评分，把具体问题反馈给实现环节，反复修改直至达标或达到次数上限。

• 从简短构想生成应用规格
• 通过独立评估改善界面
• 验证并修正全栈应用

根据工厂、仓库和办公室的用量、费率及合同条件，评估电力与天然气采购方案，支持供应商报价比较、峰值需量费用管理、可再生能源合同评估及年度预算编制。

• 比较多个场所的能源采购报价
• 审查费率方案与峰值需量费用
• 评估可再生能源合同及证书

将未来现金流折算为现值，生成计算企业价值和每股估值的Excel文件。支持修改假设后重新计算、比较不同情景，并通过敏感性分析查看估值变化。

• 制作企业估值Excel文件
• 比较悲观、基准和乐观情景
• 查看假设变化对估值的影响

在付费评估前确定通过标准、比较条件、干扰项目和停止规则，先进行零费用演练，再开展三项付费试评，并用支出表为后续实施审批提供依据。

• 支出前确定评估标准
• 进行零费用演练
• 用三项付费试评核查支出

为Paperclip Runner添加或扩展行为评估，定义判定条件、执行目标和报告验证数据，利用正常及异常证据验证结果，并保留执行记录与费用。

• 添加Runner行为评估
• 用有效与无效证据检验判定
• 更新执行列表和持续评估

为Paperclip新增或扩展贯穿真实浏览器、服务器、数据库和runner的端到端评估，整理测试数据、判定条件与证据，并在现有报告中查看操作结果。

• 新增完整用户操作流程的评估
• 更新测试数据与判定条件
• 在评估报告中加入截图和产物

Build an evaluation process for AI agents using success, reliability, cost, and latency measures. Define scoring criteria, repeatable environments, failure analysis, and regression tests that check for recurring problems to support model selection and system improvements.

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• Building an initial agent evaluation process
• Comparing models on actual work tasks
• Investigating a drop in evaluation scores

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

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• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Builds an Excel model to estimate a company’s equity value from future cash flows. It calculates financing costs, compares three business scenarios, and shows how changes in growth, margins, and other assumptions affect valuation.

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• Estimating a company’s value from future cash flows
• Comparing bear, base, and bull scenarios
• Testing how growth and financing costs affect valuation

Create shareable links that open specific Arize resources, including traces, datasets, and evaluators. Use IDs from logs or exported data, with suitable time ranges, to direct teammates to the relevant view.

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• Share a trace with teammates
• Open an Arize view from IDs in logs
• Link directly to a dataset or evaluator

Develop an eight-part article series examining one company's competitive strengths, earnings, assets, management, and valuation. Review figures, sources, accounting definitions, and claims across the series to prepare consistent articles for public publication.

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• Planning an eight-part company analysis series
• Checking figures and definitions across articles
• Revising unsupported claims before publication

Plan technology strategy and engineering team growth with frameworks for technical debt, hiring, architecture decisions, and vendor evaluation. Use Python tools and decision templates to prioritize improvements, structure teams, and track engineering performance.

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• Prioritizing technical debt reduction
• Planning hiring and engineering team structure
• Comparing technologies and vendors

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

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• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times

Design a RAG pipeline that retrieves documents to support AI answers. Analyze document splitting, select embedding models and vector databases, and evaluate retrieval quality using test queries and known relevant documents.

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• Designing a RAG system for internal documents
• Choosing how to split documents for search
• Comparing embedding models and vector databases

Analyze financial statements, compare budgets with actual results, and forecast revenue and cash flow. Estimate company value using DCF, which discounts future cash flows, and produce reports with scenario comparisons and sensitivity tables for business decisions.

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• Assess financial health using key ratios
• Estimate company value and compare valuation assumptions
• Explain budget differences by department

Evaluate whether Google SecOps rules detect threats described in articles or supplied text. Generate simulated attack logs, identify coverage gaps, summarize matching rules, and create rules for uncovered threats, with user approval before adding them.

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• Evaluate detection coverage for threats described in a blog
• Test existing rules with simulated attack logs
• Review matching rules and their alerting status

Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.

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• Compare On-Demand pricing with Editions
• Review storage billing models and retention periods
• Design table partitioning and clustering

Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.

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• Turning vague requests into precise task briefs
• Defining stopping rules for long autonomous runs
• Specifying deliverables for parallel workers

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

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• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies