'lua' 검색 결과

Use the corp-finance-mcp server tools for specialty finance, regulatory, and compliance calculations. Invoke when performing private credit (unitranche, direct lending, syndication), insurance (loss reserving, premium pricing, Solvency II SCR), FP&A (variance analysis, break-even, working capital, rolling forecast), wealth management (retirement planning, tax-loss harvesting, estate planning), restructuring (recovery analysis, distressed debt), real assets (property valuation, project finance), venture capital (dilution, convertible instruments, fund returns), ESG (scoring, climate/carbon, green bonds, SLL), regulatory capital (Basel III, LCR/NSFR, ALM), compliance (MiFID II best execution, GIPS reporting), credit derivatives (CDS pricing, CVA/DVA), convertible bonds (binomial tree pricing, scenario analysis), lease accounting (ASC 842/IFRS 16, sale-leaseback), pension & LDI (funding analysis, liability-driven investing), sovereign risk (bond analysis, country risk), real options (binomial valuation, decision trees), equity research (SOTP, target price), commodity trading (spread analysis, storage economics), treasury management (cash management, hedge effectiveness), infrastructure finance (PPP models, concession valuation), crypto (token valuation, DeFi analysis), municipal bonds (pricing, credit analysis), structured products (notes, exotic), trade finance (LC, supply chain), fund structuring (US onshore, UK/EU, Cayman/BVI offshore, Luxembourg/Ireland), transfer pricing (BEPS/Pillar Two, intercompany pricing), tax treaty (treaty network optimization, holding structures), FATCA/CRS (reporting, entity classification), economic substance (multi-jurisdiction testing), regulatory reporting (AIFMD Annex IV, SEC Form PF, CFTC CPO-PQR), AML compliance (KYC risk scoring, sanctions screening), fund of funds (J-curve, commitment pacing, manager selection, secondaries pricing), bank analytics (NIM analysis, CAMELS rating, CECL provisioning, deposit beta, loan book), carbon markets (credit pricing, ETS compliance, CBAM, offset valuation, shadow carbon price), private wealth (concentrated stock, philanthropic vehicles, wealth transfer, direct indexing, family governance). All computation uses 128-bit decimal precision.

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Evaluates factor crowding with positioning analysis, valuation spread monitoring, and unwind risk assessment. Use when analyzing factor crowding, assessing unwind risk, or monitoring factor valuation extremes.

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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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논문과 연구 계획을 공통 기준으로 검토하고 연구 방법, 근거, 인용, 글의 구성을 점검합니다. 평가 이유와 수정 우선순위, 다음 수정 사항을 정리합니다.

• 투고 전 논문 검토
• 인용 문헌의 주장 뒷받침 여부 확인
• 연구 방법과 한계 평가

앱 개발을 계획·구현·평가 에이전트로 나누고, 실행 중인 앱의 화면과 기능을 검증하며 설정한 품질 기준이나 반복 횟수 상한까지 수정을 이어 갑니다.

• 짧은 아이디어로 앱 명세를 만듭니다
• 별도 평가를 통해 화면을 개선합니다
• 풀스택 앱의 동작을 검증하고 수정합니다

공장과 사무실의 사용량, 요금 체계, 계약 조건을 바탕으로 전력과 천연가스 조달을 검토합니다. 공급업체 비교, 최대 수요 요금 대책, 재생에너지 계약 평가와 예산 수립을 지원합니다.

• 여러 사업장의 에너지 견적 비교
• 요금제와 최대 수요 요금 검토
• 재생에너지 계약과 인증서 평가

미래 현금흐름을 현재 가치로 환산해 기업 가치와 주당 평가액을 계산하는 Excel 파일을 만듭니다. 가정 변경에 따른 재계산, 시나리오 비교, 민감도 분석을 지원합니다.

• 기업 평가용 Excel 파일 만들기
• 비관·기준·낙관 시나리오 비교
• 가정 변경에 따른 평가액 변화 확인

유료 평가 전에 합격 기준, 비교 조건, 교란 항목과 중단 규칙을 정합니다. 무료 예행연습과 세 항목의 유료 시험 평가를 거쳐 지출표를 후속 승인에 활용합니다.

• 지출 전에 평가 기준 정하기
• 비용 없이 예행연습 진행하기
• 세 항목의 유료 시험으로 지출 확인하기

Paperclip Runner의 동작 평가에 판정 조건, 실행 대상, 보고서 검증 데이터를 추가하고 증거와 실행 이력을 남겨 검증합니다.

• Runner 동작 평가를 추가합니다
• 정상 및 잘못된 증거로 판정을 검증합니다
• 실행 대상 목록과 지속 평가를 갱신합니다

Paperclip에서 실제 브라우저부터 서버, 데이터베이스, runner까지 검증하는 E2E 평가를 추가·확장합니다. 테스트 데이터와 판정 조건, 증거를 기존 보고서에 연결합니다.

• 전체 사용자 작업 흐름의 평가 추가
• 테스트 데이터와 판정 조건 갱신
• 평가 보고서에 스크린샷과 산출물 추가

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

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

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

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