'valuation' 검색 결과

Guide for diagnosing and improving MSBuild project evaluation performance. Only activate in MSBuild/.NET build context. USE FOR: builds slow before any compilation starts, high evaluation time in binlog analysis, expensive glob patterns walking large directories (node_modules, .git, bin/obj), deep import chains (>20 levels), preprocessed output >10K lines indicating heavy evaluation, property functions with file I/O ($([System.IO.File]::ReadAllText(...))), multiple evaluations per project. Covers the 5 MSBuild evaluation phases, glob optimization via DefaultItemExcludes, import chain analysis with /pp preprocessing. DO NOT USE FOR: compilation-time slowness (use build-perf-diagnostics), incremental build issues (use incremental-build), non-MSBuild build systems. INVOKES: binlog MCP server tools (evaluations, evaluation_global_properties, evaluation_properties, imports, properties); falls back to dotnet msbuild -pp:full.xml for preprocessing, /clp:PerformanceSummary.

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Evaluate companies using projected cash flows, dividends, business segments, and comparisons with peers. Select suitable valuation methods, examine how assumptions change estimated value, and check for problems that can make a seemingly cheap stock misleading.

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• Selecting valuation methods suited to a company
• Comparing estimated value across multiple methods
• Testing growth-rate and discount-rate assumptions

Compare a listed company with relevant peers using valuation multiples, ratios linking price or company value to financial measures. Produce reports or editable workbooks covering peer selection, implied prices, data quality, and valuation assumptions.

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• Select comparable companies and explain peer choices
• Assess valuation premiums and discounts
• Build an Excel or Sheets valuation workbook

Advanced Evaluation workflow skill. Use this skill when the user needs This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Set up a realtime evaluation folder with a suitable shared harness, the framework that runs evaluations. Prepare prompts, tool definitions, realistic starter data, and a README, then validate the setup with evaluation and test runs.

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• Start a single-turn text-to-speech evaluation
• Set up evaluations using saved audio
• Build multi-turn simulations with mock tools

Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.

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• Planning prompt evaluation before deployment
• Allocating context capacity and designing retrieval-based answers
• Planning an authorized AI agent security assessment

Build an Excel comparison of public companies using financial performance, valuation ratios, and statistical benchmarks. The spreadsheet includes formulas, source notes, medians, and quartiles to show how each company compares with its peers.

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• Comparing public company valuations for M&A
• Benchmarking performance against industry peers
• Identifying unusually high or low valuation ratios

Advanced Evaluation workflow skill. Use this skill when the user needs This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Select, inspect, validate, and report Paperclip evaluations for Runner protocol behavior or complete product workflows. Interpret results with evidence, source revisions, costs, and failure categories, and refresh reports from retained evidence without new provider calls.

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• Choosing between Runner and Product E2E evaluations
• Inspecting evaluation catalogs without credentials
• Interpreting failures using evidence and execution records

Estimate a public company's intrinsic value per share using discounted cash flow, a method that values future cash in today's terms. Produce a Markdown report documenting financial inputs, assumptions, five-year forecasts, and sensitivity to valuation assumptions.

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• Estimate a public company's fair value per share
• Compare the current share price with estimated value
• Check how valuation changes under different assumptions

Create a fixed JSON evaluation suite from a repository's existing tests and check its structure for errors. Use the suite with harness-evolve to compare variants against consistent tasks, independently of the repository's normal test command.

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• Create a fixed evaluation suite from existing tests
• Check evaluation suite structure in CI
• Compare variants across commits using consistent tasks

Build financial models for valuation and investment analysis using discounted future cash flows, assumption testing, and probability simulations. Outputs include Excel models, comparison tables, charts, and valuation ranges to examine possible outcomes and risks.

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• Build a company valuation model from financial statements
• Test how growth and discount rates affect valuation
• Simulate acquisition valuation under uncertain assumptions

Turns supplied AI agent evaluation results into reports that distinguish autonomous success, human-assisted success, failure, timeout, and invalid runs. Makes metric denominators, retries, timing, costs, and comparison conditions explicit to support evidence-based decisions.

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• Separating autonomous and human-assisted success rates
• Reporting evaluations with retries and invalid runs
• Comparing agents or releases under matched conditions

Create and analyze AI model evaluation experiments in Arize using the ax CLI, a command-line tool. Collect real model outputs, export results, compare evaluation scores, and identify examples where a new model performs worse.

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• Comparing models on the same evaluation dataset
• Finding examples that perform worse after a model change
• Exporting experiment results for further analysis

Direct finance questions to the appropriate specialist skill: financial statement analysis or SaaS operating metrics. It helps choose the right workflow for valuation, budgets, recurring revenue, churn, and customer acquisition costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing the right skill for a financial analysis request
• Routing budget and valuation questions
• Routing recurring revenue and churn questions

Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add repeatable quality tests for an AI agent
• Evaluate chatbot behavior across multiple conversation turns
• Generate evaluation datasets from documentation

Audit a completed AI development phase to check whether its planned evaluations were implemented. Produce EVAL-REVIEW.md with a score, verdict, identified gaps, and a plan to address missing coverage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review evaluation coverage after an AI phase is complete
• Check implemented evaluations against AI-SPEC.md
• Document evaluation gaps and remediation steps

Add team-specific evaluation criteria to plugin-eval by designing a metric pack, a set of custom checks and measurements. Create its manifest and JSON-producing script, then run it through the evaluator using a consistent output structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add team-specific evaluation criteria
• Define custom checks and numerical metrics
• Build a metric-pack manifest and script

Guide for diagnosing and improving MSBuild project evaluation performance. USE FOR: builds slow before any compilation starts, high evaluation time in binlog analysis, expensive glob patterns walking large directories (node_modules, .git, bin/obj), deep import chains (>20 levels), preprocessed output >10K lines indicating heavy evaluation, property functions with file I/O ($([System.IO.File]::ReadAllText(...))), multiple evaluations per project. Covers the 5 MSBuild evaluation phases, glob optimization via DefaultItemExcludes, import chain analysis with /pp preprocessing. DO NOT USE FOR: compilation-time slowness (use build-perf-diagnostics), incremental build issues (use incremental-build), non-MSBuild build systems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide for diagnosing and improving MSBuild project evaluation performance. USE FOR: builds slow before any compilation starts, high evaluation time in binlog analysis, expensive glob patterns walking large directories (node_modules, .git, bin/obj), deep import chains (>20 levels), preprocessed output >10K lines indicating heavy evaluation, property functions with file I/O ($([System.IO.File]::ReadAllText(...))), multiple evaluations per project. Covers the 5 MSBuild evaluation phases, glob optimization via DefaultItemExcludes, import chain analysis with /pp preprocessing. DO NOT USE FOR: compilation-time slowness (use build-perf-diagnostics), incremental build issues (use incremental-build), non-MSBuild build systems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Residual Income valuation, EVA Economic Value Added, excess return valuation, book value plus economic profit, financial institution valuation, bank valuation, insurance company valuation

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Dividend Discount Model, DDM valuation, multi-stage dividend model, Gordon Growth Model, dividend growth valuation, mature company valuation, income stock valuation, dividend yield analysis

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Sum of the Parts valuation, segment-based valuation, conglomerate valuation, business segment analysis, breakup value, segment sum valuation, parts worth more than whole, hidden asset value

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.