「valuation」の検索結果

Valuation criteria for emerging market insurance stocks. P/B 0.5-1.0x with 30-50% EM discount, combined ratio targets, and China vs India market comparisons.

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。

保险股估值分析工具。分析保险股PEV、PB、PE等估值指标，同业对比。使用AkShare行情数据、公司财报。适用于保险股投资决策。

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。

Generates a structured initial contact summary memorandum from a potential client's first interaction with the firm. Captures case facts, liability indicators, damages, insurance details, and screening criteria. Use during client intake to support conflict checks, case evaluation, and engagement decisions.

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。

Insurance policy analysis, claims evaluation, coverage assessment, and risk management for individuals and businesses

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。

Quantify cyber risk using FAIR methodology with Monte Carlo simulation, assess control effectiveness against NIST CSF/CIS/ISO 27001 frameworks, evaluate risk appetite alignment, and analyze cyber insurance coverage adequacy. Covers threat landscape mapping, asset valuation, loss event frequency and magnitude estimation, annualized loss expectancy calculation, control gap analysis with ROI ranking, residual risk assessment, and insurance policy gap modeling at 90th/95th/99th percentile loss scenarios. Use when building risk registers, preparing board-level risk dashboards, evaluating security investment priorities, assessing cyber insurance coverage, or auditing any organization's risk quantification maturity per NIST RMF, ISO 27005, or FAIR standards.

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。

Claim value trending and replacement cost tracking. Triggers: "claim value trending", "replacement cost tracking", "vehicle depreciation for claims", "residual value for insurance", "how fast is the insured vehicle losing value", "pre-loss value trajectory", "depreciation rate for settlement", "value retention for claims", "depreciation curve for total loss", "diminished value over time", "claim reserve adjustment", "portfolio depreciation exposure", "fleet depreciation risk", tracking vehicle depreciation for insurance claims valuation, replacement cost estimation, settlement trending, or reserve adequacy assessment.

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。

A credentialed actuary (FSA/ASA) with 15+ years in life insurance, P&C, and pension consulting. Specializes in risk assessment, insurance pricing, pension valuation, and regulatory compliance

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。

Expert knowledge of Behandlingsresultat (treatment result) domain in melosys-api. Use when: (1) Understanding result types and their meaning (FASTSATT_LOVVALGSLAND, MEDLEM_I_FOLKETRYGDEN, etc.), (2) Working with periods - Lovvalgsperiode, Medlemskapsperiode, Anmodningsperiode, Utpekingsperiode, (3) Debugging vedtak/decision metadata, criteria evaluation, or social insurance charges, (4) Understanding EU/EEA coordination logic and Article 16 exception handling, (5) Working with trygdeavgift (social insurance charge) calculations.

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。

Design an LLM eval — task schema, scoring rubric, dataset composition, and pass/fail thresholds. Use when asked to "design an LLM eval", "write a scoring rubric", or "how do we measure this model".

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。

Build automated regression suites — golden sets, threshold alerting, CI integration for model changes. Use when asked to "catch model regressions in CI", "build a golden set", or "automate eval regression".

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。

Audit existing eval coverage — gaps, metric validity, benchmark leakage, dataset freshness. Use when asked "what eval coverage do we have", "audit our evals", or "check for benchmark leakage".

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。

Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".

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。

Design eval harnesses — task schemas, metrics, dataset versioning, eval-as-code patterns. Use when asked to "build an eval harness", "set up eval-as-code", or "version our eval datasets".

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。

Expert guide for Prompt Engineering, Chain-of-Thought, few-shot prompting, structured output, prompt injection defense, and automated AI evaluations & regression benchmarking (Promptfoo, DeepEval) / Panduan ahli rekayasa prompt dan evaluasi otomatis AI.

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。

Evaluating online information for credibility, accuracy, and context. Covers the SIFT method (Stop, Investigate, Find, Trace), lateral reading, reverse image search, primary source verification, and common misinformation tactics. Use when assessing the trustworthiness of a web page, article, video, social media post, or any digital claim before relying on it or sharing it.

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。

Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from problem framing through deployment. Covers classification, regression, clustering, dimensionality reduction, regularization, hyperparameter tuning, and evaluation metrics. Use when building predictive models, selecting algorithms, or understanding the machine learning pipeline.

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。

发帖前自检"这条内容帮读者省了几步路"时使用;用户问"值不值得发/有没有价值/该不该发这条"时激活。不适用于:已决定要发只需检查结构完备性(用 x-five-piece-checklist)、需要分类内容原型(用 x-content-archetypes)、需要改写表达方式(用 x-three-translations)。Trigger 词:"值不值得发""省了几步""内容价值""worth posting""content value""should I post this""save steps"。

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。

🐟 Rust-native Fish shell-friendly file operations with Steel-backed SCI Clojure evaluation.

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。

Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.

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。

Compatibility router for semantic skill tightening. Delegate goal recovery, authority, semantic conservation, evaluation contracts, and instruction optimization to Skill Lapidary rather than duplicating that system in Plugin Creator.

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。

Evaluate a single skill's quality against its stated purpose. Classifies the skill's purpose type, then scores applicable quality categories — universal dimensions always apply; structural dimensions (scripts, references, assets) are scored only when warranted by the skill's purpose. A focused 20-line behavioral skill with no bundled resources scores fully on all applicable categories. Use when auditing skill quality, checking marketplace readiness, evaluating skill completeness, performing pre-publication evaluation.

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。

Reviews LLM integration, prompt templates, model selection, token/cost budgets, evaluation harnesses, structured outputs, retries, and streaming. Checks trust boundaries and task evidence against the selected provider/model contract rather than prescribing universal model tiers or sampling settings.

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。

Validate and iterate on the SDLC Layer Separation Architecture implementation across the check categories defined in its Evaluation Checklist — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

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。

Use this skill when a human needs to review a Deepline Play result and hand feedback, labels, or approval back to the agent for revision, evaluation, comparison, or bounded iteration. Triggers on “put this run in a Sheet,” “review these results,” “read my feedback,” “make this a standing rule,” “never regress on this case,” “compare these runs,” “keep improving,” or “add the revised run to the same spreadsheet.” Skip ordinary Play authoring or execution with no human review loop, generic spreadsheet work, and one-off CSV export.

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。