1,274 件 ・ 関連度順
概要と使いどころ
Constructs venture return models with entry valuation, follow-on reserve, multiple scenario exits, and portfolio-level fund math. Use when modeling VC returns, calculating fund economics, or projecting portfolio outcomes.
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。
Evaluates single-asset GP-led transactions with stand-alone asset valuation, financing structure, and rolling LP vs cashing out analysis. Use when analyzing single-asset CVs, evaluating trophy asset transactions, or structuring single-asset rolls.
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。
Evaluates factor timing strategies with macro regime indicators, valuation spreads, and momentum signals for factor rotation. Use when analyzing factor timing, evaluating rotation signals, or designing tactical factor allocation.
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。
Structures IAC/LPAC design with composition, authority scope, conflict review protocols, and valuation oversight. Use when designing advisory committees, defining LPAC authority, or structuring conflict resolution processes.
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。
Evaluates conglomerate discount with SOTP analysis, segment-level valuation, and separation benefit quantification. Use when analyzing conglomerate value, building SOTP models, or evaluating break-up scenarios.
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。
Evaluates corporate vulnerability to shareholder activism with governance assessment, valuation gaps, and operational improvement opportunities. Use when assessing activist risk, identifying vulnerabilities, or preparing defensive analyses.
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。
Review user experience and interface design using Nielsen's heuristics, WCAG 2.1 accessibility guidelines, keyboard and screen reader audit, user flow analysis, cognitive load assessment, and form usability evaluation. Use when conducting a usability review before release, assessing WCAG 2.1 accessibility compliance, evaluating user flows for efficiency, reviewing form design, or performing a heuristic evaluation of an existing interface.
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。
Organize, track, and value a trading card game collection. Covers inventory methods, storage best practices, grade-based valuation, want-list management, and collection analytics for Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when starting a new collection and setting up inventory tracking, cataloging an existing collection that has grown beyond casual knowledge, valuing a collection for insurance or sale, or deciding which cards to submit for professional grading based on value potential.
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。
Comprehensive primary skill for agents working with Weights & Biases. Covers both the W&B SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (GenAI traces, evaluations, scorers). Includes helper libraries, gotcha tables, and data analysis patterns. Use this skill whenever the user asks about W&B runs, Weave traces, evaluations, training metrics, loss curves, model comparisons, or any Weights & Biases data — even if they don't say "W&B" explicitly.
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。
Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.
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 evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
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。
执行模型测评（统一入口）。LLM/VLM 走主流程 scripts/run_evaluation.py + Evaluation YAML；model_family=dit 走 DiT 扩展节（references/dit/evaluate_workflow.md，vbench.py 批量推理产出视频）。
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。
Fundamental Analysis skill — valuation ratios, growth metrics, profitability, balance sheet health, competitive moat analysis, and management quality. Returns Fundamental Score (0-100). Triggered by "trade fundamental <TICKER>".
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 AI copilot performance. TRIGGERS - Use when user needs help with ai-copilot-evaluation related tasks.
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 AI agent performance and reliability. TRIGGERS - Use when user needs help with ai-agent-evaluation related tasks.
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 third-party logistics providers. TRIGGERS - Use when user needs help with 3pl-evaluation related tasks.
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。
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
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 Discounted Cash Flow (DCF) valuation models to estimate intrinsic value. Use this skill when the user needs to value a company, evaluate an investment, estimate fair share price, or build financial projections — even if they say 'what is this company worth', 'should we acquire them', or 'build me a valuation 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。
Testing and evaluation framework for LLM prompts and applications -- promptfooconfig.yaml, assertions, model-graded evals, red teaming, CI/CD integration, custom providers, and comparative 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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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 to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server is available. Don't use for general Google Compute Engine instance management, VPC configuration, or standard IAM auditing.
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。
Router for the 53 SfSkills `agentforce` skill packages. Agentforce and Einstein: agents, topics, actions, prompt templates, grounding, guardrails, evaluation and production readiness. Use when the request mentions Agentforce, agent topic, agent action, prompt builder, Einstein, Trust Layer, grounding, RAG, guardrails, agent evaluation, prompt injection. Finds and opens the exact skill package to read; it does not contain the guidance itself.
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。
Produces structured asset and liability summaries from financial documents for legal proceedings. Extracts valuations, ownership classifications, and encumbrances, then outputs net-worth overview, categorized schedules, disputed items, and documentation gaps. Use when preparing financial summaries for divorce/dissolution, estate planning, bankruptcy, business valuation, or settlement negotiations. Trigger on: net worth summary, marital estate, balance sheet, asset schedule, liability schedule, community/separate property.
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 when loading, listing, composing, or validating graded evaluation rubrics — PASS/WARN/FAIL grading with weighted criteria and balanced PASS/FAIL anchor examples. Triggers on /mk:rubric, "load rubric", "compose rubric preset", "validate rubric", or evaluator-style code review that needs anchor-grounded judgments.
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。