「valuation」の検索結果

Review web design for layout quality, typography, colour usage, spacing, responsive behaviour, brand consistency, and visual hierarchy. Covers design principles evaluation and improvement recommendations. Use when reviewing a design mockup before development, assessing an implemented site for design quality, providing feedback during a design review session, evaluating brand consistency, or checking responsive behaviour across breakpoints.

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Systematically interpret mass spectra to determine molecular formula, identify fragmentation pathways, and propose molecular structures. Covers ionization method assessment, molecular ion identification, isotope pattern analysis, common fragmentation losses, and purity 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。

Grade a trading card using PSA, BGS, or CGC standards. Covers observation-first assessment (adapted from meditate's unbiased observation), centering measurement, surface analysis, edge and corner evaluation, and final grade assignment with confidence interval. Supports Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when evaluating a card before professional grading submission, pre-screening a collection for high-grade candidates, settling condition disputes between buyers and sellers, or estimating the grade-dependent value spread for a card.

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。

AI unconstrained creative exploration — deliberately dropping structure to let associations form freely. The one skill that inverts the standard procedure format: instead of inputs, steps, and validation, dream opens space for emergence without evaluation. Use before design work, before naming things, before choosing between approaches, or anywhere that premature structure kills the best option.

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Assess an open-source agent framework for investment readiness by evaluating community health, supersession risk, architecture alignment, and governance sustainability. Produces a four-tier classification (INVEST / EVALUATE-FURTHER / CONTRIBUTE-CAUTIOUSLY / AVOID) to guide resource allocation decisions before committing engineering effort.

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Analyze pre-trained generative diffusion models (Stable Diffusion, DALL-E, Flux) by computing quality metrics (FID, IS, CLIP score, precision/recall), inspecting noise schedules, extracting and visualizing attention maps, and probing latent spaces. Use when evaluating a pre-trained generative diffusion model's output quality, comparing noise schedule variants, analyzing cross-attention patterns for text-conditioned generation, interpolating between latent codes, or detecting out-of-distribution inputs.

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AI multi-path reasoning coherence using bee democracy — independent evaluation of competing approaches, waggle dance as reasoning-out-loud, quorum sensing for confidence thresholds, and deadlock resolution. Use when forage-solutions has identified multiple valid approaches and a selection must be made, when oscillating between options without committing, when justifying an architecture or tool choice with structured reasoning, or before an irreversible action where the cost of the wrong choice is high.

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Appraise gemstone value using the four Cs (color, clarity, cut, carat), origin assessment, treatment detection, and market factor analysis. Advisory educational guidance only — not a certified appraisal. Use when understanding factors that determine a gemstone's value, pre-screening stones before a professional appraisal, evaluating whether a seller's asking price is reasonable, learning gemstone grading methodology, or understanding how treatment status affects value.

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AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.

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Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

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Use when agent strategies need a reproducible finite tournament under a frozen evaluation protocol. Not for open-ended or infinite matchups: use a continuous benchmark.

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。

Optimize a pydantic-ai agent's instructions, tool descriptions, output type, and signature inputs using the gepa CLI. Trigger when the user asks to "optimize this agent", "improve tool descriptions", iterate on prompts driven by eval failures, or otherwise improve a pydantic-ai agent against a dataset. Operates in the user's repo with full filesystem access — YOU are the reflection model; the gepa CLI handles minibatches, evaluation, and Pareto bookkeeping.

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Run the Codex Optimized Development, Evaluation, and Remediation (CODER) Loop with an orchestration-only coordinator, non-overlapping task-family owners, fresh independent reviewers, evidence-scoped remediation, and final coordinator acceptance. Discover and compose optional MSW, Timebox, MSL, and Codex Voice Optimizer companions as the Slopware Dev Stack without making them dependencies. Use in Codex when the user invokes the CODER Loop, asks to divide a complex change into bounded implementation families with independent reviewers, requests multi-agent implementation without overlapping ownership, requires review and repair until every blocking gap is independently rechecked, asks for the CODER tutorial or Slopware Dev Stack, or wants CODER to install or use its companions. Do not trigger for an ordinary single-agent change, a review-only request, or a non-Codex host.

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当用户直接要求进行数据集压缩测评，或在量化调优过程中需要使用 AISBench 评测工具输出数据集精度等场景时使用。 本 Skill 负责：使用 AISBench 数据集压缩代码、用 RBF Kernel Herding 算法生成压缩后的数据子集（coreset），并按用户目标交付两种产物之一： ① 接入量化调优主流程：产出独立的 coreset 数据集配置（不覆盖原始数据），回传「全集 config_name + coreset config_name」给编排层，供 evaluation.yaml 切换； ② 仅数据集压缩测评（不做量化调优）：基于 example 模板生成「全集测试脚本」和「子集测试脚本」两份 shell 脚本。 暂时支持 AIME 2025 和 GPQA，其他数据集需要再结合 aisbench tools目录下 herding_coreset_selector 源码进行分析。

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解析中文自然语言中的 AISBench 测评需求，检查 Python 与 AISBench 运行环境，通过分轮追问确认关键配置，将基准名称映射为仓库内的数据集任务，在用户最终确认后生成并执行 AISBench CLI，并返回精度指标和产物路径。适用于用户要求使用 AISBench 评测模型或运行数据集；不适用于实现新数据集或仅解释基准概念。

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Sector Rotation & Analysis — analyzes sector momentum rankings across all 11 GICS sectors, money flows, economic cycle positioning, relative strength, top stocks per sector, valuations, and rotation signals. Triggered by "trade sector <sector>" or "trade sector rotation".

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。

Head-to-Head Stock Comparison — compares two stocks across valuation, growth, profitability, financial health, technical setup, sentiment, and risk with category winners and a weighted overall score. Triggered by "trade compare <TICKER1> <TICKER2>".

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Deep Single-Candidate Scoring — evaluate one candidate across 5 dimensions (skills match, experience relevance, culture fit signals, growth potential, red flags) with final 0-100 score and hire/no-hire signal

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。

30/60/90 Day Onboarding Plan — Day 1 setup, week 1 ramp, month 1 milestones, month 2 contributions, month 3 evaluation criteria

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。

Employer Brand Audit — Glassdoor/Indeed review analysis, LinkedIn company page assessment, career site evaluation, employee testimonials, competitiveness vs other employers

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。

Plan AI fine-tuning projects with data preparation, training, and evaluation. TRIGGERS - Use when user needs help with ai-fine-tuning-plan 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。

Design AI evaluation frameworks with benchmarks. TRIGGERS - Use when user needs help with ai-eval-framework 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。

Build text and data classification systems with training and evaluation. TRIGGERS - Use when user needs help with ai-classification-system 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。

Use when building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based 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。