「estimation」の検索結果

Use scipy.optimize.curve_fit for nonlinear least squares parameter estimation from experimental data.

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。

Infrastructure and development cost estimation for technical projects. Use when planning budgets, evaluating build vs buy decisions, or projecting TCO for architecture choices.

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。

Neo4j Graph Data Science (GDS) embedded plugin via Python client or Cypher — covers graphdatascience client 2.x, GraphDataScience, gds.graph.project.native, gds.graph.project.cypher, snake_case endpoints, graph catalog operations, stream/stats/mutate/write modes, memory estimation, PageRank, Louvain, WCC, FastRP, KNN, Node Similarity, ML pipelines, and cleanup. Use for Aura Pro, self-managed, local, or offline Neo4j DBMS with the GDS plugin installed. Does NOT cover Aura Graph Analytics GDS Sessions, AuraGraphDataScience, GdsSessions, gds.graph.project.remote, or AuraDB Cypher API projection/session management — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver skill.

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。

Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying bad operators (AllNodesScan, CartesianProduct, Eager, NodeByLabelScan), and prescribing fixes (indexes, hints, query rewrites, runtime selection). Use when a query is slow, when EXPLAIN or PROFILE output needs interpretation, when dbHits or pageCacheHitRatio are poor, when cardinality estimation diverges from actuals, or when deciding between slotted/pipelined/parallel runtimes. Covers USING INDEX / USING SCAN / USING JOIN hints, db.stats.retrieve, SHOW QUERIES, SHOW TRANSACTIONS, TERMINATE TRANSACTION. Does NOT write new Cypher from scratch — use neo4j-cypher-skill. Does NOT cover GDS algorithm tuning — use neo4j-gds-skill. Does NOT cover index/constraint creation syntax details — use neo4j-cypher-skill references/indexes.md.

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。

Standardized cost-estimation framework for great_cto plans. Forces explicit LLM cost, infra cost, human-supervision time, and the (defensible) human-equivalent comparison. Output format is parsable by the board's /api/cost path — must follow exactly.

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。

将本地视频转换为近亮远暗的连续灰度深度视频，使用 Video Depth Anything 时序模型，保留时长和可选原声。用于深度控制视频、depth video、视频深度估计。

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。

Optimizing FinOps and cloud cost: IaC-based estimation, right-sizing, RI/SP recommendations, anomaly detection, budget alerts, AI/GPU workload economics. Use to forecast or cut cloud spend.

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 Nexscope data to search and filter Amazon products, supporting multi-dimensional criteria including price, monthly sales, BSR ranking, gross margin, ratings, fulfillment method, badges, seller origin, and more across multiple Amazon marketplaces. Trigger when the user mentions Amazon product selection research, product filtering, sales filtering, product discovery, BSR analysis, niche product discovery, competitor analysis, market opportunity assessment, product-level market size estimation, gross margin screening, Nexscope product selection, Amazon product selection, sales filtering, BSR analysis, profit screening, market analysis, product selection tool. Even if the user does not explicitly mention "Nexscope", if their need involves filtering and analyzing Amazon product-level data for product selection, this skill should also be triggered.

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 Nexscope data to find and analyze competitors on Amazon, covering 12 marketplaces, with product metrics including sales, BSR, pricing, ratings, and growth trends. Trigger when the user mentions competitor lookup, competitor analysis, ASIN reverse lookup, competitive product research, finding similar products, market competitor discovery, product benchmarking, competitor sales estimation, analyzing competitor listings, competitor analysis, ASIN reverse lookup, competitor sales, competitor research, Nexscope, market competitor discovery, competitor trends. Even if the user does not explicitly mention "Nexscope" or "competitor lookup", if their need involves discovering and analyzing Amazon competitors by ASIN, keyword, seller name, brand, or category, this skill should also be triggered.

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。

Nexscope overview analysis of Amazon keyword market competition. Trigger when the user mentions keyword competition, supply-demand ratio, competitor count, keyword search volume estimation, market competitiveness assessment, keyword popularity ranking, ad competition analysis, number of products under a keyword, keyword competition, supply-demand ratio, competitor count, search popularity, market competition analysis, Nexscope, keyword overview. Even if the user does not explicitly say "Nexscope", if their need involves assessing keyword-level competition intensity, supply-demand balance, or search result product count on Amazon, this skill should also be triggered.

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。

Nexscope ASIN sales estimates query, returning daily estimated sales and latest known price for a specified ASIN over a given time period, covering 10 marketplaces including US, UK, Germany, and Japan. Triggered when users mention ASIN sales estimates, ASIN daily sales, sales estimation, competitor sales monitoring, average daily sales, sales trends, product sales tracking, Nexscope sales data, sales estimates, daily sales, estimated units sold, ASIN sales tracking, competitor sales monitoring, product sales trend, daily unit sales. Even if users do not explicitly mention "Nexscope", this skill should be triggered whenever the task involves viewing daily estimated sales data for an Amazon ASIN over a time period.

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。

Debug why a Hyperlane message is not being processed. Use when given a message ID or explorer URL to investigate delivery failures, gas estimation errors, validator issues, or other processing problems.

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 environmental liability exposure with remediation cost estimation, regulatory compliance requirements, and insurance coverage assessment. Use when analyzing environmental liabilities, estimating cleanup costs, or assessing environmental risk.

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。

Total-loss determination and settlement pricing. Triggers: "total loss valuation", "claims value", "settlement offer", "salvage estimate", "insurance claim pricing", "total loss threshold", "what's the claim worth", "settlement range", "pre-loss value", "diminished value", "total loss determination", insurance claim vehicle valuation, total-loss determination, settlement pricing, or salvage value estimation.

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 actuarial pricing analysis with loss cost estimation, expense loading, and rate adequacy testing. Use when analyzing premium rates, calculating rate indications, or assessing pricing adequacy.

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。

Bridge active inference theory with robot control using K-Scale's JAX/MuJoCo stack. Use when connecting predictive coding to locomotion policies, mapping KL divergence minimization to RL training, applying mean field approximation to robotics state estimation, or implementing sim2real as inference about future observations.

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。

Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.

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 an existing conjoint study for threats to inference and returns severity-ranked findings on design, estimation, measurement error, external validity, and interpretation. Use when checking whether a conjoint design or analysis holds up, or answering referee comments on one.

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。

Turns a raw Qualtrics conjoint export into an analysis-ready long-format dataset, covering export settings, identifying the implementation, package or manual wide-to-long reshaping, choice-profile alignment, ratings, reference categories, pilot exclusion, and subgroup merges. Use when the user has a conjoint CSV to clean, reshape, or debug, cannot align choices with profiles, or asks what the data must look like before AMCE estimation. Design goes to conjoint-design, validity review to conjoint-diagnostics.

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。

Turns a raw Qualtrics conjoint export into analysis-ready long-format data: implementation identification, reshaping, choice-profile alignment, reference categories, and pilot exclusion. Use when a conjoint CSV needs cleaning, reshaping, or debugging before AMCE estimation.

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 PPA analysis with tangible/intangible asset identification, useful life estimation, and goodwill calculation. Use when modeling purchase accounting, allocating deal price, or estimating amortization impact.

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。

Builds mining project financial models with resource estimation, mine plan integration, and commodity price sensitivity analysis. Use when modeling mining investments, analyzing feasibility studies, or evaluating mineral assets.

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。