「value」の検索結果

Update the status of a plan step. MUST call after completing each step to track progress. Status values - pending, in_progress, completed, failed, skipped.

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。

Read file content for text files. When you need to check file content, analyze code or data, or get configuration values.

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。

Extract page content and element text from current webpage. When you need to read page information, get element values, scrape data, or verify page content.

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。

Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'

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 cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data fo...

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。

Analyzes forest plots for meta-analysis, generating detailed descriptions and formatting figure legends in Chinese or English. Use when the user wants to interpret a forest plot image, describe its statistical significance (heterogeneity, p-value), and format the output with s...

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 generating Sankey or alluvial plots from sample annotation tables where rows are samples and selected columns are categorical stages such as risk group, response status, subtype, or cohort labels. NOT for: gene network flow analysis, continuous-value trajectories, or graph-structured pathway visualization.

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。

Reads a paper figure by figure before re-integrating the full narrative, so the user can identify the core findings quickly and check whether each visual actually supports the authors' main claims. Always separate figure content, figure-linked claim, evidentiary strength, and unsupported interpretation. Never fabricate references, PMIDs, DOIs, figure content, panel labels, result values, or study details that were not actually provided.

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。

Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.

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 you need a standardized R CLI workflow to train a two-class random forest model from an expression-like feature matrix, rank variable importance, and generate reproducible error and importance plots. NOT for regression tasks, multi-class classification, missing-value imputation, preprocessing, or remote data fetching.

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。

Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence. Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as weak early detection, poor risk stratification, treatment-response heterogeneity, monitoring gaps, diagnostic delay, undertreatment, overtreatment, or implementation failure. Always ground unmet-need claims in retrieved evidence and distinguish true care gaps from generic statements of importance.

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 you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.

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 filtering genes with high missingness and then imputing missing values in a bulk expression matrix with group-aware KNN through DMwR2, where donor samples are restricted by one annotation column before imputation. For strata with 10 or fewer samples, the script falls back to row-wise direct filling with mean or median. NOT for: single-cell data, multi-column stratification, non-tabular inputs, network access, or interactive workflows.

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 evaluating Prem Watsa / Fairfax-style value investing: insurance float, contrarian allocation, special situations, and conservative downside-focused capital 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。

Use when evaluating Bitcoin and crypto cycles through a Willy Woo-style on-chain lens: holder behavior, realized value, supply dynamics, and on-chain demand.

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 evaluating crypto investments through a Multicoin-style thesis lens: high-conviction sector bets, performance chains, token value capture, and crypto-native market structure.

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 evaluating Carl Icahn-style activist investments: undervalued assets, poor governance, capital allocation fixes, buybacks, asset sales, board pressure, and control catalysts.

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 evaluating Walter Schloss-style cigar-butt value: cheap stocks, balance sheets, diversification across many bargains, and avoiding glamorous stories.

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 evaluating bond, credit, duration, yield-curve, and fixed-income relative-value setups in a Jeffrey Gundlach style.

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 evaluating Nick Sleep-style long-term compounders: scale economies shared, customer obsession, low turnover, long runway, owner culture, and value creation through lower prices.

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 evaluating Benjamin Graham-style deep value: net-nets, working capital bargains, margin of safety, statistical cheapness, and balance-sheet protection.

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 evaluating Joel Greenblatt-style special situations: spinoffs, restructurings, merger securities, forced selling, magic formula quality/value screens, and event-driven mispricing.

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 writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation

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 the user wants to build a financial model — 3-statement model, DCF valuation, LBO, SaaS unit economics, sensitivity / scenario analysis, debt schedule, or fundraising projections — in Excel. Trigger on: 'financial model', '3-statement model', 'P&L + BS + CF', 'DCF', 'WACC', 'NPV', 'terminal value', 'LBO', 'debt schedule', 'cash sweep', 'MOIC', 'IRR / XIRR', 'sensitivity table', 'scenario analysis', 'ARR model', 'unit economics', 'CAC / LTV', 'cap table forecast'. Output is a single formula-driven .xlsx. This skill is a scene layer on top of officecli-xlsx — it inherits every xlsx v2 rule (4-color code, visual floor, number formats, cache-drift, Known Issues, Delivery Gate minimum cycle). DO NOT invoke for a simple budget tracker, CSV dump, or operational KPI sheet — route those to officecli-xlsx base.

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。