「rice」の検索結果

Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features

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Free DeFi analytics across all chains — TVL, token prices, DEX volumes, fees/revenue, stablecoins, and bridges

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。

Solana token market data via Birdeye — prices, OHLCV, trades, token metadata, security checks, and trader activity

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。

Traditional market microstructure concepts applied to crypto — order book dynamics, market making theory, price formation models, execution quality measurement, and CEX vs DEX structural differences

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。

AMM liquidity provision mathematics including constant-product, concentrated liquidity, price impact, and LP share calculations

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Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use for feature prioritization, user research synthesis, requirement documentation, and product strategy development.

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 cryptocurrency market research and analysis using specialized AI agents. Analyzes market data, price trends, news sentiment, technical indicators, macro correlations, and investment opportunities. Use when researching cryptocurrencies, analyzing crypto markets, evaluating digital assets, or investigating blockchain projects like Bitcoin, Ethereum, Solana, etc.

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Complete Product Owner / Product Manager capability — a wiki-style knowledge map of product practice in active software development: the role and its boundaries, strategy cascade (vision → OKRs → roadmap → backlog), continuous discovery, backlog craft (epics, INVEST stories, acceptance criteria), prioritization frameworks (RICE, MoSCoW, Kano, WSJF), sprint delivery, metrics (North Star, AARRR), stakeholder management, and anti-patterns. The PO/PM owns what gets built, in what order, and why — including the end-to-end user flows and user journeys. Use when scoping a product or feature, decomposing an idea into outcomes/epics/stories, prioritizing a backlog, defining an MVP, mapping user journeys, measuring success, or saying no to a stakeholder. Pairs with super-ui-ux-design, which owns how each screen looks and behaves.

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Shop on the 京东 (JD) app. Use whenever the user wants to buy, order, or price something on 京东 / JD, including 京东七鲜 fresh groceries, or names the 京东 app.

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。

Shop on the 淘宝 (Taobao) app. Use whenever the user wants to buy, order, or price something on 淘宝 / Taobao / 淘宝闪购, or names the Taobao app.

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。

Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-tools.

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Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).

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Compute the bacterial pan-genome from Prokka/Bakta GFF3 annotations with Roary's CD-HIT + BLAST + MCL clustering pipeline. Builds gene presence/absence matrices, core/soft-core/shell/cloud partitions, multi-FASTA core gene alignments (with `-e`), and a pan-genome reference. Use Panaroo for higher-accuracy pan-genomes from highly fragmented assemblies, PIRATE for paralog-aware clustering, or PPanGGOLiN for graph-based partitioning.

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Symbolic math in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, ODEs, code gen (lambdify, C/Fortran). Use for exact symbolic results. For numerical use numpy/scipy; for stats use statsmodels.

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MATLAB/GNU Octave numerical computing: matrices, linear algebra, ODEs, signal processing, optimization, statistics, scientific visualization. MATLAB-syntax examples run on both. For Python use numpy/scipy; for statistical modeling use statsmodels.

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Extract sourced FF&E specifications from supplied product PDFs into reviewable structured data. Use for fact sheets, spec sheets and price books; not web-page capture, EPD extraction or automatic schedule adoption.

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Design, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.

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Downside stop-loss management for PMCC, naked LEAPS, and stock positions in IB. Computes stop prices, detects alerts, and places conditional combo orders. Dry-run by default. Requires TWS or IB Gateway running locally.

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Get option chain data including calls and puts with strikes, bids, asks, volume, open interest, and implied volatility. Use when user asks about options, option prices, calls, puts, or option chain for a specific expiration date.

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。

Server-side trailing stop management for stocks and naked LEAPS in IB. Places native TRAIL orders that auto-ratchet the stop as price climbs. Dry-run by default. Requires TWS or IB Gateway running locally.

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。

When a founder needs demo scripts, discovery call frameworks, objection handling, RFP/RFI responses, competitive feature matrices, POC planning, or closing playbooks. Activate for sales calls, demo prep, talk tracks, bid responses, competitor comparisons, or pre-sales engineering.

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。

Calculate construction costs using resource-based method. Estimate project costs from work items, physical resource norms, and current 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。

Apply geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions.

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Access and utilize open construction pricing databases. Match BIM elements to standardized work items, calculate costs using public unit price databases with 55,000+ work items.

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。