'prediction' 검색 결과

NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.

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Evaluate prediction markets as information sources for products, agents, dashboards, and business decisions. Record probabilities implied by market prices with sources and timestamps, assess signal quality, and compare other evidence to recommend suitable uses and explain limitations.

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• Evaluating prediction-market data for a product
• Comparing market probabilities with internal dashboard metrics
• Assessing probability signals for agent memory or alerts

Browse Itô baskets, collections of prediction-market items, and organize market data with sources and timestamps. Compare baskets with selected research or watchlists, prepare market briefs, or draft planning worksheets for manual review without investment advice or trading.

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• Browsing and indexing the Itô basket catalog
• Comparing a basket with research or a watchlist
• Preparing a sourced prediction-market brief

Explore World Cup match predictions, related news, and market probabilities. Adjust model factors to compare possible outcomes, then optionally hand off to Binance Agentic Wallet for prediction-market trading, where orders require an explicit choice and confirmation.

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• Review AI predictions for a World Cup match
• Compare model probabilities with market probabilities
• Recalculate probabilities with adjusted factors

Manage a Binance Web3 wallet through the baw command-line tool. Check balances, transfer or swap tokens, manage limit orders and token permissions, and access prediction markets and DeFi, blockchain-based financial services.

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• Check wallet balances and transaction history
• Send tokens, swap assets, and manage limit orders
• Review and revoke token spending permissions

Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For Chai prediction, use chai1-structure-prediction.

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Use this skill when an admin needs to create an Einstein Discovery story in CRM Analytics Studio, configure prediction definitions, deploy writeback fields, set up what-if analysis, or manage model refresh and activation. Trigger keywords: Einstein Discovery story, prediction definition, writeback field, CRM Analytics Studio, model refresh, what-if analysis, bulk scoring, prediction field, 1OR prefix. NOT for the Einstein Discovery Flow action or model-health monitoring in Model Manager — use admin/einstein-discovery-deployment. NOT for Einstein Prediction Builder, a separate product needing no CRM Analytics license — use agentforce/einstein-prediction-builder. NOT for programmatic scoring through the Connect REST API — use agentforce/einstein-discovery-development.

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Build and test Polymarket prediction market trading strategies for YES/NO token trading. Provides 6 tools: get_all_prediction_events (browse markets, $0.001), get_prediction_market_data (analyze price history, $0.001), create_prediction_market_strategy (generate code, $1-$4.50), run_prediction_market_backtest (test performance, $0.001). Trade on real-world events (politics, economics, sports, crypto). Currently simulation only (live deployment coming soon).

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Chai-1 structure prediction for protein complexes and design validation. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For ESM-based analysis, use esm2-sequence-scoring.

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Review prediction-market and trading-agent workflows before they handle account access, portfolio data, or trade planning. Produces structured findings on venue rules, data quality, security, privacy, and execution risks, with required safeguards and a safe next step.

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• Review workflows before adding venue authentication
• Check how portfolio data and API keys are handled
• Review market data quality and source labels

Search Polymarket prediction markets, where prices express market estimates of event probabilities. Retrieve current prices, trading volume, buy and sell orders, and price history to examine expectations and market movements.

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• Check market estimates of an event's probability
• Find prediction markets about a topic
• Review current prices and buy and sell orders

Look up active Polymarket prediction markets for elections, economic outcomes, and geopolitical events. Filter by topic or keyword to see market-implied probabilities, meaning probabilities reflected in prices, alongside trading volume and closing dates.

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• Checking market odds for an election outcome
• Finding prediction markets about a ceasefire
• Reviewing trading volume and closing dates

Explain how input features contribute to machine-learning predictions using SHAP. Select an explanation method and reference data, validate contributions against model outputs, and create charts for individual predictions or patterns across samples, including models with multiple outputs.

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• Explaining the feature contributions behind one prediction
• Visualizing attribution patterns across evaluation samples
• Selecting and explaining a class in a multiclass model

Investigate how DNA variants may affect gene activity using precomputed AlphaGenome Atlas results and model predictions. Rank candidate variants, examine tissue-specific effects, and inspect the features behind scores to develop research hypotheses about biological mechanisms.

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• Ranking candidate variants from a VCF
• Examining tissue-specific effects on gene activity
• Comparing reference and altered DNA predictions

Connect an AI agent to over 100 cryptocurrency exchanges and prediction markets through a local MCP server. Fetch prices, order books and account balances, or place and cancel orders when trading is explicitly enabled.

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• Compare prices and order books across exchanges
• Check account balances, orders and positions
• Try placing and canceling orders on a testnet

Monitor requests to an AI prediction API for signs of model copying or training-data exposure. Test an authorized model with simulated attacks, measure the risk, and evaluate defenses such as request limits and reduced prediction detail.

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• Monitoring a public or partner prediction API
• Measuring model copying risk before deployment
• Testing request limits and reduced prediction detail

Integrate the Gemini API on Agent Platform using the Google Gen AI SDK. Guidance covers authentication, text and multimedia tasks, structured JSON responses, live voice interactions, context caching, and batch prediction across five programming languages.

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• Adding document and multimedia understanding to an application
• Generating JSON that matches a defined schema
• Building live voice and video interactions

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

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Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

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Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.

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Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.

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Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

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ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.

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AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.

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