数据分析、机器学习、LLM 应用、提示词设计（8854 个）
Plan, train, evaluate, and deploy neural networks using Flow Nexus cloud infrastructure. Coordinate training across multiple computing environments, tune training settings, and manage model versions, predictions, and ongoing performance monitoring.
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Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.
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Coordinate decisions across multiple AI agents by synchronizing shared memory, combining knowledge, and weighing input by expertise. The skill also guides workload distribution, conflict handling, and recording decisions with their rationale.
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
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Store useful patterns and solutions in AgentDB, then find them through semantic search, which matches meaning rather than exact wording. Use saved knowledge before starting work and record successful approaches and new lessons afterward.
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Import Claude Code auto-memory into AgentDB and search past notes by meaning across sessions and projects. The skill checks import status, consolidates near-duplicate entries, and returns search results with their memory sources identified.
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Manage stored agent memories for backups, project transfers, and team knowledge sharing. Work with RVF, a portable memory format, to export and import records, upgrade formats, inspect storage statistics, and transfer patterns between local projects.
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Find stored Ruflo memories by meaning, keywords, or an optional ranking method. Search across storage groups, retrieve past solutions and task context, and see which search method actually ran, including any fallback.
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Convert chat messages into template strings for local model runtimes such as Ollama or llama.cpp. Optionally route queries among a few registered intents using embeddings, numerical representations of meaning, without calling a model.
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Run demanding neural-trader backtests, parameter comparisons, and model training in an Anthropic Managed Agent cloud container. Estimate costs, test inputs on a small run, collect performance metrics and CSV files, and verify signed results before storage.
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Guide market prediction workflows with LSTM, Transformer, and N-BEATS models using neural-trader commands and result storage. It explicitly identifies version 2.8.11 outputs as placeholders, so they cannot be treated as fitted models or evidence of prediction accuracy.
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Compare ruflo and HAL systems for running the GAIA benchmark, an AI evaluation. Examine differences in code execution, browsing, file handling, memory, and model selection to prioritize improvements by expected impact and effort.
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Test trading strategies against historical market data and collect performance metrics. The skill checks whether data is real or synthetic and can digitally sign results to help detect tampering before a strategy moves into live trading.
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Convert text, code, or documents into 384-dimensional vectors, numerical representations used for meaning-based search, similarity comparison, and clustering. Save vectors to files, check their dimensions and magnitude, and optionally record metadata in AgentDB.
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Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.
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Guide GAIA benchmark runs from environment checks and cost estimates to result comparison. Create a signed submission package compatible with HAL, including agent traces, metadata, and an audit report that checks for benchmark exploitation.
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Calculate portfolio weights from a covariance matrix, which describes how asset returns move together, and expected returns using Conjugate Gradient. Save the weights with the solver used, numerical error, and timing so the calculation can be checked later.
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Classify market conditions as bullish, bearish, ranging, or volatile to guide strategy selection and historical testing. Distinguish synthetic demonstrations from analysis of actual price data, with confidence levels and historical comparisons where available.
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Represent dependency trees, taxonomies, and other hierarchies inside a Poincare ball, a geometric model for hierarchical distances. Use tree depth for radius and text embeddings for direction, then measure distances to examine the encoded structure.
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Explain a trading model's prediction by ranking the input features that contributed to its signal. Returns a Markdown table and saves an attribution record with calculation settings, adding a digital signature when a signing key is available.
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Capture lessons from completed tasks, train patterns from explicit examples, and consolidate accumulated learning into long-term storage. Use SONA and MicroLoRA adaptation tools for individual domains, then verify results in the appropriate stores.
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Capture token usage from Claude Code session logs, calculate USD costs using the supplied pricing reference, and save structured records to AgentDB. Review totals by model and tier, and provide data for cost reports.
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Send proofs, planning questions, or difficult debugging tasks to DeepSeek's reasoning model. Receive the final answer and model-provided reasoning text in separate fields, with token usage reported for reviewing responses and costs.
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Evaluate and improve the policy instructions that guide an AI agent using scored coding tasks. Run a learning cycle to compare candidates, or use the default dry run to inspect tasks and estimated costs without model calls.
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