数据分析、机器学习、LLM 应用、提示词设计（9137 个）
Manage a production-critical Marin training run from launch checks through monitoring, recovery, handoff, and completion. Keep commands, code versions, checkpoints, saved training states, and final outputs tied to a durable run record.
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Define and launch Marin SkyRL reinforcement learning experiments on Iris. It guides configuration checks, model and data dependencies, small trial runs, and progress verification while keeping artifact versions and runtime revisions explicit.
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Register Hugging Face datasets for Marin by inspecting their structure without downloading the full dataset. Identify text fields, configurations, and data splits, then add a dataset module and validate the mapping with a sample.
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Intersect polygon layers, which describe geographic areas, and measure their overlap after checking coordinate systems and geometry. Produce GeoJSON results, an area summary, and records of inputs and settings for repeatable analysis.
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Guide the addition of a new model generation to oh-my-hermes. The workflow checks model recognition, official specifications, routing rules that select models for tasks, documented pricing, and live access, then validates changes with tests and comparative benchmarks.
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Create a local run efficiency report from supplied timings and context limits, the amount of information available to the agent. Include recorded Hermes token usage and costs when available, and clearly identify missing measurements.
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Prepare an implementation handoff for an LLM feature, covering model calls, validated output formats, versioned prompts, document retrieval, and evaluation datasets so future prompt or model changes can be compared against a recorded baseline.
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Review external agent prompt files before adapting them for Hermes. The skill inventories sources, checks file formats and argument placeholders, identifies command name conflicts, and records trust concerns so conversion candidates and unresolved issues are clear before implementation.
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Inspect Hermes model settings, confirm active providers, and prepare model assignments for different roles or task categories. Preview configuration changes for approval, guide provider login, and verify the resulting settings after applying approved changes.
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Submit text and independent yes-or-no, multiple-choice, or rating questions to Jev using your own key. Receive probabilities rather than prose, along with the served model, request status, and cost information, including cases where no answer is returned.
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Guide the adoption of a new model generation or changed service specifications. Check model recognition, research official documentation, draft behavior-specific instructions, plan routing and pricing updates, and define baseline comparisons to measure the adjustments.
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Plan whether to fine-tune a model on your own data, choose a suitable training method, and define evaluation criteria. Compare candidates with the original model before deciding whether to adopt a trained version.
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Plan how to preserve essential information during long agent tasks within limits on tokens, the units used to measure model input and output. Define budgets, summarization checkpoints, and recovery steps while keeping the full objective intact.
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Consult a configured external AI for a second opinion on a plan or answer. Prepare the question and context, summarize its critique, and explain which suggestions to accept or reject before deciding what to do next.
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Plan repairs and changes for batch or streaming data pipelines. Define writes that remain consistent when repeated, limit historical reprocessing, map downstream dependencies, and specify quality checks before data reaches its readers.
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Scope analysis of supplied CSV, JSON, tables, logs, or other data by defining sources, records, questions, and methods. Plan checks for anomalies and trends, and tie reported findings to observed data while requiring separate evidence for causal claims.
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Choose methods for interpolation, estimating values between known data points, based on spacing, smoothness, and noise. Provides SciPy implementation examples, polynomial calculations, and checks for boundary oscillations, prediction error, and the shape of resulting curves.
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Work through optimization problems using gradients, which indicate how a function changes. The skill guides method selection, step size choices, and convergence checks, with SciPy examples for conjugate gradient and BFGS methods.
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Use Shapely to create points, lines, and polygons from coordinates or text descriptions of shapes. Calculate areas and distances, test spatial relationships, combine or transform shapes, and check or repair invalid geometry, with results returned as JSON.
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Set up Agentica patterns that coordinate multiple AI agents. Choose different perspectives for research, a coordinator with specialists for implementation, repeated generation and critique for refinement, or a panel of agents for majority-based validation.
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Write clearer instructions for Agentica and REPL agents by specifying roles, file operations, and output formats. Includes templates for dividing research, coordinating specialists, reviewing solutions, and passing results between agents through files.
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Choose LLM settings for theorem proving, code generation, and creative exploration. It provides examples for output limits and response variety, plus proof planning prompts and a method for generating multiple proof attempts and selecting the best candidate.
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Review Claude Code sessions using Braintrust traces, records of agent activity. Summarize tool, agent, and skill usage, inspect repeated actions, replay sessions, and track token consumption to understand failures and recurring work patterns.
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Analyze CSV files with Python and get concise numerical summaries. The skill checks column structure before calculating requested aggregates, includes units when available, and explains assumptions when data is missing or incorrectly formatted.
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