'search evaluation' 검색 결과

Combine specialized language models without retraining using mergekit. The skill explains how to choose merging methods, configure each model’s contribution, compare resulting variants, and prepare a merged model for evaluation and deployment.

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• Combine math, coding, and conversation capabilities
• Compare model variants with different merge weights
• Choose a method for merging several models

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

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• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Review and improve SKILL.md files using nine evaluation criteria and test prompts. Independent agents compare original and revised versions, while user checkpoints, version history, and result cards help track which changes are retained or rolled back.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess skill quality without editing files
• Improve instructions in a specific SKILL.md
• Prioritize weaker skills for revision

Register and search existing Git repositories and worktrees, then choose which projects Ouroboros interviews use as context. You can also generate a configuration containing verified build, test, and other check commands for the evaluation workflow.

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• Selecting existing projects as interview context
• Finding registered repositories by topic
• Rescanning after repositories are added or moved

Organize research reports around what readers need to understand or decide. Choose structures for overviews, comparisons, background investigations, or event tracking, with specialized formats for academic, medical, legal, and policy reports that make evidence and uncertainty explicit.

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• Structure an industry overview or feasibility report
• Compare products or technologies in an evaluation matrix
• Organize a company or individual background investigation

Predict how a small molecule may sit within a protein using structural inputs. Generate ranked 3D poses without defining a search region, then use those poses as starting points for further evaluation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting ligand poses from a protein structure and SMILES
• Generating ranked poses for a small fragment library
• Preparing starting poses for downstream rescoring

Draft an English referee report for an economics or finance paper, with a findings summary, specific numbered critiques, and a letter to the editor. Choose the recommendation, review depth, and three to eight comments.

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• Drafting a referee report for an economics or finance paper
• Examining causal claims and robustness of research findings
• Choosing review depth and the number of comments

Generate a Chinese master's thesis review from the full thesis and a user-provided score. Produce an academic evaluation plus specific shortcomings and revision suggestions, with tone and criticism adjusted to the supplied score.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting a Chinese master's thesis review
• Matching review wording to an assigned score
• Turning thesis-specific issues into revision suggestions

Evaluate policy effects with difference-in-differences, a method comparing changes in treated and untreated groups. Guides assumption checks, regression models, staggered adoption analysis, robustness checks, and reporting with event study plots.

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• Evaluating policy effects with comparison groups
• Checking outcome trends before treatment
• Analyzing policies adopted at different times

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a reproducible R or Quarto analysis workflow
• Compare regression models with progressively added controls
• Test robustness across samples and statistical methods

Plan and troubleshoot Nemotron retrieval recipes for embeddings, which turn text into searchable vectors, and reranking, which reorders search results. Review configurations, prepare training and evaluation commands, and check export or deployment requirements.

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• Choosing training steps for missing relevant documents
• Improving the ordering of retrieved passages
• Reviewing training commands with a dry run

Build MCP servers, which expose external services as tools for AI agents. Follow a structured process covering API research, tool design, Python or TypeScript implementation, code review, and realistic evaluations of how agents use the tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting external APIs to AI agents
• Building an MCP server in Python or TypeScript
• Improving tool inputs, responses, and error messages

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

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Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

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Best practices for systematic research, source evaluation, and evidence gathering

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Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output quality.

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Audit and rewrite evaluation/numeric claims to ensure they carry minimal protocol context (task + metric + constraint) and avoid underspecified model naming.

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Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).

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Guides Researchers through Faiss installation, dense and binary similarity search, index training and composition, persistence and evaluation, and explicitly gated CPU/GPU interoperability workflows.

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Operate Tongyi DeepResearch and its WebAgent family: ReAct inference setup, rollout validation, benchmark evaluation, and variant routing.

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Route DeepSearcher workflows for provider setup, data ingestion, RAG querying, CLI and service operation, and evaluation.

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Use this skill when an agent needs Google Cloud Vision API for still-image understanding: labels, object localization, OCR, document text, SafeSearch, image properties, crop hints, web detection, batch annotation, Cloud Storage based pipelines, confidence evaluation, privacy, quotas, cost, and QA. Do not use it for Gemini multimodal reasoning, video analysis, image generation, custom model training, product catalog search design, or human reference/authenticity review.

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