'search evaluation' 검색 결과

Helps design semantic search and RAG, where an AI answers using retrieved documents. Covers embeddings, numerical representations of content, plus document splitting, search indexes, keyword search combinations, and evaluation of retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design document retrieval for AI answers
• Compare embedding models and document splitting strategies
• Combine semantic search with keyword search

Plan and carry out research by refining questions, improving searches, evaluating sources, and checking claims across independent references. Turn findings into cited reports that explain the evidence, conflicting views, confidence levels, and remaining gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing options using multiple sources
• Checking source reliability and conflicting claims
• Writing research reports with citations

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Use when a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure systems.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

조사 주제를 여러 질문으로 나누고 웹 검색과 본문 검토를 진행합니다. 출처, 확신 수준, 정보 부족 부분을 밝힌 보고서로 정리합니다.

• 경쟁사와 시장 동향 조사
• 도입 전 기술 비교
• 시장 규모와 성장 전망 조사

Prepare and review research funding proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Maps application requirements to draft sections, identifies gaps, and helps refine research aims, budgets, impact statements, and responses to reviewers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Map funding-call requirements to proposal sections
• Review research aims against evaluation criteria
• Align budgets and timelines with the research plan

Develop and prioritize early research ideas through independent brainstorming, structured discussion, evidence checks, and critical review. Keep assumptions, uncertainty, dissent, and scoring reasons visible, then record the human decision and appropriate next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring candidate directions for a new research topic
• Organizing ideas before a research team discussion
• Comparing proposals using explicit evaluation criteria

Prepare and check research documents for clinical decision-support evaluation, including evidence profiles, cohort tables, survival plans, and model assessments. Work with aggregate or synthetic inputs, record sources and review status, and keep outputs outside patient care.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing research model evaluation documents
• Checking evidence profiles and source links
• Reviewing survival analysis plans

Turn a research proposal into an experiment plan tied to specific paper claims. Define comparisons, evaluation measures, tests that remove individual components, run order, and compute estimates, with a written plan and tracker for execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a research proposal into concrete experiments
• Designing comparisons and component-removal tests
• Prioritizing runs within a compute budget

Use when a user asks an agent to evaluate or tune text search similarity for an existing Viking AI Search application and dataset.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Viking Search tuning for specified policy directions. Use this when the user provides specific queries, a type of query, or a business policy direction, and asks to boost, suppress, or fix a class of search results through request-parameter passthrough. You must only perform read-only baseline evaluation and request-level candidate testing; do not modify search scenes, app config, dictionaries, recommend scenes, or primary recall parameters.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants web research: gathering cited, multi-angle evidence on a specific question. Triggers on: "research X for me", "do web research on", "look up sources for", "find citations for", "gather evidence on", "what does the web say about X". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), local file search, LLM-as-judge evaluation, or paywalled/auth-gated scraping. Produces a written plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build TypeScript code for Azure AI Foundry projects using its SDK, a library for accessing project services. Create and run agents, inspect connections and deployed models, and manage datasets and search indexes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating and running agents in TypeScript
• Adding file search or external tools to agents
• Inspecting project connections and deployed models

Run repeated change-and-evaluation cycles for one predefined improvement mission within an explicit runtime limit. Save each evaluation as JSON and each decision in readable Markdown logs, with support for periodic reruns through Claude Code native cron.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Repeating improvements under a fixed evaluation method
• Keeping evaluation results and decision logs
• Running experiments within a time limit

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Turn a broad robotics research direction into ranked ideas with concrete benchmarks and simulation plans. The workflow combines literature review, novelty checks, and external critique while accounting for hardware access, safety, evaluation metrics, and failure cases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore research topics in manipulation or navigation
• Plan simulation studies before using physical robots
• Check novelty and feasibility of robotics ideas

Develop robotics research ideas through literature review, feasibility filtering, novelty checks, and external critique. Get ranked proposals with specific benchmarks, comparison methods, failure metrics, and simulation-first pilot plans matched to available infrastructure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring research topics in manipulation or navigation
• Choosing robotics ideas that fit available infrastructure
• Planning pilot studies with simulation or existing logs

Evaluate NVIDIA Cosmos Policy for robot manipulation in LIBERO and RoboCasa simulations. Follow GPU environment setup, configure rendering without a display, run small checks before larger benchmarks, inspect success rates, and profile inference latency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a small LIBERO evaluation
• Evaluating kitchen tasks in RoboCasa
• Setting up simulation on a GPU cluster without a display

Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare code generation models on shared benchmarks
• Measure code generation performance across languages
• Evaluate instruction-tuned coding models

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Design and implement RAG systems, which retrieve documents to inform AI answers. Get document preparation strategies, vector database comparisons, combined keyword and meaning-based search, result reranking, and an evaluation plan for retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design an AI application that answers using internal documents
• Compare vector databases and document splitting strategies
• Improve search through keyword matching and result reranking

Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with academic-paper-composer skill).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.