'outputs' 검색 결과

Evaluate BMad skills using your agent's configured runner. Compare results with the model alone or another version, grade outputs against criteria, and test whether descriptions trigger correctly, preserving transcripts and evidence for each run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare a skill with the model alone
• Compare an edited skill with a previous version
• Grade outputs against defined criteria

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Helps organize biological datasets and models in LaminDB. Covers data registration, table validation, annotation with standardized biological terms, metadata searches, and tracking analysis inputs and outputs so researchers can preserve the context needed to repeat their work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Register and organize biological datasets
• Validate DataFrame or AnnData metadata
• Standardize annotations with biological vocabularies

Prepare bulk RNA-seq sequencing files or quantification outputs for gene-level comparisons. Check sample information and data quality, configure processing workflows, and assemble validated count tables and metadata for downstream differential expression analysis with PyDESeq2.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing gene count tables from FASTQ files
• Configuring an nf-core/rnaseq workflow
• Combining Salmon, STAR, or featureCounts outputs

Convert existing scripts, CLI tools, or Crawlee projects into reusable Apify Actors, programs packaged as Docker images. Define JSON inputs and outputs, integrate the SDK, test locally, and deploy to Apify.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning an existing script or CLI tool into an Actor
• Migrating a Crawlee project to Apify
• Defining JSON inputs and structured outputs

Export an approved fine-tuned model into a format suited to its hardware and runtime. Choose weight packaging and quantization, which reduces numerical precision, then compare outputs before and after export to check for regressions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exporting a checkpoint approved for deployment
• Choosing a model format for a target GPU or laptop
• Comparing outputs before and after export

Design and refine prompts for AI applications using examples, reusable templates, and structured outputs such as JSON. Test variations for accuracy, consistency, response time, and token usage to diagnose unreliable answers and formatting failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refine prompts that produce inconsistent answers
• Build reusable prompt templates with variables
• Design JSON outputs that follow a defined schema

Configure Turborepo builds for projects containing multiple apps and shared packages. Set task dependencies, reuse saved build outputs locally or remotely, limit runs to affected packages, and investigate why cached results are not being reused.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up build tasks in a new Turborepo project
• Sharing cached build outputs between local machines and CI
• Running builds only for affected packages

Check that an actual app, API, or command-line tool works by exercising its real entry point with realistic inputs. Collect observable evidence such as outputs, logs, exit codes, or screenshots, and report remaining uncertainty.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking app behavior through the actual interface
• Verifying API behavior with realistic inputs
• Collecting command-line outputs and exit codes

Create and analyze AI model evaluation experiments in Arize using the ax CLI, a command-line tool. Collect real model outputs, export results, compare evaluation scores, and identify examples where a new model performs worse.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same evaluation dataset
• Finding examples that perform worse after a model change
• Exporting experiment results for further analysis

Guides Python agent development with PydanticAI, covering tool calls and structured outputs checked against defined data types. Explains service injection, provider switching, streaming responses, and tests that run without calling a real language model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building agents that return validated structured data
• Connecting customer support agents to order data
• Testing agent logic without real model calls

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Set up LLM serving with structured responses, including JSON and outputs restricted to specified patterns. Covers reusing shared prompt calculations, tool calling, and multi-turn conversations through SGLang, with examples for server setup and application integration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract information into JSON with a defined schema
• Build agents that call tools
• Reuse shared prompts across chat requests

Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting errors from large logs
• Summarizing test failures and build warnings
• Inspecting JSON and CSV data

Turn a feature into an agent skill or improve an existing one. Define purpose-specific quality criteria, review outputs with models from three providers, then add tests and check that user requests reach the intended skill.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a reusable feature into an agent skill
• Audit an existing skill for missing parts
• Check quality after fixing or extending a skill

Organize the outputs of a substantial work session into linked knowledge pages with sources. A seven-phase review checks classification rules, identifies reusable workflows, audits overlapping skills, and verifies that saved pages and skills can be found.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving the findings from a completed research session
• Organizing outputs from a development session
• Finding workflows worth turning into reusable skills

Create a Claude Cowork plugin through guided conversation, from defining its purpose to choosing skills and external integrations. The workflow creates the required files, checks their structure, and packages an installable .plugin file for delivery.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a new plugin for a recurring work process
• Packaging specialist knowledge as reusable skills
• Designing a plugin with external service integrations

Compare a feature implemented in Qwen Code with Codex or Claude Code under the same scenario. Capture communication and terminal records, inspect tool definitions and outputs, and guide fixes toward matching observable behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a reproduced feature against its reference agent
• Comparing tool definitions and required arguments
• Investigating differences in terminal output

Build market research evidence by comparing two market-size estimates, calculating survey sample sizes for each customer group, and scoring potential segments. Outputs show methods and assumptions so teams can assess the basis for business decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimating market size for a business decision
• Planning survey responses for each customer segment
• Evaluating which market segments to pursue

Improve prompts through test cases and baseline comparisons, measure how well AI answers use retrieved sources, and check agent configurations. Includes Python tools for prompt analysis, retrieval quality reports, and token estimates, with costs calculated from supplied rates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare prompt revisions against a baseline
• Measure answer quality against retrieved sources
• Check agent tool settings and loop risks

Guide file storage and context retention in Kortix shared folders. Identify accessible folders, save lasting outputs, keep user and team notes across sessions, and handle conflicting file copies according to the documented sharing and synchronization rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving outputs for use after the session ends
• Reusing user notes and preferences across sessions
• Keeping shared context for the team

Verify Kortix changes with focused tests and real inputs and outputs. This skill guides test placement, API and CLI workflows, browser checks, database verification, and reporting of results, including skipped cases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify a bug fix with focused tests
• Check API and CLI behavior through real inputs and outputs
• Test database query conditions

Plan how an AI agent uses limited input space through reusable prompt prefixes, shorter tool outputs, summaries, and task splitting. Measure token use, response quality, and latency to choose optimizations that provide a net benefit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing token use from lengthy tool outputs
• Improving prompt cache reuse
• Planning context budgets for long conversations