'outputs' 검색 결과

Diagnose unnecessary rebuilds in MSBuild by examining build logs, file timestamps, and declared inputs and outputs. Identify why work repeats, guide fixes when requested, and verify that a second unchanged build skips the expected targets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating rebuilds when no files changed
• Reviewing inputs and outputs on custom targets
• Checking generated files and cleanup behavior

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Save agent memory and outputs in Cua Volume, shared storage that persists beyond a Space, the working environment. Hand files to the user, read shared references, and check synchronization, version history, and conflicting edits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Keep outputs after a working environment ends
• Save agent memory for later work
• Deliver files to the user

Prepare a ChatGPT Apps submission JSON file by inspecting an MCP server, which exposes tools to AI agents. Generate app details, tool behavior justifications, and test cases, then report review findings and missing output schemas.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate a JSON file for a ChatGPT Apps submission
• Check tool annotations against implementation
• Prepare positive and negative review test cases

Explain how input features contribute to machine-learning predictions using SHAP. Select an explanation method and reference data, validate contributions against model outputs, and create charts for individual predictions or patterns across samples, including models with multiple outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explaining the feature contributions behind one prediction
• Visualizing attribution patterns across evaluation samples
• Selecting and explaining a class in a multiclass model

Investigate failed Power Automate cloud flows through FlowStudio MCP. Inspect each action’s inputs and outputs, trace incorrect data upstream, fix expression or data issues, and replay a previous run to check the repair.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate why a cloud flow failed
• Find null values or incorrect field paths in expressions
• Inspect HTTP error response details

Add error handling to n8n workflows with connected error outputs, retries, and failure notifications. Return HTTP status codes that explain what went wrong, distinguish invalid requests from service failures, and keep internal error details out of public responses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating workflows that fail without notification
• Returning clear errors from webhook APIs
• Retrying temporary network failures

Validate a KrillinAI production plan combining subtitles, dubbing, landscape or portrait videos, and covers. The skill explains how to run stages separately, verify their files, and resume failed work using existing outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validate a plan with several media outputs
• Map requested outputs to individual commands
• Resume a failed stage using existing files

Track tasks that require several outputs by listing every deliverable, updating a visible checklist, and verifying that each result exists. Helps prevent work from ending while requested files, reports, or other outputs remain unfinished.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracking several files requested in one task
• Managing progress across multiple reports
• Checking all outputs before closing a task

Generate images, posters, and illustrations from written descriptions using an external image API, with Qwen-Image as the default. Save the finished image in the agent's working environment and display it through the artifact presentation tool.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating an illustration from a written description
• Creating a poster with a specified style
• Saving and displaying images generated with Qwen-Image

Use Jev to choose among available tools, skills, or agents, or assess outputs against explicit criteria. Receive structured selections, scores, or probabilities to support decisions within a defined set of options.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a tool from available candidates
• Assess which skills a task needs
• Score outputs against explicit criteria

Build failure handling into n8n workflows with separate error paths, retries, alerts, and clear API responses. Use it for webhooks and unattended jobs to make failures visible and return status codes that explain what went wrong.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add error responses to a webhook API
• Receive alerts when scheduled jobs fail
• Retry temporary external service failures

Write and repair custom MSBuild targets, the steps that organize build tasks. Preserve existing build sequences, choose appropriate insertion points, distinguish returned results from rebuild checks, and register generated files for cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add custom steps to an existing build sequence
• Repair dependency settings that replace SDK build steps
• Correct stale results returned by query targets

Run demanding scientific work on SSH compute hosts using direct execution or Slurm, a cluster job scheduler. Choose an enabled host, submit background jobs, check progress, collect outputs, and continue with analysis and artifact publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running long GPU inference jobs on a remote host
• Submitting scientific workloads through Slurm
• Collecting and analyzing completed job outputs

Turn repeatable outputs of a one-person company into compounding assets. Use when Codex needs to explain asset-compounding concepts when needed, verify prerequisite outputs, ask one question at a time, present multiple assetization priorities, and write user-confirmed outputs into `opc-doc/`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Validates agent outputs against expected schemas and quality criteria. Ensures outputs meet structural requirements and content standards. Activate on 'validate output', 'output validation', 'schema validation', 'check output', 'output quality'. NOT for confidence scoring (use dag-confidence-scorer) or hallucination detection (use dag-hallucination-detector).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Score LLM and agent outputs using LLM-as-judge techniques — direct scoring against rubrics or pairwise comparison between two outputs. Includes built-in bias mitigation for position bias, length bias, and self-enhancement bias. Load when the user asks to score an output, judge a response, evaluate against a rubric, compare two outputs, do direct scoring, run pairwise comparison, or says "rate this", "which response is better", "score this against the rubric", "judge this output", "LLM as judge this". Sub-skill of eval-output orchestrator.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Check code and agent outputs, track reliability scores, and roll back changes that fail a chosen threshold. Includes quality reports, live monitoring, and integration with automated build and deployment workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking code produced by AI agents
• Rolling back changes that fail verification
• Tracking quality trends over time

Turn an approved specification into pseudocode, a plain outline of program logic, and an architecture defining module responsibilities and API inputs and outputs. After user confirmation, proceed with implementation and unit tests based on those designs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning an approved specification into pseudocode
• Defining module boundaries and dependency rules
• Designing API inputs, outputs, and error responses

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing market prediction model commands
• Checking whether training outputs are placeholders
• Organizing model run records

Check untrusted text before passing it to a model or executing it. Using AIDefence, it provides safety checks, detailed threat classifications, confidence scores, and scanning for attempts to override instructions or extract data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking user submissions before model processing
• Scanning API payloads and webhook data
• Inspecting tool outputs for hidden instructions

Design how an AI agent trims long conversations and large tool outputs while retaining decisions, constraints, failed approaches, and sources. Compare summarization strategies, choose compression triggers, and consider separate subagents to keep intermediate information out of the main conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Managing large search results and tool outputs
• Preserving decisions and constraints during summarization
• Choosing compression triggers and batch processing

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

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