'failures' 검색 결과

Diagnose interrupted or repeatedly restarting AI training jobs managed by JobSet on GKE. Correlate restart metrics, node events, host health, and worker logs to distinguish Spot VM preemptions, infrastructure failures, and application crashes, then recommend responses.

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• Investigating repeated JobSet restarts
• Checking whether Spot VM preemptions interrupted training
• Tracing recurring failures to a physical host

Inspect GKE TPU Dynamic Slices, diagnose provisioning failures, and validate workload configuration for single slices or JobSet workloads spanning multiple slices. Get cleanup commands, with confirmation required before destructive changes.

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• Checking TPU slice lifecycle states
• Diagnosing TPU slice provisioning failures
• Validating single-slice and multi-slice JobSet manifests

Design Google Cloud infrastructure with Terraform, validate it locally, and assess the planned changes for security, cost, and reliability. The skill then imports templates into Application Design Center, deploys them, investigates failures, and checks the deployed services.

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• Designing and locally validating Terraform for GCP
• Assessing infrastructure plans before deployment
• Importing and deploying Application Design Center templates

Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.

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• Build evaluation datasets from conversation logs
• Generate simulated user scenarios
• Choose scoring criteria for responses and tool use

Guide a Trae agent through recurring failures with diagnosis, alternative approaches, and relevant checks. The skill defines when to intervene, requires evidence for completion, and separates the agent’s self-review from external validation.

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• Change approaches after repeated failures
• Check evidence behind a completion claim
• Review risks and validation gaps before delivery

Guide Pi agents through repeated failures or unverified completion using competing hypotheses, small verifiable actions, and relevant checks. It separates self-review from external pass-or-fail decisions and requires evidence and remaining risks in delivery reports.

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• Revisiting hypotheses after repeated failures
• Delivering results with evidence and remaining risks
• Keeping self-review separate from external evaluation

Guide Trae through stalled tasks by comparing hypotheses, changing approaches after repeated failures, and checking results with relevant commands. It structures delivery around evidence, self-review, and remaining risks rather than unsupported claims of completion.

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• Reassess a task after repeated failures
• Check evidence behind a completion claim
• Report verified results and remaining risks

Plan and troubleshoot large language and vision model training and inference. This guide uses hardware measurements to identify bottlenecks, address memory shortages and unstable training, and improve checkpointing, recovery, and performance across multiple GPUs and machines.

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• Diagnosing slow training and GPU memory shortages
• Investigating loss spikes and invalid numerical values
• Debugging communication hangs across GPUs and machines

Investigate incidents and recurring defects with structured methods such as Five Whys and Fishbone cause maps. Identify multiple contributing conditions, build a blame-free account of what happened, and propose practical changes to prevent similar failures.

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• Review a production outage
• Investigate recurring defects
• Compare failing and working environments

Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.

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• Add repeatable quality tests for an AI agent
• Evaluate chatbot behavior across multiple conversation turns
• Generate evaluation datasets from documentation

Diagnose iii invocation failures using error codes across Node, Python, Rust, and browser workers. Distinguish permission denials, timeouts, and handler failures, then choose appropriate exception handling and decide whether retrying is suitable.

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• Diagnosing failed function calls by error code
• Deciding whether a timed-out operation should be retried
• Handling permission denials without blind retries

Analyze records of an AI agent’s decisions and tool use to find repeated failures, lost goals, and misunderstood instructions. Use the findings to refine prompts, rerun tasks, and turn debugging lessons into reusable skills.

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• Investigating repeated agent failures
• Reviewing incorrect tool choices
• Refining prompts through repeated evaluation

Install, upgrade, and remove the ingress-nginx-based KubeSphere Gateway extension, which manages external access to applications. Inspect gateways across cluster, workspace, and project scopes, and investigate stalled operations, Helm installation failures, or pod problems.

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• Installing or upgrading the Gateway extension
• Checking gateways by cluster, workspace, or project
• Investigating stalled gateway operations and pod failures

Review recent changes, automated test failures, issues, and conversations to prioritize engineering work. Produce a concise report separating urgent actions, watch items, noise, and state updates, with suggested next steps and rough effort estimates.

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• Prioritizing recent CI and test failures
• Sorting team issues and conversation findings
• Separating urgent work from items to monitor

Reviews recent changes, CI failures, open issues, pull requests, and conversations to produce a prioritized findings report. It separates urgent work from monitoring and noise, suggests next actions, and records facts for the next automated review cycle.

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• Prioritizing recent CI and test failures
• Reviewing blocked pull requests and unanswered issues
• Preparing next actions for an automated work cycle

Run concurrent Omnigent conversations with real hosts and runners while replacing the LLM with a zero-latency mock. Read the generated report to explain response-time distributions, throughput, and failures, helping assess Omnigent's own processing overhead.

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• Measuring performance with concurrent hosts
• Studying latency as conversation history grows
• Comparing throughput and failures under load

Deploy and operate Vercel projects from the command line. Manage environment variables and domains, investigate build failures with logs and metrics, and handle platform settings while checking which project and team each operation targets.

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• Deploy preview and production versions
• Manage environment variables, domains, and DNS
• Investigate build failures and performance issues

Capture production errors from Convex server functions and browser interfaces in your own deployment. Redact sensitive information before storage, inspect recent failures, monitor new events, and optionally route recurring errors into a workflow that opens fix pull requests.

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• Collect server and browser errors in one deployment
• Inspect production failures with sensitive details redacted
• Monitor newly recorded errors

Decide when repeated search failures justify continuing document creation with existing knowledge. Track errors, distinguish retryable problems, and record verification limits, while excluding tasks that require current information or explicit external verification.

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• Deciding what to do after repeated search errors
• Continuing technical document drafts with established knowledge
• Recording information that could not be externally verified

Analyze agent runs that fail before any work begins. Check iteration counts, tool use, logs, and outputs to identify startup failures, then direct investigation toward environment setup, input formatting, permissions, or resource limits.

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• Investigating failed runs with zero iterations
• Checking startup errors across multiple agents
• Recording failures that require system review

Diagnose Python script failures by showing error output and exit codes alongside the working directory and file listing. Use these clues to investigate missing files, missing modules, permission errors, or scripts that produce no output.

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• Investigating Python failures during automated execution
• Checking file paths after a FileNotFoundError
• Diagnosing Python failures in CI/CD pipelines

Diagnose Python script failures by capturing full error traces, which show where execution failed, alongside exit codes and working directory details. The skill also covers file existence checks and clearer paths for troubleshooting.

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• Investigate Python failures with unclear error messages
• Find why a script cannot locate its input file
• Add diagnostic output to file processing scripts

Investigate Python script failures by capturing regular output, error details, and exit codes. Log the working directory and resolved file paths to expose missing files, permission problems, missing modules, or differences in automated execution.

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• Investigating Python failures with unclear error messages
• Checking paths when file reads or writes fail
• Debugging scripts that fail only in automation

Handle repeated tool failures by documenting what went wrong and which deliverables remain blocked. Produce useful partial work with clear limits, distinguish unverified content from completed work, and provide concrete steps for recovery or handoff.

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• Reporting repeated PDF extraction failures
• Providing partial research with clear reliability limits
• Handing off blocked tasks with errors and next steps