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Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.
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Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.
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Manages Agent Platform endpoints, the stable URLs used to serve online model predictions. It supports listing, inspecting, creating, updating, and deleting endpoints, plus troubleshooting permission errors, quota limits, and resources that are busy.
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Build a PDF from a LaTeX paper, diagnose compilation errors, and fix common problems with citations, references, and figures. Check page limits, anonymity, and embedded fonts, then report remaining warnings before submission.
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Investigate software errors, version conflicts, and API questions across official documentation, GitHub, technical Q&A, and Chinese developer communities. Results distinguish matching errors, source authority, and reported environments, giving you documented leads and next checks for debugging.
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Build a PDF from a LaTeX paper, diagnose compilation errors, and repair reference or citation problems. Check the resulting layout, page count, anonymity, and embedded fonts, then report fixes and remaining warnings.
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Investigate service errors in Sentry by project, environment, and time period. Rank problems by event counts and affected users, compare releases or earlier periods, and report findings with links and a clear search scope.
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Investigate and fix production errors reported in Sentry through MCP, a connection to external tools. Analyze error details and execution history, check them against source code, implement fixes, and verify them with tests and regression checks.
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Investigate Convex production errors, prepare fixes on a branch, and verify them before opening a pull request for human review. After an approved deployment, check whether the same error recurs. Never automatically merge changes or deploy to production.
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Write and review interface text, including button labels, errors, empty states, and confirmations. Match the product's established vocabulary and tone, explain actions clearly, and give people a useful next step when something goes wrong.
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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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Run multi-line Python scripts through run_shell when execute_code_sandbox repeatedly fails. The guide explains heredocs, a way to pass blocks of text to a command, with examples for PDF creation, data processing, and inspecting errors.
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Troubleshoot CSV and XLSX processing when execute_code_sandbox fails. The workflow locates input files, saves processing code as a Python script, captures execution errors, checks dependencies and file access, and confirms output exists before reading it.
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Investigate unclear errors in Python PDF generation by displaying output immediately and inspecting error details. Use the resulting traceback, a record of where execution failed, to identify missing dependencies, font problems, or invalid file paths.
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Resolve Unicode character errors when converting Markdown to PDF with pandoc. Follow a workflow to replace troublesome symbols with ASCII equivalents, regenerate and check the PDF, or consider another PDF engine when preserving characters matters.
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Audits arithmetic code for mixed units, decimal precision mistakes, and scaling errors. Adds comments identifying each value’s units and produces a summary of findings, unit definitions, and files reviewed.
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Test and debug rendered web apps by reproducing interaction bugs, checking layouts and console errors, making focused fixes, and validating again. Results include screenshots, observed state changes, and a QA report explaining coverage and remaining gaps.
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Investigate errors, crashes, and failures after updates using reproducible steps, logs, and runtime checks. Identify the root cause before making authorized fixes, then verify the result and check for the same problem elsewhere.
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Inspect production errors recorded in Sentry through read-only API access. Summarize unresolved issues, recent crashes, occurrence counts, and event details to understand what happened and when each problem was last seen.
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Inspect and control live Chrome pages to investigate display problems, console errors, failed requests, and slow loading. Capture screenshots, automate page interactions, and record performance traces to identify where delays or layout shifts occur.
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Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.
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Build custom calculation and parsing tools that an n8n AI Agent can call. This skill covers JavaScript or Python inputs, structured arguments, string results, tool descriptions, and fixes for invocation and output errors.
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Write and troubleshoot n8n expressions, the formulas that pass and transform data between workflow steps. Correct field references, read webhook inputs, format dates and text, and query nested JSON while checking for silently missing results.
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Write and troubleshoot native Python in n8n Code nodes. Covers data filtering and transformation, migration from older Python code, valid return formats, and restricted execution environments, including cases where errors pass unprocessed input downstream.
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