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Unified workspace maintenance skill. Covers: (1) backing up the workspace to GitHub git repo, and (2) updating OpenClaw and skills. Use when: asked to backup, push to git, update openclaw, update skills, or run maintenance.
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Use this skill when the user asks how to design or improve a Preventive Maintenance (PM) program for a fleet — mileage vs time intervals, A/B/C/D inspection levels, ATA standards, common PM tasks, scheduling software, cost tracking, and how PM affects roadside inspections + CSA scores. Reference ATA Equipment Maintenance Council standards.
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从本地保存的GitHub数据中搜索Issue、PR和潜在重复项，检查同步状态、信息时效与缺漏，并在作出处理决定前核对GitHub当前状态。
Review Archify issues, pull requests, and code by weighing user value, implementation and maintenance costs, and wider impact. Use evidence to decide whether work is worthwhile, compare alternatives, and identify questions that still affect the decision.
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Review JavaScript written directly in HTML and move suitable code into external files. Replace inline event handlers with addEventListener, clarify browser verification steps, and explain how separating code supports maintenance, caching, and security.
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Audit a codebase across nine areas, including architecture, tests, security, and documentation. Produces TECH_DEBT_AUDIT.md with source locations, severity ratings, estimated repair effort, and prioritized recommendations to help plan maintenance work.
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Guide AAS maintainers through pull request review, batch merging, generated-file synchronization, and scripted releases. Follow protected-branch procedures and check evidence tied to the exact revision before accepting changes.
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Review PostgreSQL queries and data designs using execution plans, which show how queries run, and targeted index recommendations. Get guidance on JSONB, arrays, full-text search, analytical queries, extensions, and routine database maintenance.
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Turn an existing GitHub Actions workflow into a structured specification for maintenance and AI reference. Document its purpose, triggers, job dependencies, inputs, outputs, permissions, and failure handling, focusing on expected behavior rather than implementation details.
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Improve PostgreSQL performance through a staged workflow covering slow query analysis, indexes that speed up data access, query rewrites, configuration tuning, routine maintenance, and monitoring for production databases.
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Audit a gbrain knowledge base for outdated pages, broken or missing links, inconsistent tags, and missing citations. Get issue counts and specific repair actions, with additional checks for search quality and maintenance status.
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Check your gbrain setup and receive prioritized suggestions for overdue updates, pending migrations, stalled jobs, and other maintenance issues. Findings include repair commands, while any fixes require your approval.
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Build a persistent connection that receives Zoom events, such as meeting starts and participant arrivals. This reference covers choosing WebSockets, a connection kept open for events, plus authentication, connection maintenance, and reconnection.
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Generate an operational runbook template from a service name. Provide sections for starting, stopping, health checks, incident handling, maintenance, and rollback, so teams can add service-specific commands and build consistent operating procedures.
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Scan a codebase for technical debt—issues that make future maintenance harder—and prioritize fixes using business impact and effort. Generate a ranked backlog, sprint allocations, and trend reports to assess cleanup work over time.
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Prepare a daily NemoClaw maintenance plan by ranking pending pull requests and issues, checking work carried over from earlier versions, and selecting release targets. Produce a queue with owners, next actions, and merge readiness.
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Monitor TPU workloads on GKE using system metrics and PromQL, a metrics query language. Check utilization, memory, node availability, and interruptions to investigate infrastructure issues and calculate average recovery times and intervals between interruptions.
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Install LifeOS into an existing AI agent environment, then use a guided interview to capture current circumstances and goals for the Pulse dashboard. The skill also handles installation checks, updates, and removal.
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Improve PostgreSQL queries and database design by examining execution plans and choosing indexes for actual access patterns. Get guidance on large tables, JSONB data, connection pooling, and maintenance while accounting for write overhead and memory use.
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Check whether Apache Hudi table maintenance is keeping pace with incoming data. Runs a read-only utility, interprets its JSON report, and explains unhealthy or skipped checks with concrete next steps.
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Run Nexent daily or manual full test suites on a self-hosted Ubuntu machine, using the appropriate test layer for each check. Keep results and evidence local, and prepare maintenance candidates for local test assets.
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Industrial AI literature research with mandatory intake questions, venue-aware source prioritization, structured report outputs, and survey draft generation. Use when the user needs up-to-date research on predictive maintenance, intelligent scheduling, industrial anomaly detection, smart manufacturing, cyber-physical systems, edge AI for automation, or crossover robotics-for-industry topics. Also trigger for adjacent terms: "digital twin", "industrial IoT", "Industry 4.0", "manufacturing AI", "factory automation", "process optimization", or "survey draft" in industrial contexts.
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Report wiki ingestion/completeness state, pending source delta, structural insights, or maintenance equilibrium. Use for remaining-work/status questions, hubs and bridges, or whether maintenance skills still have pending changes. wiki-digest summarizes learned knowledge instead.
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This skill should be considered when you need to answer reflow machine maintenance questions or provide detailed guidance based on thermocouple data, MES data or defect data and reflow technical handbooks. This skill covers how to obtain important concepts, calculations, definitions, thresholds, and others from the handbook and how to do cross validations between handbook and datasets.
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