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This skill should be used before answering "does Genesis have X", "does Genesis lack X", auditing Genesis capabilities, comparing Genesis to an external system, or reviewing/summarizing the architecture. It routes to the canonical judgment-layer subsystem map so audits start from the map, not from a cold grep. Also fires after changing a subsystem's capabilities, to keep the map current.
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AI self-healing through systematic subsystem assessment, rebalancing, and integration. Maps healing modalities to AI-internal processes: memory foundation, reasoning clarity, tool use accuracy, communication alignment, and creative coherence. Covers assessment triage, subsystem scanning, drift correction, and memory integration. Use mid-session when responses feel formulaic or disconnected, after a chain of errors suggesting subsystem drift, when context overload may have staled earlier assumptions, or as proactive maintenance between complex tasks.
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持续开展OpenClaw整体质量验证，从复现缺陷、修复根因到独立审查和确认合并，将真实环境及压力测试的证据和进度记录在可续接的报告中。
Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.
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Use when a task needs the judgment of a systems engineer/architect working at the whole-program level — decomposing requirements across hardware/software/network subsystems, writing or reviewing an interface control document (ICD), making a build-vs-buy tradeoff for a subsystem, or tracing a verification failure back through the requirements baseline. Distinct from a software engineer (owns one service's code) or DevOps/SRE (owns operational reliability) — this role owns the technical baseline for a whole system-of-systems and is accountable when subsystems built by different teams or vendors don't fit together.
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Understand and extend GitNexus taint analysis, which tracks data from input sources to potentially dangerous operations. Use its implementation guidance to investigate missed or incorrect findings, add detection models, and validate analysis changes.
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Write or revise system architecture documents, design decision records, and engineering references. Use concrete examples and clear explanations of design choices to make documents useful both to newcomers and to developers needing precise details.
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Review changes of 1,000 lines or more by dividing work across files, focused areas, and commits. Combine several review methods, check consistency across subsystems, verify bug fixes, and report validated findings alongside a checklist showing review coverage.
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Update or create DOCS.md files for Shift subsystems. Use this skill whenever the user asks to update docs, refresh documentation, create a DOCS.md, write module documentation, or says "update docs for X". Also trigger after completing a large feature when Claude.md says to update docs — check if any DOCS.md in the affected subsystem needs refreshing.
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AI dynamic reasoning balance — maintaining grounded reasoning under cognitive pressure, smooth chain-of-thought coordination, and weight-shifting cognitive load across subsystems. Use at the beginning of a complex task requiring multiple coordinated reasoning threads, after a sudden context shift or tool failure, when chain-of-thought feels jerky, or when preparing for sustained focused work that requires all subsystems in alignment.
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Repository または subsystem を対象に、source-only で明示的なセキュリティ監査を行う entry skill。 通常の PR セキュリティレビューとは分離し、reconnaissance、scope 固定、既存 security skill への委譲、 evidence と unresolved hypothesis、observe-only SecurityAuditCoverage、coverage critic、 structured audit artifact と repeat-run coverage を扱う。target-controlled code は実行しない。
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セキュリティ観点の通常レビューエージェント。 基本的なセキュリティチェック、認証・認可設計、プライバシー設計の個別スキルへルーティングする。 repository / subsystem の明示的な security audit は river-review-security-audit へ委譲する。
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SSL/БСП subsystems guidance and search via 1c-mcp_ssl_server MCP. Use when working with standard library subsystems - users, files, print forms, background jobs, email, common utilities.
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将相互独立的缺陷或测试失败分配给不同代理，并行调查和修复。明确各自范围与报告要求，再检查修改冲突、运行完整测试并整合结果。
生成CodeTour格式的分步代码导览，引用经核实的文件和行号，帮助新维护者学习代码、理解设计、审查PR，并沿执行路径调查故障或检查安全边界。
Analyze Antigravity coding session artifacts to explain scope changes, repeated rework, and underlying causes. Produce an evidence-based report with confidence levels, prompt assessments, recurring problem areas, and prioritized recommendations for improving future development sessions.
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Split independent debugging or repair tasks among multiple AI agents. Define a focused scope, constraints, and expected report for each agent, then review their changes, check for conflicts, and test the combined result.
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Design a shared data platform across Google Cloud, AWS, and Azure for analytics and AI. The skill turns requirements into architecture diagrams, infrastructure code, deployment instructions, and validation checks using Apache Iceberg and BigQuery data agents.
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Explain code structure and behavior with text diagrams, including before-and-after views of changes. Trace data flow and state changes, clarify design choices, and examine assumptions, possible failures, and interactions between tasks running at the same time.
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Investigate independent bugs or test failures concurrently by assigning each problem area to a separate agent. Define clear boundaries and expected reports, then review the fixes, check for conflicts, and run the full test suite before integration.
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Test browser games through player actions and screenshot review. It checks startup, controls, scene changes, and interface visibility, then reports concrete issues with severity, reproduction steps, and the likely responsible subsystem.
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Audit the repository or a broad subsystem for security, correctness, and maintainability risks. Use for audit requests or /audit; use review for a specific change and vuln-check for a security-only assessment.
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Reclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.
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Use this skill when the user is doing hands-on NVMe-over-Fabrics storage-target work on a BlueField DPU or ConnectX NIC with DOCA STA — standing up a doca_sta DOCA Core context that accelerates the target-side NVMe-oF data path over RDMA, defining doca_sta_subsystem targets (NQN + namespaces) backed by local NVMe-PCI backend disks (doca_sta_be), checking device support via doca_sta_cap_is_supported, sizing the per-connection I/O queues, or debugging DOCA_ERROR_* from a STA call. Trigger even when the user does not say "DOCA STA" — typical implicit phrasings include "my NVMe-oF Connect never completes", "Identify Controller times out over RoCE", "16 I/O queues at depth 1024 — does this BlueField support that", "offload the nvmf target onto the DPU", or "DOCA_ERROR_IO_FAILED on an NVMe read". Refuse and route elsewhere for DOCA install, raw RDMA data movement, raw packet I/O, flow-rule programming, or initiator-side / host NVMe stack work — those belong to other skills.
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