92 件 ・ 関連度順
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
OpenClaw全体の品質検証を継続し、不具合の再現から根本原因の修正、レビュー、反映確認まで進めます。実環境や負荷の検証結果を、再開可能な報告書に記録します。
Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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。
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。
互いに依存しない不具合やテスト失敗を別々のエージェントに割り当て、並行して調査・修正するスキル。担当範囲を明確にし、結果の確認と変更の統合まで案内します。
実在するファイルや行番号を参照した段階的なコード案内をCodeTour形式で作成し、新任者の学習、設計の理解、PRレビュー、障害原因の調査に役立てるスキル。
Use when working on, reviewing, or extending GitNexus's CFG/taint/PDG subsystem (the `--pdg` layers), or when reasoning about source→sink data-flow findings. Examples: "How does taint analysis work here?", "Why didn't explain find this flow?", "Add a new sink/source", "Review the interprocedural taint code".
日本語の概要は準備中です。原文の説明を表示しています。
Write or rewrite architecture subsystem docs, ADRs, and reference material for the engineering team. Use when creating, updating, or reviewing docs under docs/architecture docs/, or when the user asks you to write documentation that describes the system's design.
日本語の概要は準備中です。原文の説明を表示しています。
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog instead).
日本語の概要は準備中です。原文の説明を表示しています。
Deep code analysis with ASCII visualizations showing structure, flow, and state transitions. Use when analyzing patches/diffs, explaining classes or subsystems, understanding code architecture, reviewing changes for inconsistencies, or when asked to visualize how code works. Provides before/after diagrams, data/control flow, concurrency analysis, assumptions, and failure modes. Triggers on explain this patch/code/class, how does X work, show me the flow, visualize this change, code review requests, or proactive analysis during PR reviews.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Explain a body of work plainly so a person actually understands it. Runs the `how` and `why` skills and weaves what they find into one clear explanation. Use for 'teach me this', 'help me really understand X', 'explain this change or subsystem to me'.
日本語の概要は準備中です。原文の説明を表示しています。
Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which package owns this", "is this the right layer"). Explains subsystem architecture, runtime flow, onboarding mental models. Use why for motivation.
日本語の概要は準備中です。原文の説明を表示しています。
Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what's the architecture / which subsystems exist?", "what's the impact of changing this?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).
日本語の概要は準備中です。原文の説明を表示しています。
Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).
日本語の概要は準備中です。原文の説明を表示しています。
Converts a double-precision Simulink system or subsystem to single precision using DataTypeWorkflow.Single (Fixed-Point Designer). The single conversion replaces all user-specified double-precision data types, as well as output data types that compile to double precision, with single-precision data types. Use this skill when converting Simulink systems to single precision, reducing memory usage of a Simulink system, optimizing for embedded targets. Do NOT use for standalone MATLAB .m code single conversion.
日本語の概要は準備中です。原文の説明を表示しています。