4,978 件 ・ 関連度順
概要と使いどころ
16:9 HTML deck in editorial-minimalist taste. Warm cream slides, serif display + grotesque body, hairline rules, monospace meta, generous macro-whitespace, one accent. Distilled from Leonxlnx/taste-skill `minimalist-skill`.
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。
Team-management dashboard skill in the FlowAI aesthetic — three tabs (Team Members, Team Details, Activity Log), KPI stat row, member table, role distribution bar chart, online presence and activity sparklines, and a top-contributors panel, all in a single self-contained HTML file with light/dark theming, hoverable chart tooltips, click-to-zoom panels, and CSV export. Use when the brief asks for a team / workspace admin dashboard, an interactive admin dashboard with charts, or names FlowAI.
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。
Automate D2lbrightspace tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Agent spawning, lifecycle management, and coordination patterns. Manages 60+ agent types with specialized capabilities. Use when: spawning agents, coordinating multi-agent tasks, managing agent pools. Skip when: single-agent work, no coordination needed.
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。
SPARC development workflow (Specification, Pseudocode, Architecture, Refinement, Completion). Use when: new features, complex implementations, architectural changes. Skip when: simple fixes, documentation, configuration.
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。
Run the SPARC Refinement and Completion phases — review code, improve test coverage, validate against specification, and generate documentation
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。
Run the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in memory
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。
Run the SPARC Pseudocode and Architecture phases (2 and 3) — write algorithm pseudocode, design module boundaries and API contracts, then implement
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。
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration
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。
Spawn nested sub-agents (agents that spawn sub-agents, up to depth=5) via Claude Code's native Task tool — for context-managed deep delegation
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 releasing the Cline VS Code extension — stable (standalone SDK build of main via ext-vscode-publish), nightly (ext-vscode-publish-nightly, manual dispatch), or an emergency legacy-branch hotfix (ext-vscode-publish-legacy). Guides version selection, changelog, workflow dispatch, environment approvals, tagging, post-publish verification, and the remaining retirement of the finished A/B rollout machinery.
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。
Organize existing ChatGPT Pages and Space structure when the user requests changes to that structure. Do not use for ordinary chat drafts or advice; a selected Page alone does not authorize reorganization. A narrow text edit does not need this workflow.
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。
Reconcile new evidence into existing ChatGPT Pages or a Space when the user requests upkeep. For a direct text correction, use write-page. Do not use for ordinary chat drafts, writing, or advice, or treat a selected Page as a request to save. Schedule future upkeep only when requested.
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 the user wants the agent to connect to an existing Twenty workspace through MCP, retrieve or inspect workspace records and metadata, or present Twenty CRM data as readable Markdown with formatted dates, values, record links, and compact tables instead of raw API output.
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。
Local document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering pages to PNG for multimodal agents. Distinguishing capabilities are spatial text boxes, Markdown, page raster output, and local parsing with optional custom HTTP OCR.
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。
Queries the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for kernel attack surface, namespace and cgroup boundaries, container isolation assumptions, syscall paths, and escape primitive verification. Use when the user asks to analyze container-to-host escape paths, kernel exploit prerequisites, namespace crossover, capability misuse, or prove whether an exploit primitive crosses the sandbox boundary. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Configure and optimize Nx monorepo workspaces. Use when setting up Nx, configuring project boundaries, optimizing build caching, or implementing affected commands.
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。
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
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。
Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.
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。
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
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。
Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.
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 whenever you need the allowed_tools / skills preset for a sub-agent role before calling spawn_subagent. Trigger especially when user mentions: "role", "spawn_subagent", "allowed_tools", or any OMP role name (executor, architect, analyst, critic, security-reviewer, code-reviewer, qa-tester, planner, explore, debugger, verifier). Also use when a /ultrawork, /ralph, /autopilot, /ultraqa, or /team workflow step asks for role tool/skill configuration.
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。
Detects container escape at runtime across tooling - namespace manipulation, capability abuse, kernel exploits, sensitive host mounts, and anomalous syscalls - and explains which signals matter regardless of whether Falco, Sysdig, auditd, or an EDR is doing the collection. Use when deciding what breakout behaviour to monitor, investigating a suspected Docker or Kubernetes breakout, or comparing escape coverage across runtime sensors. Keywords: container escape, breakout, namespaces, CAP_SYS_ADMIN, privileged, hostPath, kernel exploit, syscall. Do not use for Falco rule syntax itself - use detecting-container-escape-with-falco-rules; for a static configuration sweep use performing-container-escape-detection.
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。