「tasks」の検索結果

Expert knowledge for Azure Batch development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Batch pools, autoscale, private networking, CMK encryption, or CI/CD for HPC/render jobs, and other Azure Batch related development tasks. Not for Azure Container Instances (use azure-container-instances), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Functions (use azure-functions), Azure Virtual Machines (use azure-virtual-machines).

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Expert knowledge for Azure Blob Storage development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing Blob tiers, configuring NFS/SFTP or BlobFuse, securing with SAS/RBAC, tuning performance, or using SDK/CLI, and other Azure Blob Storage related development tasks. Not for Azure Files (use azure-files), Azure Queue Storage (use azure-queue-storage), Azure Table Storage (use azure-table-storage), Azure NetApp Files (use azure-netapp-files).

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Expert knowledge for Azure Blueprints development including troubleshooting, decision making, security, configuration, integrations & coding patterns, and deployment. Use when migrating off Blueprints, mapping to PBMM/ISO/SWIFT, automating via CLI/PowerShell/REST, or fixing deployment errors, and other Azure Blueprints related development tasks. Not for Azure Policy (use azure-policy), Azure Resource Manager (use azure-resource-manager), Azure Resource Graph (use azure-resource-graph), Azure Portal (use azure-portal).

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Plan and execute substantial multi-file builds, features, migrations, and refactors with Astra as orchestrator and DeepSeek V4.1 Flash as the native Codex implementation worker. Use for phased planning and delegated build execution, including existing Superpowers or GSD plans. Skip trivial edits and explicitly single-agent tasks. Worker children must not invoke this orchestration skill.

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Start a substantial build with Codex on Crack. Inspect the user's own configured models and readiness, recommend a lead/worker/tool shape with reasons, and let the user decide before work starts. Use before delegating; keep small or solo tasks direct.

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Maintains a repo-local task forest or task DAG for the current workspace. Use when the user asks to initialize or update a task forest, close a session, summarize evolving project work, align a request with a global goal, track progress/history/deviations/todos, save or apply a task proposal, or export the client-readable task-forest HTML. Do not use for executing the tracked tasks themselves or for generic HTML work.

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Create detailed implementation plans with bite-sized tasks for engineers with zero codebase context

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Execute implementation plan by dispatching fresh subagent for each task, with code review between tasks

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Use this skill when the user wants an evidence-based research memo, literature review, market/policy/technical landscape, or a multi-source decision brief with citations, trade-offs, and a clear conclusion. Best for tasks that need synthesis across multiple external sources, iterative follow-up research, or a reusable written artifact. Do not use for quick factual lookups, single-source summaries, simple Q&A, summarizing one document the user already provided, plan-only requests where the user explicitly defers the actual research, or when the user wants a short answer with no need for cited evidence (a short but evidence-backed conclusion is still a brief memo, which this skill covers). Chinese trigger examples: "帮我调研", "深度研究", "综述报告", "技术选型分析", "竞品研究", "政策分析".

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Use when the user wants a small, ad-hoc change handled with GSD guarantees (atomic commits, STATE.md) but without the full pipeline. Tasks land in .planning/quick/; --full, --discuss, --research, --validate add the skipped agents back.

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Create custom subagents for specialized AI tasks. Use when the user wants to create a new type of subagent, set up task-specific agents, configure code reviewers, debuggers, or domain-specific assistants with custom prompts.

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Use when executing implementation plans with independent tasks in the current session

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Use when completing tasks, implementing major features, or before merging to verify work meets requirements

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End-of-session brain flush. Scans the full conversation, extracts all new entities, decisions, tasks, events, relationships, and updates, presents a summary, and writes to project_brain.db after confirmation. Use at end of every session or when you want to capture everything discussed.

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Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

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Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections.

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Generate multiple diverse solutions in parallel and select the best. Use for architecture decisions, code generation with multiple valid approaches, or creative tasks where exploring alternatives improves quality.

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Break down feature requests into detailed, implementable plans with clear tasks. Use when user requests a new feature, enhancement, or complex change.

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Find the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural expansion, and in-body verification. Use this skill for literature-finding and paper-retrieval tasks of any kind, including clinical, biomedical, drug, gene, disease, and other life-science questions, whether the answer is a single paper or a full multi-paper set. The index is reached only through the `firecrawl_research_*` MCP tools or the `firecrawl research` CLI subcommands. Calling `firecrawl_search` with its `categories` option set to `["research"]` is a different feature — it filters ordinary web search to research-affiliated websites (the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites) and returns page results from them, without querying the paper records in this index.

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Safely inspect and modify local Avibe configuration, routing, runtime settings, watches, scheduled tasks, Avibe Cloud remote access, and operational state. Also use it to report an Avibe bug, send product feedback, or file a feature request from the current conversation.

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Use Avibe Harness for durable Agent delegation, Sessions, scheduled Tasks, Watches, Runs, queues, and work that must continue beyond the current turn.

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Execute the next TaskMaster task using the implementation plan with CDD verification. Picks the next ready task, matches it to the plan step, implements via a dispatched subagent, verifies subtasks with evidence, marks the task done, and loops until every task is complete. Wraps the TaskMaster next -> in-progress -> done lifecycle with CDD GREEN / RED / BLUE verification and the plugin's triple-verification rule. Autonomous by design — no user prompts inside the loop.

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Use when executing implementation plans with independent tasks in the current session

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React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.

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。