「script」の検索結果

Use when working with Aws Sqs — aWS SQS queue metrics, dead letter queue management, SNS subscription management, message attributes, and queue health monitoring.

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MANDATORY parallel execution patterns, cluster overview script, -o json with jq filtering, batch operations, and common pitfalls

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Use when working with Gcp — cost anti-hallucination rules, MANDATORY parallel execution patterns (30x speedup), monitoring aligners, reusable billing/pricing scripts, VAT/tax handling, and filtering/pagination.

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 working with Aws Rds Deep — aWS RDS deep analysis covering Performance Insights, event subscriptions, proxy management, and global database status. Covers PI counter metrics, wait event analysis, top SQL queries, read replica lag, storage autoscaling, and Aurora-specific diagnostics.

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 working with Aws Pricing — aWS pricing helper for cost queries. ALWAYS use get_aws_cost script for pricing questions. Use when: - User asks about AWS resource costs or pricing - User wants to compare pricing across regions - User needs spot, on-demand, or reserved pricing info Triggers: aws pricing, aws cost, how much does, ec2 price, rds cost, s3 pricing.

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Use when working with Aws Cloudwatch Logs — aWS CloudWatch Logs group management, Logs Insights query execution, metric filter analysis, retention policy review, and subscription filter management. Covers log group inventory, storage cost optimization, query patterns, and cross-account log aggregation.

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Drive and script tldraw offline canvases with an agent.

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Scaffolding für Scripts, Skills und Plugins aus Templates.

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Teams meeting summaries, job replay, Graph subscriptions.

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YouTube transcripts to summaries, threads, blogs.

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Use for the self-evolving civilization - skills, memories, swarm agents, energy economy, grid duels, metacognition. Run `python scripts/autonomous_loop.py --loop` from the repo root (or `darwin_engine.py --full` for skill evolution only).

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Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.

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This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.

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Extract clean Markdown or text from one or more known URLs through Tavily. Use when the user supplies specific pages and needs their content, including query-focused chunks or JavaScript-rendered pages.

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Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy, 審查論文, 論文審查, 模擬審查, 同儕審查, 幫我審這篇, 以審查人角度評估, 審查者校準, 논문 심사, 동료 심사, 모의 심사, 심사자 관점에서 평가, 심사자 보정.

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React 19+ with TypeScript — hooks, custom hooks, state management (useState/useReducer/useContext), React Query/SWR, Tailwind CSS, performance. Use when building React components, apps, or optimizing renders.

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Evaluate recommender system quality for the CSX4207 Vinyl Record Store. Use whenever you must compute or report recommender metrics (Precision@k, Recall@k, HitRate@k, MRR, MAP@k, NDCG@k, coverage, diversity, novelty, serendipity, personalization), design an evaluation protocol/split, run a baseline comparison, or write the evaluation section of a course deliverable. Covers formulas, JavaScript reference implementations, and the reporting checklist.

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Upload approved local HTML, markdown, or image assets to a Stitch project using direct MCP for small DESIGN.md files or the bundled API script for larger files.

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Convert approved Stitch exports into accessible React and Vite dashboards with DESIGN.md tokens, TanStack Query data boundaries, responsive layouts, and optional read-only Web3 integrations.

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Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and helper script for reproducible notebook structure and safer editing.

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Next.js 16.2.4 with TypeScript — App Router, Server Components, use cache directive, Turbopack dev, Server Actions, ISR, SSR, SSG, MCP devtools, metadata API, route handlers, instrumentation.

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Use when the task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) via `playwright-cli` or the bundled wrapper script.

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Build high-quality MCP servers with strong tool design, structured outputs, clear error handling, and realistic evaluations. Use when creating or improving MCP servers in TypeScript or Python for external APIs, services, or internal platforms.

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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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。