「selection」の検索結果

Badge design and selection knowledge base for shields.io badges in README files. Use when writing or updating READMEs, choosing badge layouts, selecting badge styles, adding project health indicators, or picking Simple Icons logo slugs. Covers shields.io URL encoding rules, static vs dynamic badge selection, style variants (flat/flat-square/for-the-badge/social/plastic), layout patterns (two-tier/inline/centered), project-type badge sets for Python/JS/Rust/Claude plugins, color reference, non-obvious logo slugs, and common anti-patterns to avoid.

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Coordinates central clearing requirements with CCP selection, margin optimization, and clearing threshold monitoring. Use when managing clearing obligations, optimizing CCP selection, or analyzing clearing economics.

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Tested output format specifications for Claude prompts. Use when the user wants a specific deliverable structure — retrieving, reviewing, customizing, or building output formatting. Trigger on: "give me the Executive Brief format," "show me the output template for," "I need the Decision Matrix structure," "show me available output formats," "build a custom output format for." Provides 10 ready-to-use structures (executive briefs, technical designs, research summaries, implementation plans, decision matrices, competitive analyses, post-mortems, stakeholder updates, strategic memos, process documentation) plus a custom template, with per-section length guidance and format constraints tested against Claude formatting defaults. If unsure which format fits, use rootnode-block-selection first if available. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or choosing reasoning/identity approaches (use rootnode-block-selection if available).

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Claude Code の SubAgent（agents/*.md）を正しく定義するためのベストプラクティスガイド。 YAML frontmatter、ツール選択、3-Phase 構造、コンテキスト受け渡し、アンチパターンを網羅。 Use when: agents/*.md を書く、SubAgent 定義を改善する、エージェントの動作が想定外、 コンテキストが渡らない、ツール選択に迷う。 Triggers: "subagent", "agent definition", "agents/*.md", "エージェント定義", "サブエージェント", "3-Phase", "context passing", "コンテキスト渡し", "tool selection", "ツール選択", "subagent_type", "bypassPermissions"

AI multi-path reasoning coherence using bee democracy — independent evaluation of competing approaches, waggle dance as reasoning-out-loud, quorum sensing for confidence thresholds, and deadlock resolution. Use when forage-solutions has identified multiple valid approaches and a selection must be made, when oscillating between options without committing, when justifying an architecture or tool choice with structured reasoning, or before an irreversible action where the cost of the wrong choice is high.

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TanStack Table best practices for building headless, type-safe data tables in React with sorting, filtering, pagination, row selection, and column management. Use when building data grids, implementing client-side or server-side table features, defining column structures, managing table state, or optimizing table rendering performance.

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Generate HTML slide presentations (1 slide = 1 HTML file, 1280x720px) using Tailwind CSS, Font Awesome, and Google Fonts. Use when the user asks to create a new presentation deck or slide HTML files. Covers design guidelines, 43 layout patterns, component library, and PPTX conversion compatibility rules. Supports style selection (creative, elegant, modern, professional, minimalist) and theme selection (marketing, portfolio, business, technology, education).

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"Apply organizational ecology (Hannan and Freeman) to analyze population-level dynamics of organizational founding, failure, and selection. Use this skill when the user needs to explain industry-level patterns of birth and death rates, analyze structural inertia and liabilities of newness or aging, evaluate why adaptation is rare relative to selection, or when they ask 'why do most startups fail', 'why is this industry dominated by old firms', or 'why do organizations resist change despite environmental pressure'.".

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Use when deciding which Salesforce automation tool should own a requirement, including before-save and after-save Flow, screen Flow, scheduled Flow, invocable Apex, Apex triggers, and migration off Workflow Rules or Process Builder. Triggers: 'Flow or Apex trigger', 'which automation tool should I use', 'Process Builder migration', 'Workflow Rule retirement', 'same-record update or trigger', 'automation decision record', 'why did we reject Apex', 'one object one order', 'automation inventory for this object', 'scheduled Flow or Batch Apex'. NOT for 'which tool should stamp this field on save, and will it recurse' — use admin/workflow-field-update-patterns. NOT for actually running the conversion of an existing Process Builder — use flow/process-builder-to-flow-migration.

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Use this skill to determine whether a Salesforce Commerce project should use B2B Commerce (account-gated, BuyerGroup-based, contract pricing) or D2C Commerce (consumer storefront, guest checkout, individual-based). Trigger keywords: B2B vs B2C, commerce platform selection, buyer journey, account-based purchasing, consumer storefront, licensing decision. NOT for implementation mechanics — use admin/b2b-commerce-store-setup or admin/b2c-commerce-store-setup for configuration work. NOT for Salesforce B2C Commerce (SFCC/Business Manager) setup.

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Reference guide for using Xpoz effectively. Load this skill whenever working with Xpoz MCP tools, SDKs, or CLI — it ensures correct query syntax, optimal field selection, proper pagination, and best practices for every Xpoz interaction. Covers authentication, query syntax (boolean operators, date filtering), response modes (fast/paging/CSV), field selection, tracking setup, and all platform tool references (Twitter, Instagram, Reddit, TikTok). Use for ANY Xpoz-related work, not just explicit best-practices questions.

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Design and safely evolve data models, database schemas, migrations, queries, indexes, and ORM data-access boundaries. Use for data-model, migration, query, index, ORM schema, or datastore-selection tasks. Discover the existing engine and migration source before proposing syntax.

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Trim multiple sequence alignments using ClipKIT, trimAl, BMGE, Divvier, or HMMcleaner with mode selection guidance per downstream goal. Use when removing unreliable columns or contaminating residues before phylogenetic inference, HMM building, or selection analysis.

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Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

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Evaluates patient’s selection of sensor insertion sites and site care to prevent irritation or infection. Triggers include: 'Review CGM site rotation', 'Check sensor site hygiene and adhesion'.

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Guides selection of the optimal confirmatory test (CCT, SIT, FUT, OSLT) for primary aldosteronism based on patient safety and feasibility. Triggers include when a clinician asks “Which confirmatory test should I use?” or evaluates patient comorbidities before testing.

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Expert-level green chemistry knowledge. Use when working with sustainable synthesis, atom economy, solvent selection, catalysis, renewable feedstocks, waste reduction, life cycle assessment, or the 12 principles of green chemistry. Also use when the user mentions 'atom economy', 'E-factor', 'green solvent', 'catalysis', 'renewable feedstock', 'waste minimization', 'sustainable chemistry', 'life cycle assessment', 'PMI', 'solvent selection guide', or 'benign by design'.

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Expert-level evolutionary biology covering natural selection, genetic drift, molecular evolution, phylogenetics, speciation, macroevolution, and the modern synthesis.

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Catalog and selection guide for Exasol tools, extensions, connectors, integrations, and architecture patterns. Use when the primary intent is comparison, discovery, support-status research, or architecture selection rather than executing a workflow; hand operational work to the selected dedicated skill. Covers deploy, load, explore, enrich, surface, and scale capability families, including named products with no dedicated skill of their own such as Lakehouse Turbo, the Exasol MCP Server, Governed SQL / Text-to-SQL MCP Server, Agent Control Plane, Extension Manager, Terraform and Ansible tooling, and BI, ETL, and warehouse integrations such as Databricks, SAP, Kafka, Power BI, and Tableau.

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。

Synthesized reference notes from three US Government vehicle-safety sources: NHTSA Cybersecurity Best Practices for the Safety of Modern Vehicles (updated 2022, final), NHTSA Automated Driving Systems 2.0: A Vision for Safety (2017), and a 16-section selection of 49 CFR Part 571 (FMVSS) pinned at eCFR versioner date 2025-01-01. Use for voluntary vehicle cybersecurity practices, the ADS 2.0 twelve priority safety design elements, and FMVSS occupant protection, brakes/stability/lighting, and fuel/EV integrity requirements. Not ISO 26262, not ASPICE, not ISO/SAE 21434, not UNECE R155/R156, not SAE J3016, and not the full Part 571; the FMVSS material is a selection of vehicle-level minimum performance standards, not a process standard.

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Use native Worktrunk commands for explicit Git worktree operations when requested. Distinguish repository selection from worktree selection, and inspect command help before changing configuration or hooks.

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Produces complete monthly marketing campaign plans — themes, product selection, weekly content structures, recurring posts, promotions, and presentation decks. Use when the task asks to "plan this month's content", "orchestrate a campaign", "build a marketing plan", "select products for promotion", "create a campaign calendar", or "monthly SOP". Covers the full 7-step campaign lifecycle from theme selection to presentation delivery.

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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.

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Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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。