「structures」の検索結果

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Structured 6-step procedure for improving, renovating, or rebuilding existing pipelines, individual project folders, documentation structures, or software stacks. Addressable as "pipeline optimizer" (for whole topic pipelines, e.g. a software, research, or game-dev pipeline) or "project-folder optimizer" (for individual project folders within a pipeline, e.g. a single software tool or paper project). Triggers on tasks like "improve pipeline X", "optimize the stack", "rebuild Y", "renovation", "pipeline refactoring", "clean up project folder", "improve folder structure", "unify conventions", "documentation consolidation", "integrate into existing system", or any substantial intervention in established structures. Delivers building-stock analysis, purpose clarification, ideal sketch, gap plan, empirical pain-point identification, and retests with fresh subagents. Prevents parallel standards, duplication, and pipeline breaks.

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Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.

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Access AlphaFold DB's 200M+ predicted structures by UniProt ID. Download PDB/mmCIF, analyze pLDDT/PAE, bulk-fetch proteomes via Google Cloud. For experimental structures use PDB; for prediction use ColabFold or ESMFold.

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Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.

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。

Expert en marketing d'affiliation (structures de commission, tracking, recrutement, prévention de fraude)

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。

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

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Design author-friendly content structures that work for non-technical content creators. Use when designing content structures for blocks in AEM Edge Delivery Services.

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Organize, categorize, rename, and manage files systematically using automated rules, naming conventions, and folder structures for efficient file management. Use when organizing uploaded files, implementing file naming conventions, categorizing files by type or metadata, creating folder structures, cleaning up messy directories, automating file movements, implementing media libraries, or building file management systems.

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。

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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。

講座の計画から章・演習・問題・解答・解説のフォルダと説明ファイルのひな形を作り、命名規則やリンクを検査して教材づくりの土台を整えるスキル。

• 新しい講座の演習構成を作りたいとき
• 問題・解答・解説のひな形づくり
• 演習の改番と構成チェック

リポジトリから外部経由で悪用できる脆弱性を探し、入力から危険な処理までの経路、再現手順、影響を確認して報奨金制度向けの報告を支援するスキル。

• 外部リクエストで届く脆弱性の調査
• 静的解析の結果を精査したいとき
• 最小限の再現手順と影響の整理

Redisを使ったキャッシュ、アクセス回数制限、複数処理の同時実行制御、メッセージ配信を設計し、キーの有効期限や接続管理も見直すスキル。

• APIにキャッシュを追加したいとき
• 利用者別にアクセス回数を制限
• ワーカーのジョブ同時実行を防止

システムの構成やデータの流れを、クリックして詳しく読める立体的なHTMLマップに整理します。同じデータから説明文も生成し、設計上の判断や未解決の質問を管理します。

• システム構成とデータの流れの説明
• 新しい設計をチームで検討したいとき
• レビュー中の未解決の質問の管理

Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, splitting uncommitted work in a messy working tree into clean atomic commits, opening or reviewing a pull request (PR), pushing to a remote, or when you need to organize work across multiple parallel streams. Use when cutting a release, choosing a semantic version bump, tagging, or writing a changelog.

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。

Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.

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。

Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.

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。

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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 generating URL slugs from article titles, auditing URL structures for unnecessary depth or length, or reviewing URL patterns in a CMS or router 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。

Use when auditing URL structures for word separator format, generating slugs from titles, or planning a URL migration from underscores or spaces to hyphens with appropriate redirects.

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。

Writes, structures, and formats SEO-optimized blog posts for the Appwrite blog following established conventions for voice, tone, frontmatter, and post structure. Use when the user asks to write, draft, create, or outline a blog post, or when working with files in src/content/blog/posts/.

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。

Analyzes, validates, converts, and transforms materials structures and computed materials data with pymatgen. Use for local phase diagrams, symmetry sensitivity, electronic-structure I/O, and bounded Materials Project queries.

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Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.

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Queries 20+ bioinformatics resources through CLI/Python. Supports quick lookups of gene info, BLAST/BLAT, viral sequence downloads, PDB/mmCIF structures, G2P residue annotations, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

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。