1,999 件 ・ 関連度順
概要と使いどころ
Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".
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。
Turn the current conversation into a spec and publish it to the project issue tracker: no interview, just synthesis of what you've already discussed.
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。
Compact the current conversation into a handoff document for another agent to pick up.
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。
Hand the current conversation off to a fresh background agent that picks up the work immediately.
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。
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web 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。
Query EBI QuickGO REST API for GO terms and protein annotations. Fetch term metadata by ID, search by keyword, walk ancestor/descendant hierarchies, download annotations filtered by taxon, evidence code, aspect. Use for GO resolution, ontology traversal, annotation retrieval before enrichment. Use gseapy-gene-enrichment for enrichment; uniprot-protein-database for proteins.
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。
Universal QA checklist for generated scientific plots: overlapping labels, clipped text, missing axes/legends, overcrowded data, and cross-journal resolution/format guidance.
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。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Provides Spring Data Neo4j integration patterns for Spring Boot applications. Use when you need to work with a graph database, Neo4j nodes and relationships, Cypher queries, or Spring Data Neo4j. Creates node entities with @Node annotation, defines relationships with @Relationship, writes Cypher queries using @Query, configures imperative and reactive Neo4j repositories, implements graph traversal patterns, and sets up testing with embedded databases.
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。
Adversarially fact-check DOCS.md files against the actual source code, verifying every concrete claim rather than trusting structure checks. Use when the user asks to audit docs, verify documentation accuracy, check whether docs are still true, or on a scheduled documentation review. This is the semantic layer the mechanical checkers cannot cover.
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 starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
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。
Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead.
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。
Continue the current local Codex conversation and coding task in an AgentKit Studio cloud Codex Sandbox using a Studio URL and short one-time pairing code. Use when the user asks to hand off, migrate, or continue a local repository in Studio, especially when a prompt contains a Studio address and pairing code. Export the complete user-visible user and assistant message history from every prior turn, including interrupted turns and progress commentary, create a temporary Studio Sandbox, restore tracked and non-ignored working-tree files plus Git metadata, inject that visible history into the cloud Thread, transfer GitHub CLI authentication separately when needed, and send one final continuation message; never copy system or developer prompts, reasoning, tool logs, SQLite state, global configuration, local skills, or SSH private keys.
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 current conversation context to recommend the best skills and subagents for the task at hand. Use proactively when unsure which tool, skill, or agent to use.
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Analyze Reddit threads for sentiment, consensus opinions, top arguments, and discussion patterns. Use this when users want to understand Reddit community opinions, analyze discussions, or gather insights from subreddit conversations.
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Enhanced skill navigator that maps conversation history, recommends multi-skill chains, identifies patterns from past usage, and learns from session outcomes. Goes beyond basic scout with deep context analysis and workflow orchestration.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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Convenes a multi-LLM expert panel to pressure-test hard-to-reverse decisions. Use when reversibility score is low and adversarial review is warranted.
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 Fairlearn to assess group fairness, compute disparity metrics, visualize subgroup performance, and mitigate unfairness with preprocessing, reductions, postprocessing, or adversarial learning.
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Use FFCV to convert datasets into .beton files, build fast PyTorch loader pipelines, handle image and custom fields/transforms, and tune cache, traversal, and throughput behavior.
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Foolbox operating guidance for wrapping vision models, running adversarial attacks, measuring clean and robust accuracy, handling NumPy/PyTorch/TensorFlow/JAX backends, and loading Foolbox-compatible model-zoo repositories.
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Operate ChatterBot conversational dialog engine workflows, including chatbot setup, training, logic adapters, storage backends, Django integration, and optional LLM adapters.
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Use the ALAE repository for Adversarial Latent Autoencoder data preparation, CUDA training, checkpoint-backed generation, latent editing, and legacy metrics.
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File upload attack hunting - extension/content-type/magic-byte bypass to web-shell RCE, path traversal in filename, SVG/XML XSS, zip slip, and pixel-flood DoS. Wiki-first, FIND schema 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。