「intelligent」の検索結果

CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.

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Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI 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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Parse git history and produce or update a CHANGELOG.md following the Keep a Changelog convention. Supports Conventional Commits, basic prefix conventions, and unstructured commit messages. Intelligently categorizes changes, detects breaking changes, links to PRs/issues, and handles both initial generation and incremental updates. **Triggers — use this skill when:** - User asks to "generate", "create", "update", or "write" a changelog - User mentions "CHANGELOG", "changelog", "release notes" - User says "document changes", "what changed since last release" - User wants to "prepare a release" and needs a changelog entry - User asks to "clean up" or "reformat" an existing changelog **Covers:** Any git-based project. Handles Conventional Commits (feat/fix/chore), Angular convention, basic prefixes (Add/Fix/Remove), and freeform commit messages. Outputs Keep a Changelog format with optional Common Changelog enhancements.

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Build unified multi-level category taxonomy from hierarchical product category paths from any e-commerce companies using embedding-based recursive clustering with intelligent category naming via weighted word frequency analysis.

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Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.

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。

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI 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。

Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.

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Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.

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Expert guide for intelligent model cascading and routing — complexity-scored task routing from Flash/Haiku to Sonnet/Opus/Astra, dynamic escalation with quality gates, 40-60% token cost reduction while maintaining output quality / Panduan ahli untuk kaskade dan routing model cerdas — routing tugas berbasis skor kompleksitas dari Flash/Haiku ke Sonnet/Opus/Astra, eskalasi dinamis dengan gerbang kualitas, pengurangan biaya token 40-60% dengan kualitas output terjaga.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Design and implement agentic AI workflows and patterns. Covers ReAct, planning agents, tool use, memory systems, and multi-agent orchestration. Use when building autonomous AI agents, implementing complex task automation, or designing intelligent workflow 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。

Use this skill in the scenario of intelligent agent application development.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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 task distributor specializing in intelligent work allocation, load balancing, and queue management. Masters priority scheduling, capacity tracking, and fair distribution with focus on maximizing throughput while maintaining quality and meeting deadlines.

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Intelligent financial management skill for Claude Code that provides comprehensive PocketSmith API integration with AI-powered analysis, transaction categorization, rule management, tax intelligence, and scenario planning. Use when working with PocketSmith data for (1) Transaction categorization and rule management, (2) Financial analysis and reporting, (3) Australian tax compliance (ATO) and deduction tracking, (4) Scenario analysis and forecasting, (5) PocketSmith setup health checks, (6) Budget optimization and spending insights.

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Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration. Use when: the task directly matches context manager responsibilities within plugin agent-orchestration. Do not use when: a more specific framework or task-focused skill is clearly a better match.

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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.

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Manages ArgoCD application sync operations with intelligent drift detection and rollback strategies. Uses the ArgoCD gRPC API and ApplicationSet CRD for multi-cluster GitOps deployments.

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Deploys intelligent web scraping actors on the Apify platform using the Apify SDK with RequestQueue and Dataset APIs. Handles dynamic content via Apify CheerioCrawler and PlaywrightCrawler with automatic scaling.

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Edit images with precision using ImageMagick 7, sips, transparent-background, and Pillow — with intelligent routing to AI semantic editing (Gemini/nano-banana-pro) for content-aware operations. Covers cropping, resizing, compositing, annotating, format conversion, color adjustment, background removal, batch processing, and montage creation. Triggers on image editing, crop, resize, composite, annotate, remove background, format conversion.

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。