8 रिपॉजिटरी
Configurations for defining the capabilities and operational scope of AI agents.
Distinguishing note: Focuses on defining coding skills and workflow integrations for AI assistants.
Explore 8 awesome GitHub repositories matching artificial intelligence & ml · Agent Skill Definitions. Refine with filters or upvote what's useful.
ECC एक LLM एजेंट ऑर्केस्ट्रेशन फ्रेमवर्क और क्रॉस-प्लेटफ़ॉर्म AI टूलिंग सूट है जिसे मल्टी-मॉडल वर्कफ़्लो का समन्वय करने के लिए डिज़ाइन किया गया है। यह विभिन्न AI-संचालित कोड संपादकों में जटिल सॉफ्टवेयर विकास कार्यों को निष्पादित करने के लिए विशेष एजेंट भूमिकाओं, पुन: प्रयोज्य कौशल और संरचित नियोजन को प्रबंधित करने के लिए एक सिस्टम प्रदान करता है। प्रोजेक्ट खुद को एक मॉडल कॉन्टेक्स्ट प्रोटोकॉल मैनेजर के रूप में अलग करता है, जो बाहरी सर्वर को एकीकृत करने और टूल निष्पादन का ऑडिट करने के लिए एक कॉन्फ़िगरेशन परत प्रदान करता है। यह आगे एक एजेंटिक सुरक्षा सैंडबॉक्स लागू करता है जो संवेदनशील फ़ाइल एक्सेस को प्रतिबंधित करता है और स्वायत्त वर्कफ़्लो को सुरक्षित करने के लिए गुप्त रिसाव (secret leakage) के लिए स्कैन करता है। फ्रेमवर्क AI कोडिंग वर्कफ़्लो ऑटोमेशन, टेस्ट-ड्रिवन डेवलपमेंट गार्डरेल्स, इंटेलिजेंट रूटिंग के माध्यम से मॉडल लागत ऑप्टिमाइज़ेशन और स्टेट-आइसोलेटेड मेमोरी प्रबंधन सहित व्यापक क्षमता क्षेत्रों को कवर करता है। इसमें भाषा-विशिष्ट कोडिंग मानकों को लागू करने और विभिन्न एकीकृत विकास वातावरणों में एजेंट व्यवहारों को प्रबंधित करने के लिए टूल भी शामिल हैं। सिस्टम को एक कमांड-लाइन इंटरफ़ेस के माध्यम से प्रबंधित किया जाता है जो टूल इंस्टॉलेशन, कॉन्फ़िगरेशन मरम्मत और टूलिंग प्रीसेट की तैनाती को संभालता है।
Defines reusable coding skills and behavioral instincts that can be injected into agent contexts.
Mem0 is an agent-agnostic memory layer designed to provide intelligent agents with long-term persistence and cross-session state management. By acting as a centralized service, it allows diverse AI agents to recall user preferences, past interactions, and historical context, ensuring continuity across multiple workflows and independent agent systems. The platform distinguishes itself through a multi-signal retrieval engine that combines semantic vectors, keyword matching, and entity-linked metadata to surface the most relevant information. It employs an adaptive memory engine that automatical
Configures coding skills for AI assistants to ensure consistent SDK and CLI usage.
Impeccable is a design system framework for large language models and an AI coding assistant plugin. It functions as an AI-driven UI generator and a rule-based design linter, providing a structured set of instructions and configuration files to standardize the production of professional user interfaces. The project features a design token orchestrator that maps standards across different AI provider environments and a config-driven factory for managing skills across multiple providers. It employs a deterministic rule engine to audit interfaces for accessibility violations, typography errors,
Converts single skill definitions into multiple provider-specific configurations to maintain consistent AI behavior.
This project is a library of prompt-based skill definitions and functional extensions for AI assistants. It provides a system for automating product management tasks and strategic workflows by integrating specialized capabilities into coding agents and command line interfaces. The toolset utilizes a collection of standardized templates and guided processes to execute product management frameworks. These include tools for product discovery, market analysis, and business viability modeling, allowing users to chain analytical skills into end-to-end automated processes. The capability surface co
Provides standardized prompt-based definitions that establish the capabilities and operational scope of AI product management skills.
Openwork is an AI agent for desktop automation that uses large language models to execute browser tasks, manage local files, and automate desktop workflows. It operates on a local-first execution model, translating natural language prompts into sequences of tool calls to perform digital chores. The system functions as a framework for defining and saving repeatable sequences of actions as reusable skills. It integrates large language models with third-party services and local APIs to synchronize data and share files. The agent includes capabilities for headless browser automation to conduct r
Allows the definition of reusable configurations that store successful action sequences as named skills.
This project is a collection of patterns and configurations for deploying AI agents with specialized technical skills and personas. It provides a framework for agentic software engineering, defining standards for AI-driven development workflows and the management of modular technical capabilities. The system features a skill framework that activates technical guidelines based on prompt intent and a context management system that preserves project state using persistent plans and checklists across session resets. It employs a modular organization of guidelines to prevent context window overflo
Defines libraries of technical patterns for backend, frontend, and API testing to guide AI behavior.
vibe-vibe is an LLM agent engineering framework and toolchain optimizer designed for orchestrating multi-agent systems. It serves as a comprehensive guide and methodology for transforming conceptual ideas into deployed applications through agentic software engineering. The project focuses on the orchestration of specialized AI agent roles with defined collaboration boundaries and iterative feedback loops. It provides frameworks for toolchain optimization, including the selection and evaluation of protocols that extend model capabilities and the design of standardized tool interfaces. The sys
Allows the creation of natural language commands that define specific operational capabilities and skills for AI agents.
OpenViking is a multi-tenant context server and knowledge base administration system designed to provide AI agents with persistent long-term memory. It enables the indexing of diverse documents and codebases to support retrieval-augmented generation, allowing agents to recall past interactions, user preferences, and learned experiences across sessions. The project is distinguished by its use of a URI-based virtual filesystem to organize memories, resources, and skills. It implements a tiered context loading system that balances retrieval precision with token budgets by structuring data into a
Creates functional capabilities for agents using structured data, Markdown files, or converted MCP tool definitions.