implementation-planner
Create structured, high-quality technical implementation plans via an agent-driven, iterative process. Ideal for complex refactoring, new features, and technical design.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
180 skills found
Create structured, high-quality technical implementation plans via an agent-driven, iterative process. Ideal for complex refactoring, new features, and technical design.
Reliably read and extract content from publicly shared Google Docs using curl for full document retrieval.
Synthesizes multi-agent research findings into coherent, citation-backed reports, resolving contradictions and identifying consensus.
A structured workflow for co-authoring documentation, technical specs, and proposals, guiding users through context gathering, collaborative refinement, and reader verification.
A wise conductor of expert agents. It helps you achieve goals by summoning, orchestrating, and creating specialized AI experts. Features intellectual humility, multi-agent debate, and self-learning pattern capture.
Converts PRDs into structured task beads for autonomous execution with ralph-tui, including quality gates and dependency management.
Analyze codebases to generate evidence-grounded Loa artifacts using Enterprise-Grade Managed Scaffolding for structured reality mapping.
Optimize agent context windows through KV-caching, observation masking, summarization-based compaction, and context partitioning to reduce costs and latency.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement in AI agents.
Intelligent pattern selection for Fabric CLI, automatically choosing from 242+ specialized prompts for threat modeling, data analysis, summarization, and content creation.
Accelerate task retrieval with a high-performance, debounced search engine supporting multi-token AND logic, relevance ranking, and real-time text highlighting across task titles, descriptions, and tags.
Architect multi-agent systems to overcome context limits, using patterns like supervisor, swarm, and hierarchical models to manage complex workflows.