Plugins

1 plugin

Adobe For Creativity

Brings together Adobe Creative Cloud tools for images, vectors, design, and video. Edit multiple assets at once, adapt for different platforms, and complete multi-step creative workflows for polished results.

7 skills · plugin

Results for “r-multiple”

13 skills

Watches conversations continuously and compresses them into prioritized notes, consolidating and recovering missed sessions with multiple redundancy layers.

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Converts documentation websites, GitHub repositories, and PDF files into AI-ready skills for multiple LLM platforms.

Build voice AI agents with natural conversations, multiple LLM providers, custom tools, and easy web embedding.

Coordinates multiple AI agents through a shared room: shared memory, task claiming, conflict detection, and destination tracking.

orchestra-research

Build LLM-powered applications with agents, chains, and RAG using a framework that supports multiple providers and 500+ integrations.

10.4k · bundle

More results

Spawns multiple parallel AI agents that compete on the same task using isolated git worktrees, evaluates results, and merges the best solution.

20.4k · bundle

Automates scientific research workflows from data analysis to publication, orchestrating multiple agents for hypothesis generation, methodology development, computational experiments, and LaTeX paper writing.

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.

Edge Signal Aggregator

Aggregate and rank signals from multiple edge-finding skills into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

Configure and optimize .faf files, MCP servers, and bi-directional sync for AI context across multiple platforms, with championship scoring to achieve 85%+ AI-readiness.

sakamoto-family-smile

Orchestrates parallel AI agent sessions using dmux, a tmux pane manager, with patterns for research, implementation, testing, and code review across multiple harnesses.

Automatically measures whether coding agents follow skills, rules, or agent definitions by generating scenarios at multiple prompt strictness levels, running agents, classifying tool calls, and reporting compliance rates with full timelines.