Plugins

5 plugins

Results for “pipelines”

18 skills
More results
github
Eval Driven Dev
Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.
36.2k · bundle
samuraigpt
Muapi Workflow
Build, run, and visualize multi-step AI generation workflows by chaining image, video, and audio nodes into automated pipelines.
3.7k · bundle
lingxling
AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
253
jorcan
AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
0 · bundle
phoroth
AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
3
antigravity
Evaluation
Build evaluation frameworks for agent systems, covering rubric design, test set creation, and automated evaluation pipelines.
42.4k
lingxling
Daily
Reference for building real-time voice and multimodal AI applications with Pipecat, covering pipelines, speech services, LLM integration, and transports.
253
nimoqup046-collab
Daily
Reference for building real-time voice and multimodal AI agents with Pipecat, covering pipelines, speech services, LLMs, transports, and deployment.
2
affaan-m
Ecc Recipes
Maps a described workflow to the right ECC command-group with run-order and stop condition, and browses all command-group recipe families.
226k
joshuashepherd
Create Agent
Scaffold and develop AI agents using OpenAI Agents SDK patterns, covering agent definition, tools, guardrails, handoffs, context, RAG pipelines, streaming, API routes, testing, and debugging.
1
sakamoto-family-smile
Autonomous Loops
Patterns and architectures for running Claude Code autonomously in loops, from simple sequential pipelines to RFC-driven multi-agent DAG systems.
0
inference-sh
AI Automation Workflows
Build automated AI workflows combining multiple models and services for batch processing, scheduled tasks, event-driven pipelines, and agent loops using the inference.sh CLI.
584