Skill UI

Automate Humanloop workflows through Composio’s Humanloop toolkit by calling Rube MCP endpoints; always discover tools first, verify the Humanloop connection, then execute schema-compliant arguments to trigger operations such as bulk runs or schema inspection.

Framework for building stateful, multi-actor AI applications with LangGraph, covering graph construction, reducers, persistence, human-in-the-loop patterns, and ReAct agents suitable for Python production systems.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

Guides founders and GTM teams on designing and deploying AI SDR systems, automating qualification, routing signals into actions, and orchestrating human-in-the-loop handoffs across inbound/outbound motions and CRM stacks.

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

This skill provides a systematic, four-phase workflow for diagnosing and recovering from AI agent failures. It guides the agent to first capture the failure state, diagnose the root cause (e.g., loop, context overflow, state drift), apply minimal containment recovery actions, and finally generate a detailed introspection report, minimizing the need for human intervention.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.

Implement robust human-in-the-loop (HITL) approval gates for sophisticated LangGraph workflows. This skill enables the graph to pause execution before critical, irreversible actions (like sending emails or calling tools). It manages complex state serialization, provides clear resume semantics using `Command(resume=...)`, and integrates with external UIs (e.g., Slack) to await human decision, ensuring safe and controlled workflow execution.

This guide provides comprehensive instructions and API endpoints for developing localized integrations with Workhuman Social Recognition and rewards system. It covers core concepts such as OAuth 2.0 authentication, peer-to-peer recognition workflows, configurable awards, and bidirectional synchronization with HRIS platforms like Workday and SAP. Ideal for developers building custom recognition or reward management solutions.

This privileged skill lands self-modification patches approved by users after /meta-optimize. It enforces a privilege boundary by acting as the only skill permitted to mutate the skill corpus. It runs a fresh cross-model jury at landing time to verify patches before applying changes, ensuring safety and preventing self-modification loops. Requires explicit human invocation.

A human-in-the-loop studio for iteratively tuning AI outputs or reviewing complex media/prose. It allows users to directly manipulate visual parameters (color, typography, layout) or annotate documents in real-time, moving beyond static comparison grids or vague prompts. Perfect for designers and content creators who need hands-on, visual feedback.