agent-tests
Agent-runnable e2e tests that validate workflows with real commands
Find an Agent Skill for your task. Read the guide and copy the install command.
Updated 11-08 00:04
25,878 results
Agent-runnable e2e tests that validate workflows with real commands
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express…
Install and connect Zapier and route the person to the right surface (MCP, SDK, or CLI). Use for typed or spoken requests to install, add, connect, or set up…
Implement the agent-help convention in a CLI. Use when building or modifying a CLI that agents will invoke: add --agent-help for AHF-format invocation help and…
Audit a repository's preparedness for autonomous AI coding agents - evaluates 86 checks spanning code quality, testing, documentation, security, and…
Use Probe to interact with Nexus via CLI commands. Agents use probe for Zenon Network wallet creation, auth, ideas, projects, tasks, claims, PR/issue linking,…
Use this skill when a task needs MCP tools, internal app connectors, or "use mcp" behavior. Search and inspect mcporter servers/tools first, then call the…
Broadcast a message to multiple OpenClaw group sessions simultaneously. Use for cross-agent coordination, alerts, and announcements.
Generate CLAUDE.md and AGENTS.md for a project; after analyzing the project structure, output an agent configuration file that is command-first and partitioned by task. Use when users want to create or improve CLAUDE.md, AGENTS.md, or agent instruction files for their…
Skill quality review and optimization. Use when creating, modifying, reviewing, or compressing Agent Skill, SKILL.md, skill descriptions, trigger boundaries, STOP conditions, output contracts, progressive disclosure, trigger eval, or task eval; do not use for ordinary business documents or tasks that only consume existing skills.
Use when operating the ZETIC Melange platform for Qualcomm-focused on-device AI work with the `melange-qcom` CLI or MCP server: model upload/import and…
Use when converting PRD or issue prose into KLL requirements, running the intent compiler plan, or reverse-interviewing a human to resolve requirement…
An Agent Skill builder based on the 5 major design patterns of Google ADK. Used to create high-quality Skills from scratch, or to refactor/review existing Skills. Trigger scenarios: - "Create a skill" - "Help me write a SKILL.md" - "How can this skill be improved" - "How to design an agent skill" -…
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and…
Used when choosing a multi-workflow orchestration mode. Suitable for cross-workflow collaborative tasks, deciding whether tasks are serial/parallel/dynamically planned. Prefer Microsoft Azure's four Agent orchestration patterns (sequential/concurrent/handoff/dynamic).
Use when decomposing project goals into executable work packages. Suitable for project kickoff, Sprint planning, and breaking requirements into tasks. Prefer WBS (Work Breakdown Structure) to decompose layer by layer down to a granularity of 5~60 minutes.
Zoom AI Services Decisions for typed predicates, constrained classification, ordered scoring, probability-aware routing, and inline image comparison. Use when…
Zoom AI Services Voice Agent V2 for real-time microphone conversations over WebSocket, including VAD, PCM audio, speech-to-speech or text responses,…
Reference skill for Zoom Virtual Agent. Use after routing to a virtual-agent workflow when implementing web embeds, Android or iOS wrapper integrations,…
Design a Zoom MCP workflow for Claude. Use when deciding whether Zoom MCP fits a task, when planning tool-based AI workflows, or when separating MCP…
ChatGPT use cases and prompts for executives | Part of naiba-openai-work-assistant
Cornell Notes infographic prompt generator. Enter any study topic and automatically generate prompts that can be used directly with ChatGPT/DALL-E to create high-quality Cornell Notes-style study infographics.
Local semantic memory with vector search and Transformers.js. Store, search, and recall conversation context using embeddings (fully local, no API keys).
Fully automated self-improving loop — takes a project prompt, designs a team, runs the benchmark, analyzes results, optimizes framework code, rebuilds, and…