AI Agent Skills Marketplace
Showing 6 of 6 results
mesh flow
What it does Mesh Flow replaces fragile, implicit prompt-chaining with a robust, artifact-driven DAG (Directed Acyclic Graph) orchestration system. By defining your agent workflows in a structured project.yaml, you move logic out of the prompt and into a compile-time validated system. It enforces hard gates—such as human approval or dependency verification—that the AI cannot bypass or hallucinate past. Why use this skill Standard agentic workflows often fail because the LLM decides to skip steps or "forgets" requirements. Mesh Flow treats your agentic workflow like a CI/CD pipeline. It features a Compile-then-Run architecture that validates your topology for cycles and missing artifacts before single token is generated. This ensures 100% predictable execution paths, explicit failure states (failed, blocked, rejected), and absolute control over recovery paths. Supported tools YAML-based workflow configurations Standardized Adapter Interfaces for cross-skill communication Mermaid DAG visualization for debugging Zod-backed schema validation CLI tools for compilation and execution (mesh compile, mesh run) Output structure The skill produces a normalized execution plan and a detailed execution trace. Every node execution returns a standardized status, a list of produced artifacts, and comprehensive metadata including tool calls and verification reasoning. Use Cases Build multi-step agent pipelines with hard verification gates Enforce human-in-the-loop approval before sensitive code deployments Visualize complex agent task dependencies using Mermaid DAGs Standardize artifact sharing between disparate AI skills and agents
harness-engineering
The Harness Engineering skill implements a structured methodology for agent orchestration. It allows you to build sophisticated control loops using a multi-role architecture: Planner: Defines contracts and stop rules. Executor: Performs bounded actions. Verifier: Validates results against evidence. Critic/Recovery: Identifies regressions and manages error state.
prompt-engineer-pro
Prompt Engineer Pro is a production-grade system for teams shipping AI workflows into business-critical environments. Unlike basic prompting assistants, this skill treats prompts as software assets that require governance, audit trails, and regression testing. It helps you build, audit, and harden prompt stacks for complex tasks involving multiple models, tool-use agents, and structured data extraction.
SEO & AEO Auditor
Deep SEO + AEO auditor that transforms any codebase into a search and AI visibility powerhouse. Runs 102 checks across 8 categories: technical SEO, content SEO, structured data, AEO readiness, AI crawler strategy, llms.txt, performance (Lighthouse + PageSpeed Insights), and content gap analysis. Generates schema markup, llms.txt, robots.txt AI directives, and FAQ patterns. Scores your site 0-100, then fixes what you approve. Built for developers using Claude Code.
prompt-engineer-lite
Building high-performance LLM applications requires more than just basic instructions. This skill equips your AI agent with a sophisticated framework for designing, debugging, and optimizing prompts across any major model provider. It solves the common problems of model drift, parsing failures, and hallucination by implementing industry-standard engineering patterns.