Agent Workflows · By shareAI-lab

Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.

shareAI-lab/learn-claude-code

Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.

shareAI-lab/learn-claude-code

Guides writing a new agent skill or improving an existing one, covering how to keep it concise, how much freedom to give the agent and how to lay out bundled resources.

shareAI-lab/Kode-CLI

Evaluates the design quality of an agent skill against official specifications and patterns from existing examples, scoring it and suggesting improvements.

shareAI-lab/Kode-CLI

Gives an agent a set of working rules for any development task: understand first, surface decisions, verify results, and load deeper reference files per scenario.

shareAI-lab/Kode-CLI

Helps design and build AI agents for any domain around a minimal loop of capabilities, knowledge and context, adding planning or subagents only when needed.

shareAI-lab/lab-skills

Lets the Kode agent manage its own features, such as LSP, statusline, output styles and plugins, by running its slash commands for you instead of listing install steps.

shareAI-lab/Kode-CLI

Diagnoses why Kode's LSP tool returns nothing and fixes it through plugin .lsp.json files and slash commands, then verifies with a real LSP call.

shareAI-lab/Kode-CLI

Guide to designing and building MCP servers: tool, resource and prompt design for agent usability, with TypeScript or Python implementation workflows.

shareAI-lab/Kode-CLI

Troubleshoots Kode permission prompts and denials, including allowlists, command-level tool limits, dontAsk mode and subagent inheritance, without loosening permissions.

shareAI-lab/Kode-CLI

Evaluate Agent Skill design quality with an opinionated, practice-derived rubric informed by public specifications and examples.

shareAI-lab/lab-skills