lark-agent-simple

Parse Markdown test files into compact JSON for Lark task hierarchies.

17|8|Updated Oct 21, 2025
One-click install
npx skills add https://github.com/Interstellar-code/claud-skills --skill lark-agent-simple
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lark-agent-simple
Source: https://github.com/Interstellar-code/claud-skills/tree/main/generic-claude-framework/skills/lark-agent-simple
Command: npx skills add https://github.com/Interstellar-code/claud-skills --skill lark-agent-simple

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill parses Markdown test files and generates a compact, data-only JSON structure for Lark task creation, reducing token overhead by bypassing heavy workflow generation.

Core Features & Use Cases

  • Parse Markdown test files into a minimal JSON data model (test overview, scenarios, and tasks) for direct processing.
  • Direct MCP execution: uses a slash-command workflow to create a 3-level Lark task hierarchy without generating an intermediate workflow.
  • Observability and reuse: returns a concise summary with source file reference, scenario and task counts, and timestamps, enabling easy auditing and reuse.
  • Use case: teams with existing Markdown test docs can rapidly spin up Lark tasks with minimal token usage and latency.

Quick Start

Run the lark-agent-simple skill against a Markdown file:

  • /lark-agent-simple examples/sample-test.md
  • /lark-agent-simple tests/manual/login-test.md --owner="QA Team" --due-date="2025-12-31"
  • /lark-agent-simple tests/manual/api-test.md --owner="Dev Team" --start-date="2025-10-20" --due-date="2025-11-03"

Frequently Asked Questions about lark-agent-simple

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create Lark tasks from Markdown test files efficiently?▼

You can create Lark tasks from Markdown test files by running a slash command that parses the document into a compact JSON structure and directly executes Lark MCP calls to build a 3-level task hierarchy with minimal token usage.

What is the best way to reduce token usage when importing test scenarios into Lark?▼

The best way to reduce token usage is bypassing heavy workflow generation. This Skill parses Markdown into a minimal data model and directly creates the test, scenario, and task hierarchy via MCP calls to eliminate intermediate processing overhead.

Can I assign owners and due dates when generating Lark tasks from Markdown?▼

Yes, you can assign owners and due dates when generating Lark tasks from Markdown. The Skill supports optional parameters like --owner, --due-date, and --start-date to configure the directly created 3-level Lark task hierarchy.

How does direct MCP execution work for Markdown task parsing?▼

Direct MCP execution for Markdown task parsing works by using a slash-command workflow to immediately invoke Lark MCP calls. This creates a 3-level task hierarchy without generating an intermediate workflow, significantly reducing token consumption.

Does Lark MCP integration support parsing existing Markdown test docs into a structured hierarchy?▼

Yes, Lark MCP integration supports parsing existing Markdown test docs. It extracts test overviews, scenarios, and tasks into a minimal JSON data model to create a 3-level Lark task hierarchy and returns a structured summary with source file references.