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lark-event

Streams Feishu/Lark real-time events as NDJSON via lark-cli with bounded run options.

35|5|Updated May 7, 2026
Search Tags:#real-time#streaming#feishu#lark#ndjson#event-listening
One-click install
npx skills add https://github.com/yokingma/weclaws --skill lark-event-yokingma
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lark-event
Source: https://github.com/yokingma/weclaws/tree/main/resources/skills/managed/lark-event
Command: npx skills add https://github.com/yokingma/weclaws --skill lark-event-yokingma

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill enables AI agents to subscribe to Feishu/Lark real-time events and stream them as NDJSON, enabling immediate processing and routing to downstream tasks.

Core Features & Use Cases

  • Real-time event subscriptions for multiple EventKeys
  • NDJSON streaming with structured event payloads for downstream AI tooling
  • Subprocess-friendly long-running listeners with bounded run options (--max-events, --timeout)
  • Authentication and safety guidance as documented in the prerequisite SKILL.md

Quick Start

Start a listening session for a key such as im.message.receive_v1 and read the NDJSON stream from stdout.

Frequently Asked Questions about lark-event

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

FAQPage Schema
How do I listen to real-time Feishu events and stream them as NDJSON?▼

Real-time Feishu event listening streams events as NDJSON via lark-cli. You subscribe to specific EventKeys and read structured event payloads directly from stdout for immediate downstream processing.

Can I limit the duration of a Lark event subscription session?▼

Yes, Lark event listening supports bounded runs using the --max-events and --timeout flags. These options prevent long-running subprocesses from executing indefinitely during event subscriptions.

What is the NDJSON format used for streaming Feishu messages?▼

NDJSON streams structured Feishu event payloads line by line as newline-delimited JSON. This format enables subprocess-friendly consumption, allowing AI agents to process each real-time event independently.

Does lark-cli support subscribing to multiple EventKeys at once?▼

Yes, real-time Lark event listening supports event subscriptions across multiple EventKeys simultaneously. This allows bots and data pipelines to aggregate various Feishu event streams into one NDJSON output.

Do I need lark-cli to capture real-time Lark events for an AI agent?▼

Yes, capturing real-time Lark events as NDJSON requires the lark-cli binary. It provides the necessary subprocess-friendly interface and follows documented safety and authentication guidance for agents.

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