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polym-im-digest

Generate a daily SA intelligence digest from Lark IM conversations.

8|Updated May 13, 2026
Search Tags:#knowledge base#priority queue#customer intelligence#lark im#feishu doc#product taxonomy
One-click install
npx skills add https://github.com/byteplus-sa/polym --skill polym-im-digest
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: polym-im-digest
Source: https://github.com/byteplus-sa/polym/tree/main/skills/polym-im-digest
Command: npx skills add https://github.com/byteplus-sa/polym --skill polym-im-digest

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lark-cli, and includes references (resource) components.

What problem does it solve?

This Skill turns yesterday’s Lark IM conversations into a prioritized SA intelligence digest, so you can quickly spot risks, customer feedback, feature asks, and business updates without manually reading chat history.

Core Features & Use Cases

  • Yesterday IM collection & filtering: Pulls yesterday’s Lark P2P and joined group messages via lark-cli, then applies a local-only blacklist to skip noisy or long-lived excluded chats.
  • Priority-first analysis for SA actionability: Extracts the 12 signal dimensions and renders a ModelArk / MaaS-focused digest with P0/P1 owner/deadline tracking and evidence.
  • Deliverables & knowledge base updates: Creates a standalone Feishu digest document (XML primary with Markdown fallback) and writes the resulting knowledge to the local wiki and the Lark SA Wiki via polym-sa-wiki workflow.

Quick Start

Ask the assistant to organize yesterday's messages into an IM digest and save it to the knowledge base.

Frequently Asked Questions about polym-im-digest

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

FAQPage Schema
How do I generate a daily Lark IM digest from yesterday's messages?▼

To generate a daily Lark IM digest, this Skill fetches yesterday's P2P and group messages via lark-cli, extracts 12 signal dimensions, prioritizes results, and creates a structured Feishu report.

What is the best way to summarize Lark IM conversations into a Feishu report?▼

Summarizing Lark IM conversations into a Feishu report involves pulling yesterday's messages, applying local blacklist filtering, and extracting prioritized SA intelligence signals like risks, feedback, and feature asks.

Does this IM digest tool work with both Lark P2P chats and group messages?▼

Yes, this IM digest tool works with both Lark P2P threads and joined group chats, pulling yesterday's messages via lark-cli and applying local blacklist filtering to skip noisy or excluded conversations.

Do I need lark-cli to extract customer intelligence from Feishu IM history?▼

Yes, you need lark-cli installed to fetch Lark IM messages and create the Feishu digest document, while an optional valid local wiki root is required for knowledge base persistence.

How does the daily IM digest handle priority tracking for customer intelligence?▼

The daily IM digest handles priority tracking by extracting 12 signal dimensions from Lark IM conversations and rendering a ModelArk/MaaS-focused report with P0/P1 owner and deadline tracking.

Can I write the Lark IM digest directly to a local wiki knowledge base?▼

Yes, you can write the Lark IM digest to a local wiki and the Lark SA Wiki via the polym-sa-wiki workflow, ensuring content parity between Markdown and Feishu XML formats.

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