By is908
Feishu/Lark channel plugin for Claude Code — receive messages via WebSocket, reply with text/images/files, manage cross-session memory
npx claudepluginhub is908/claude-lark-plugin --plugin larkConfigure the claude-lark-plugin by managing ~/.claude/channels/lark/.env. Use when the user asks to configure, setup, or change Lark/Feishu settings or credentials.
Manage scheduled jobs (cronjobs) — create, list, pause, resume, and delete recurring tasks. Invokes the plugin's MCP tools from the Claude Code terminal; defaults to a redacted view to reduce incidental exposure (screen share, shoulder surfing).
Memory compression system for Claude Code - persist context across sessions
Standalone image generation plugin using Nano Banana MCP server. Generates and edits images, icons, diagrams, patterns, and visual assets via Gemini image models. No Gemini CLI dependency required.
Streamline people operations — recruiting, onboarding, performance reviews, compensation analysis, and policy guidance. Maintain compliance and keep your team running smoothly.
Admin access level
Server config contains admin-level keywords
Share bugs, ideas, or general feedback.
Chat with Claude Code in real time through Feishu (Lark). Local-file memory, scheduled jobs, rich media support.
Feishu User ──> Feishu Open Platform ──WebSocket──> claude-lark-plugin (MCP Server) ──> Claude Code
<── reply / edit / react ──<
The plugin connects to Feishu via the Lark SDK WebSocket client, receives messages in real time, enriches them with memory context, and forwards them to Claude Code as an MCP channel. Claude's responses are sent back through the Feishu IM API.
format='card' to force card, format='text' to force plain. Optional footer footnote supported~/.claude/channels/lark/memories/save_memory, create_job, list_jobs, update_job, delete_job, what_do_you_know, forget_memory) resolve the calling user from the authenticated Feishu event stream, not from tool arguments — socially-engineered prompts cannot act on behalf of another userwhat_do_you_know lists what the bot has stored about the caller (filtered by current-chat visibility); forget_memory removes a specific line by hash. Optional promote_to_rule feeds corrections into privacy-rules.md — a self-learning loop that makes future misclassifications less likely~/.claude/channels/lark/audit.log records every sensitive-tool invocation (timestamp / tool / caller / outcome / redacted args) so the operator can retrospectively inspect what was accessed on their machine/lark:jobs hides prompt bodies by default; verbose opt-in is required. Destructive operations require interactive confirmationpublic.md (visible to anyone who @mentions the user) and private.md (owner-only). Private-chat preferences no longer leak into groups via @mention injectionprivacy-rules.md. User-editable privacy-rules.md covers personal/org-specific cases; LLM handles the nuance. parseTieredProfile applies an L1 safety net over LLM output so misclassified credentials get forced to privateprivacy-rules.md before (or during) the upgrade, ## Always private phrases are applied as case-insensitive substring matches during migration — org-specific codenames, client names, and people mentions get routed to private.md even though L1 alone wouldn't flag themOwn this plugin?
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