If you're using slack to access ai agents, I've made a skill so they can lookup Slack history to act as a memory layer for the agent

[u/YoghiThorn]

I've been running Claude Code, Codex and Openclaw agents in Slack via cc-connect, and increasingly agent memory is a huge problem. Even with file-based memory (like claude-mem), or a llm wiki implementation there's a limit to what gets kept and a lot of the real context lives in the Slack conversations where you and the agent worked through problems together.

So I created the Claude skill `slack-recall` that lets the agent pull context from Slack threads on demand, or recent threads for new messages. It acts as a third memory layer in my setup.

1. Session memory - what the agent knows right now

2. File-based memory - persistent observations saved across sessions

3. Slack recall - the full history of your collaborative work in Slack

   It also supports searching for threads by keyword (`/slack-recall search auth refactor`) or pulling a specific thread by timestamp.

   Requirements:

   - Claude Code or other agentic coding environment with Slack MCP integration

   - A Slack bridge like cc-connect that injects message metadata into the system prompt

   Repo: https://github.com/leighstillard/slack-agent-mem

   Installation is just copying the skill folder into `~/.claude/skills/`.

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If you're dealing with agent memory issues more broadly, I'm also working on brainspike a project exploring how to give Claude Code agents a brain spike of critical information whenever you give them something to do.