slack-context-memory
Conversation summarization and context compaction for Slack channels. Reduces context window usage by 70-99% while preserving key information through semantic summaries.
Best use case
slack-context-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Conversation summarization and context compaction for Slack channels. Reduces context window usage by 70-99% while preserving key information through semantic summaries.
Teams using slack-context-memory should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/slack-context-memory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How slack-context-memory Compares
| Feature / Agent | slack-context-memory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Conversation summarization and context compaction for Slack channels. Reduces context window usage by 70-99% while preserving key information through semantic summaries.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Slack Context Memory Compress Slack conversation history into searchable summaries for context-efficient sessions. ## Problem Solved Clawdbot sessions lose context as conversation history grows. This skill: 1. **Detects conversation boundaries** in Slack message history 2. **Generates structured summaries** (TL;DR, decisions, topics, outcome) 3. **Stores summaries with embeddings** for semantic search 4. **Compacts context** - replace 1000s of messages with a few summaries 5. **Enables semantic retrieval** - find relevant past discussions ## Quick Start ```bash # Setup database schema cd /home/david/clawd/scripts/slack-context-memory node setup-schema.js # View compacted context for a channel node context-compactor.js C0ABGHA7CBE # Compare original vs compacted size node context-compactor.js C0ABGHA7CBE --compare # Search for relevant conversations node context-compactor.js --query "email newsletter filtering" ``` ## Token Savings | Channel | Original | Compacted | Savings | |---------|----------|-----------|---------| | #accounts (1000 msgs) | 112K tokens | 951 tokens | **99.2%** | | #homeassistant (50 msgs) | 3.1K tokens | 911 tokens | **70.8%** | ## Components ### Conversation Detection ```bash node detect-conversations.js <channel_id> node detect-conversations.js --all ``` ### Context Compaction ```bash node context-compactor.js <channel_id> --recent 20 node context-compactor.js <channel_id> --compare node context-compactor.js --query "search term" ``` ### Search ```bash node search-conversations.js semantic "query" node search-conversations.js text "query" node search-conversations.js recent --limit 10 ``` ## Requirements - PostgreSQL database with pgvector - Node.js 18+ - Slack message history in database ## Database Schema The `conversation_summaries` table stores: - `tldr` - 1-2 sentence summary - `full_summary` - Detailed summary - `key_decisions` - Array of decisions made - `topics` - Array of topics discussed - `outcome` - resolved/ongoing/needs-follow-up - `embedding` - Vector for semantic search (1024-dim) --- Built for Clawdbot 🦞 | 2026-01-28
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