Agent-Slack: The Token-Efficient CLI That Speaks LLM

Building AI agents that interact with Slack? You’ve probably discovered the hard way that the Slack API returns massive JSON blobs that burn through your context window. Agent-Slack takes a different approach: ship a CLI designed from the ground up for LLM consumption.

The Core Insight

Agent-Slack’s guiding principle is token efficiency. Every design decision optimizes for minimal context consumption:

• Compact JSON output with empty/null fields pruned automatically
• No redundant data structures or verbose error messages
• File attachments auto-downloaded to local paths (so agents can read content directly)
• Canvas documents converted to clean Markdown

This isn’t just nice-to-have—it’s the difference between an agent that works reliably and one that runs out of context mid-task.

The CLI provides a complete Slack toolkit:

# Read messages and threads
agent-slack message get "https://workspace.slack.com/archives/C123/p170000..."
agent-slack message list "#general" --thread-ts "1770165109.000001"

# Search with filters
agent-slack search all "smoke tests failed" --channel "#alerts" --after 2026-01-01

# Write back
agent-slack message send "#general" "Build complete ✅"
agent-slack message react add "#general" "👍" --ts "1770165109.628379"

# Fetch canvases as Markdown
agent-slack canvas get "https://workspace.slack.com/docs/T123/F456"

Why This Matters

There’s a design pattern emerging in AI agent tooling: agent-native interfaces. Traditional CLIs were designed for human operators who can interpret verbose output, handle edge cases, and provide interactive input. Agent-native tools flip this:

1. Structured output that LLMs can parse reliably (JSON, not human-readable prose)
2. Minimal tokens per operation (pruned fields, no decorative formatting)
3. Complete operations that don’t require follow-up questions
4. Deterministic behavior that agents can predict and plan around

Agent-Slack also solves the auth problem elegantly. On macOS, it reads credentials directly from Slack Desktop’s local data—zero configuration required. Fall back to Chrome extraction if needed, or just set environment variables for programmatic use.

The message get vs message list distinction shows thoughtful API design:
– get fetches a single message plus thread metadata (reply count, participants)—enough to decide if you need more
– list fetches the full thread when you actually need to read the conversation

This two-step pattern saves tokens on the common case (checking a notification) while enabling the complete case (reading a full discussion).

Key Takeaways

• Zero-config auth on macOS: reads from Slack Desktop automatically
• Token-efficient output: empty/null fields pruned, minimal JSON
• File handling: attachments auto-downloaded to ~/.agent-slack/tmp/downloads/
• Canvas support: Slack canvases converted to Markdown
• Multi-workspace: --workspace flag for users in multiple orgs
• Search with filters: channel, user, date range, content type
• Ships as agent skill: Compatible with Claude Code, Codex, Cursor via npx skills add stablyai/agent-slack

Looking Ahead

Agent-Slack hints at a future where every developer tool ships an “agent mode”—a parallel interface designed for LLM consumption rather than human operators. We’re already seeing this with --json flags becoming standard, but dedicated agent-native tools take it further.

The skill distribution via skills.sh is worth noting. As coding agents become the primary interface for many developers, having a standardized way to extend agent capabilities will matter more. “Install this skill” may become as common as “pip install this package.”

For teams building AI agents that need Slack integration, this is the tool to reach for. The token savings compound across every interaction, and the auth story just works.

Based on analysis of GitHub – stablyai/agent-slack: Slack automation CLI for AI agents