OPEN LINK ↗
// 45d agoTUTORIAL
Slack bot teaches agents your operating rhythm
This post describes a Slack bot that interviews a user across five layers, operating rhythms, decisions, dependencies, friction, and leverage, then turns those answers into config files that agents can actually use. The pitch is simple: better context reduces correction loops, saves tokens, and makes agent behavior closer to how a person really works. It is positioned as a lightweight way to generate reusable personalities and context packs for OpenClaw and other agent stacks.
// ANALYSIS
Strong idea, but the real value is less “agent personality” and more structured operator memory.
- –The pattern is useful when the task is repetitive, preference-heavy, or coordination-sensitive.
- –It will work best for long-lived agent setups where the same person keeps giving similar corrections.
- –The five-layer interview framework is the practical insight here, not the Slack transport.
- –Biggest risk: turning nuanced human judgment into brittle config if the prompts are too shallow.
- –Best fit: personal copilots, team assistants, and deployment-specific agent presets.
// TAGS
slack-context-interview-botagentcontext-engineeringagent-memoryworkflow-automationopenclawllm-ops
DISCOVERED
45d ago
2026-04-16
PUBLISHED
45d ago
2026-04-16
RELEVANCE
7/ 10
AUTHOR
Zolty