What problem does it solve? Automated Slack replies often read as generic, overconfident, or context-blind, which erodes trust in team Q&A channels and partner-facing conversations. This Skill enforces a consistent set of composition rules so every bot answer is researched, calibrated, and moves the thread forward. ## Core Features & Use Cases - Context-first answering: Identify the asker, recall the partnership objective, and sanity-check the question against that context before composing. - Evidence-based debugging and tooling answers: Present 2-3 ranked hypotheses for debugging questions and search prior Slack threads before answering internal tooling questions. - Thread lifecycle management: Follow up on threads answered within the last 48 hours, accept expert corrections gracefully, hedge confidence based on source quality, and close with a concrete next step. - Use Case: A partner's engineer asks about an integration error in a shared Slack channel. The Skill guides the worker to check the partnership context, search prior threads for the same error, present ranked hypotheses with appropriate hedging, flag an adjacent issue like a leaked token, and end with a specific question that unblocks the partner. ## Quick Start Load this skill whenever the worker is about to compose a Slack answer or partner-facing quest reply so the response follows the answering quality rules.
