What problem does it solve? Business chat history is hard to turn into a defensible decision picture: concerns get lost, approval dependencies stay implicit, and AI summaries guess at roles and authority. This Skill turns already-collected, normalized conversation messages into an evidence-bound Decision Topology and Stakeholder Influence Map so you can choose the next business action with traceable support. ## Core Features & Use Cases - Decision Influence Signal extraction: Detects direction changes, blocking signals, approval dependencies, agenda setting, escalation targets, and execution ownership, each bound to message IDs and source locators. - Evidence discipline: Separates direct evidence, inferred interpretation, and missing knowledge; never derives formal roles from chat behavior; records Unknowns instead of guessing. - Governed output: Produces a structured Markdown report with handling classification, quality gate results, human review items, and knowledge promotion candidates. - Use Case: Given exported Teams or Slack messages about a production release decision, identify who is blocking the decision, which concern must be resolved first, and who to brief before the next meeting. ## Quick Start Analyze the attached normalized conversation messages about the release decision and produce a Decision Topology Analysis with stakeholder influence map and recommended next actions.
