Decision Brief

Messy Slack threads, ticket comments, and scattered data exports make alignment impossible. This skill converts fragmented workplace context and technical evidence into a single, high-signal decision snapshot.

What it does

• Synthesizes context by extracting key arguments and data points from project history or ticket threads.
• Evaluates trade-offs by mapping specific options against their technical or business costs.
• Quantifies risk by calculating reversal costs and identifying critical gaps in the current evidence.
• Categorizes evidence by strictly separating source-backed facts from team assumptions.

How it works

1. Provide the agent with your project context, such as a ticket export or meeting notes.
2. Include any supporting evidence like analytics data, hex outputs, or technical logs.
3. The skill analyzes the inputs to identify the core decision and potential paths.
4. It generates a brief focused on trade-offs, risks, and concrete next steps.

Frameworks & tools

This skill is tool-agnostic and works with text-based data from Jira, Slack, GitHub Issues, and analytics platforms like Hex or Mixpanel.

Why this beats prompting it yourself

Generic prompts often produce summaries that bury the lead or ignore the cost of being wrong. This skill enforces a specific structure that surfaces reversal costs and data gaps, ensuring you don't miss the hidden risks in a recommendation.

Use cases

• Architecture decisions where technical trade-offs between two frameworks need to be visible.
• Product prioritization when competing features have conflicting data signals.
• Incident post-mortems to decide on long-term infrastructure changes after a failure.
• Technical debt triage to determine if a refactor is worth the immediate velocity hit.

Known limitations

Does not perform live data fetching or execute external analytics queries. It relies entirely on the text and data provided in the prompt context.