decision-advisor

Evaluate progress, risks, and options at workflow checkpoints.

Updated Jan 23, 2026
One-click install
npx skills add https://github.com/cdman28/antigravity-devkit --skill decision-advisor
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: decision-advisor
Source: https://github.com/cdman28/antigravity-devkit/tree/main/.agent/skills/decision-advisor
Command: npx skills add https://github.com/cdman28/antigravity-devkit --skill decision-advisor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

The Decision Advisor provides structured decision support at critical workflow checkpoints to reduce uncertainty and misalignment among stakeholders.

Core Features & Use Cases

  • Evaluate progress, risks, and options at checkpoints to surface actionable insights.
  • Propose concrete recommendations for next steps, including trade-offs and feasibility.
  • Integrate with checkpoint.sh to generate consistent, auditable decision outputs for reviews.

Quick Start

Provide an AI-driven decision analysis at the current checkpoint by evaluating progress, risks, and recommended actions.

Frequently Asked Questions about decision-advisor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate workflow risks and progress at project checkpoints?

Evaluating workflow risks at checkpoints requires structured decision support to assess completion status, identify risks, and determine next step feasibility. This approach reduces uncertainty by generating consistent, auditable reports for human review and stakeholder alignment.

What is the best way to generate an auditable decision report for a project review?

The best way to generate an auditable decision report is to apply AI-driven analysis at workflow checkpoints, evaluating progress and trade-offs. This outputs a structured, consistent report that satisfies review requirements and surfaces actionable recommendations for stakeholders.

Can I use AI decision support for both planning and execution phases?

Yes, AI decision support applies across planning, execution, and evaluation scenarios. It weighs progress, risks, and trade-offs at each checkpoint to determine the feasibility of next steps, ensuring consistent decision-making throughout the entire workflow lifecycle.

How do I reduce stakeholder misalignment when determining the feasibility of next steps?

Reducing stakeholder misalignment involves providing structured decision support at critical checkpoints to surface actionable insights. By proposing concrete recommendations with clear trade-offs and feasibility assessments, stakeholders can review consistent, structured outputs to align on next steps.

Does checkpoint decision support work without external dependencies?

Yes, checkpoint decision support can operate without external dependencies by using internal scripts to evaluate project status. It integrates with checkpoint workflows to generate consistent, structured outputs, requiring no additional frameworks to assess risks and recommend actions.