fenghou-qimen

What problem does it solve?

It solves the problem of uncertain task prioritization by turning multiple task attributes into a consistent ranked plan, then validating schedule robustness with Monte Carlo simulation.

Core Features & Use Cases

• Nine-Dimension Scoring & Composite Ranking: Combines urgency, importance, dependency complexity, resource match, and additional readiness/team factors into a single composite score.
• Eight-Gate Planning (八门映射): Maps scores into actionable gates (开门/生门/景门/杜门/死门) to produce recommended next steps and whether to freeze resources.
• Monte Carlo Robustness Evaluation: Simulates time variance across tasks to estimate on-time completion rate and output a robustness assessment with buffer guidance.
• Dynamic Weight Templates: Supports configurable templates (e.g., urgency_priority, quality_priority, resource_limited, team_driven) to match different operational scenarios.

Use case example: Given a JSON list of engineering tasks with deadlines, dependencies, and estimated durations, generate a ranked execution plan and a risk-aware buffer recommendation for which work to start, defer, or stop.

Quick Start

Use the fenghou-qimen skill to produce a priority plan for tasks.json by invoking the priority engine with the appropriate weight template, e.g., urgency_priority.