project priority decision planner

    1

    Rank competing projects against strategic goals and capacity using weighted scoring and opportunity cost analysis.

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    project priority decision planner

    Example session with this skill installed

    We have 4 projects for Q3 but only 400 dev-hours. Goals:

    1. Reduce churn, 2. AI integration. Projects: A (Churn dashboard, 100h, high value), B (Legacy refactor, 150h, low alignment), C (LLM feature, 200h, high alignment), D (API cleanup, 80h, moderate value). Rank them.
    • Read your context and instructions
    • Compiled the project priority decision

    I have analyzed your Q3 portfolio against your available 400 dev-hours. Applying a 0.8 utilization factor, your effective capacity is 320 hours. Total demand is 530 hours, resulting in a 210-hour overcommitment gap.

    Project Priority Decision — Q3

    #ProjectValueUrgencyAlignEffortRiskScoreDecision
    1Churn Dashboard545444.55Pursue
    2LLM Feature435223.60Pursue
    3API Cleanup322442.70Delay
    4Legacy Refactor221231.85Stop

    Weights: Value 0.30, Urgency 0.25, Alignment 0.25, Effort 0.10, Risk 0.10

    Pursue

    Churn Dashboard — Primary driver for Goal 1. High value with manageable effort. First action: Finalize schema for churn event tracking. Owner: Data Team.

    LLM Feature — Directly serves Goal 2. High strategic alignment despite significant effort and risk. First action: Select base model and set up prompt evaluation framework. Owner: AI Engineering.

    Delay

    API Cleanup — Moderate value but lacks urgency compared to churn and AI goals. Revisit: End of Q3 or if LLM feature finishes early.

    Stop

    Legacy Refactor — Lowest alignment with current goals and high effort. Opportunity cost of continuing: Completing the LLM feature would be impossible this quarter.

    Opportunity Cost

    • Pursuing the

    Churn Dashboard requires delaying the

    API Cleanup, pushing back third-party integration improvements by 3 months.

    • Pursuing the

    LLM Feature means we must

    Stop the

    Legacy Refactor, accepting technical debt in the auth module to hit the AI shipping window.

    Execution Roadmap

    Week 1–4

    • Churn Dashboard: Implementation of telemetry and initial visualization.
    • LLM Feature: RAG architecture design and vector store setup.

    Week 5–8

    • Churn Dashboard: Beta testing with CS team.
    • LLM Feature: Integration into the main application UI.

    Next Period

    • API Cleanup: Re-evaluate based on new API usage patterns from the LLM feature.

    Next steps

    1. Review the "Stop" decision on Legacy Refactor with the Lead Architect.
    2. Confirm the 150h estimate for the LLM Feature with the AI team.
    3. Schedule the Churn Dashboard kickoff for Monday.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Rank backlogs using weighted value and effort scores.Identify overcommitment by comparing demand against dev-hour capacity.Defend roadmap decisions by documenting explicit opportunity costs.Sequence execution based on dependencies and urgency.

    About this skill

    The problem

    Engineering and product teams often suffer from "everything is a priority" syndrome. Without a structured way to rank competing requests against capacity, teams overcommit, ship late, or waste cycles on low-value tasks.

    What it does

    • Ranks projects using a weighted formula based on value, urgency, strategic alignment, effort, and risk.
    • Calculates capacity vs. demand to identify overcommitment gaps before they happen.
    • Categorizes initiatives into Pursue, Delay, Delegate, or Stop buckets.
    • Surfaces the specific opportunity cost for every top-tier project selected.
    • Generates a sequenced execution roadmap with first-step actions for every active stream.

    Why this beats prompting it yourself

    This skill prevents the "middle-ground" bias where LLMs rank everything as equally important. It uses a specific weighted mathematical model and anchored scoring to force difficult trade-offs that simple prompting usually misses.

    Use cases

    • Quarterly planning to decide which initiatives get funded and which get shelved.
    • Managing inbound requests that threaten to derail a current sprint or roadmap.
    • Rationalizing a bloated project backlog by identifying high-risk or low-alignment work to stop.
    • Communicating resource constraints to stakeholders using data-backed capacity analysis.

    Known limitations

    Requires the user to provide rough effort estimates and strategic goals. It does not integrate directly with Jira or linear APIs to pull data or update tickets.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

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