gap-analysis

aj-geddes/useful-ai-prompts · updated Apr 8, 2026

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$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill gap-analysis
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summary

Gap analysis systematically compares current capabilities with desired future state, revealing what needs to change and what investments are required.

skill.md

Gap Analysis

Table of Contents

Overview

Gap analysis systematically compares current capabilities with desired future state, revealing what needs to change and what investments are required.

When to Use

  • Strategic planning and goal setting
  • Technology modernization assessment
  • Process improvement initiatives
  • Skills and training planning
  • System evaluation and selection
  • Organizational change planning
  • Capability building programs

Quick Start

Minimal working example:

# Systematic gap identification

class GapAnalysis:
    GAP_CATEGORIES = {
        'Business Capability': 'Functions organization can perform',
        'Process': 'How work gets done',
        'Technology': 'Tools and systems available',
        'Skills': 'Knowledge and expertise',
        'Data': 'Information available',
        'People/Culture': 'Team composition and mindset',
        'Organization': 'Structure and roles',
        'Metrics': 'Ability to measure performance'
    }

    def identify_gaps(self, current_state, future_state):
        """Compare current vs desired and find gaps"""
        gaps = []

        for capability in future_state['capabilities']:
            current_capability = self.find_capability(
                capability['name'],
                current_state['capabilities']
            )

            if current_capability is None:
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide Contents
Gap Identification Framework Gap Identification Framework
Gap Analysis Template Gap Analysis Template
Gap Closure Planning Gap Closure Planning
Communication & Tracking Communication & Tracking

Best Practices

✅ DO

  • Compare current to clearly defined future state
  • Include all relevant capability areas
  • Involve stakeholders in gap identification
  • Prioritize by value and effort
  • Create detailed closure plans
  • Track progress to closure
  • Document gap analysis findings
  • Review and update analysis quarterly
  • Link gaps to business strategy
  • Communicate findings transparently

❌ DON'T

  • Skip current state assessment
  • Create vague future state
  • Identify gaps without solutions
  • Ignore implementation effort
  • Plan all gaps in parallel
  • Forget about dependencies
  • Ignore resource constraints
  • Hide difficult findings
  • Plan for 100% effort allocation
  • Forget about change management
how to use gap-analysis

How to use gap-analysis on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add gap-analysis
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill gap-analysis

The skills CLI fetches gap-analysis from GitHub repository aj-geddes/useful-ai-prompts and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/gap-analysis

Reload or restart Cursor to activate gap-analysis. Access the skill through slash commands (e.g., /gap-analysis) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.641 reviews
  • Shikha Mishra· Dec 24, 2024

    Registry listing for gap-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Valentina Diallo· Dec 20, 2024

    gap-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sakura Farah· Dec 20, 2024

    gap-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sophia Brown· Dec 16, 2024

    I recommend gap-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Hiroshi Bansal· Dec 8, 2024

    Solid pick for teams standardizing on skills: gap-analysis is focused, and the summary matches what you get after install.

  • Soo Nasser· Nov 27, 2024

    Registry listing for gap-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Yash Thakker· Nov 15, 2024

    Solid pick for teams standardizing on skills: gap-analysis is focused, and the summary matches what you get after install.

  • Yuki Shah· Nov 15, 2024

    Keeps context tight: gap-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Diego Bhatia· Nov 11, 2024

    We added gap-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Carlos Khanna· Nov 7, 2024

    Useful defaults in gap-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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