data-encryption

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 data-encryption
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summary

Implement robust encryption strategies for protecting sensitive data at rest and in transit using industry-standard cryptographic algorithms and key management practices.

skill.md

Data Encryption

Table of Contents

Overview

Implement robust encryption strategies for protecting sensitive data at rest and in transit using industry-standard cryptographic algorithms and key management practices.

When to Use

  • Sensitive data storage
  • Database encryption
  • File encryption
  • Communication security
  • Compliance requirements (GDPR, HIPAA, PCI-DSS)
  • Password storage
  • End-to-end encryption

Quick Start

Minimal working example:

// encryption-service.js
const crypto = require("crypto");
const fs = require("fs").promises;

class EncryptionService {
  constructor() {
    // AES-256-GCM for symmetric encryption
    this.algorithm = "aes-256-gcm";
    this.keyLength = 32; // 256 bits
    this.ivLength = 16; // 128 bits
    this.saltLength = 64;
    this.tagLength = 16;
  }

  /**
   * Generate a cryptographically secure random key
   */
  generateKey() {
    return crypto.randomBytes(this.keyLength);
  }

  /**
   * Derive a key from a password using PBKDF2
   */
  async deriveKey(password, salt = null) {
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide Contents
Node.js Encryption Library Node.js Encryption Library
Python Cryptography Implementation Python Cryptography Implementation
Database Encryption (PostgreSQL) Database Encryption (PostgreSQL)
TLS/SSL Configuration TLS/SSL Configuration

Best Practices

✅ DO

  • Use AES-256-GCM for symmetric encryption
  • Use RSA-4096 or ECC for asymmetric encryption
  • Implement proper key rotation
  • Use secure key storage (HSM, KMS)
  • Salt and hash passwords
  • Use TLS 1.2+ for transit encryption
  • Implement key derivation (PBKDF2, Argon2)
  • Use authenticated encryption

❌ DON'T

  • Roll your own crypto
  • Store keys in code
  • Use ECB mode
  • Use MD5 or SHA1
  • Reuse IVs/nonces
  • Use weak key lengths
  • Skip authentication tags
how to use data-encryption

How to use data-encryption 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 data-encryption
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 data-encryption

The skills CLI fetches data-encryption 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/data-encryption

Reload or restart Cursor to activate data-encryption. Access the skill through slash commands (e.g., /data-encryption) 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

4.730 reviews
  • Dev Srinivasan· Dec 20, 2024

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

  • Chaitanya Patil· Dec 12, 2024

    data-encryption has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Charlotte Harris· Dec 4, 2024

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

  • Kabir Khanna· Nov 27, 2024

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

  • Soo Yang· Nov 23, 2024

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

  • Charlotte Martin· Nov 11, 2024

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

  • Piyush G· Nov 3, 2024

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

  • Shikha Mishra· Oct 22, 2024

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

  • Dev Singh· Oct 18, 2024

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

  • Emma Rao· Oct 14, 2024

    data-encryption has been reliable in day-to-day use. Documentation quality is above average for community skills.

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