legacy-to-ai-ready

nicepkg/ai-workflow · updated Apr 8, 2026

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$npx skills add https://github.com/nicepkg/ai-workflow --skill legacy-to-ai-ready
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summary

Transform legacy codebases into AI-ready projects by generating Claude Code configurations.

skill.md

Legacy to AI-Ready

Transform legacy codebases into AI-ready projects by generating Claude Code configurations.

Quick Start (5-Minute Setup)

For most projects, start with just CLAUDE.md:

  1. Analyze: python scripts/analyze_codebase.py [path]
  2. Create CLAUDE.md with build commands, code style, architecture overview
  3. Done - Claude can now write code following your project's conventions

Expand to full configuration only when needed.

Interactive Discovery

Before generating configs, ask these questions:

Project Scope:

  • What is this project? (web app, API, CLI, library)
  • Primary language and frameworks?
  • Team size? (solo, small team, enterprise)

Pain Points:

  • What mistakes do new developers commonly make?
  • What patterns should always be followed?
  • What operations are repeated frequently?

Integration Needs:

  • External services used? (databases, APIs, cloud)
  • CI/CD pipeline? (GitHub Actions, Jenkins)
  • Code quality tools? (linters, formatters)

Configuration Decision Tree

Start
├─ Small project / Solo dev
│  └─ CLAUDE.md only
├─ Team project
│  ├─ Multi-language? → Add .claude/rules/
│  ├─ Complex domain? → Add .claude/skills/
│  ├─ Code reviews? → Add .claude/agents/
│  └─ Repeated tasks? → Add .claude/commands/
└─ Enterprise / Large team
   └─ All configurations + MCP servers + Hooks

Generated Configurations

Config Purpose When to Create
CLAUDE.md Project memory (shared) Always (required)
CLAUDE.local.md Personal preferences (git-ignored) Individual customization
.claudeignore Files Claude should not access Sensitive files exist
.claude/rules/ Path-specific rules Multi-module projects
.claude/skills/ Domain knowledge Complex business logic
.claude/agents/ Task specialists Repeated review/debug tasks
.claude/commands/ Quick prompts Common workflows
.claude/settings.json Hooks + permissions Auto-formatting, security
MCP servers External tools Database/API integrations

Workflow

Phase 1: Automated Analysis

python scripts/analyze_codebase.py [project-path]

The script detects:

  • Languages and frameworks
  • Directory structure patterns
  • Development tools (linters, formatters)
  • Git commit patterns
  • Environment variables
  • Code style indicators
  • CI/CD configurations (GitHub Actions, etc.)
  • Sensitive files (warns about .env, credentials)

Output includes:

  • Recommendations for which configs to create
  • Security warnings for sensitive files
  • Suggested .claudeignore patterns

Phase 2: Context Gathering

Claude should read:

  1. 3-5 representative source files - understand naming, patterns
  2. Test files - understand testing approach
  3. Config files - package.json, tsconfig, etc.
  4. README/docs - project overview
  5. Recent commits - understand commit style

Phase 2.5: Discover Existing Resources

Before creating custom configs, search for existing skills and MCP servers:

  1. Search skill marketplaces - SkillsMP, SkillHub.club, Claude Skills Hub
  2. Check GitHub repositories - awesome-claude-skills, themed skill collections
  3. Find relevant MCP servers - Glama, MCP Market, official registry

See references/resource-discovery.md for complete directory of sources.

Tip: Many common needs (git commit, code review, database patterns) already have well-maintained skills available.

Phase 3: Generate Configurations

1. CLAUDE.md (Required)

Create at project root with:

  • Quick reference commands (build, test, lint)
  • Naming conventions
  • Architecture overview
  • Testing guidelines
  • Git workflow

See references/claude-md-patterns.md.

2. Rules (If Multi-Module)

Create .claude/rules/ when:

  • Multiple languages need different conventions
  • Modules have distinct patterns (frontend/backend)
  • Path-specific requirements exist

See references/rules-patterns.md.

3. Skills (If Complex Domain)

Create .claude/skills/ when:

  • Domain-specific workflows exist (database, API patterns)
  • Team knowledge needs preservation
  • Complex procedures are repeated

See references/skills-patterns.md.

4. Subagents (If Specialized Tasks)

Create .claude/agents/ for:

  • Code review automation
  • Debugging assistance
  • Security auditing
  • Documentation generation

See references/agents-patterns.md.

5. Commands (If Common Operations)

Create .claude/commands/ for:

  • Git commit workflow
  • PR review process
  • Deployment steps
  • Test running

See references/commands-patterns.md.

6. Hooks (If Auto-Formatting Needed)

Configure .claude/settings.json for:

  • Auto-format on file edit
  • Protected files
  • Command logging

See references/hooks-patterns.md.

7. MCP Servers (If External Integrations)

Configure MCP for:

  • Database access
  • GitHub integration
  • Slack notifications
  • Custom internal tools

See references/mcp-patterns.md.

Phase 4: Validate

  1. Ask Claude to perform a typical task
  2. Verify it follows project conventions
  3. Iterate based on gaps discovered

Output Structure

Minimal (small projects):

project/
├── CLAUDE.md
└── [existing files]

Standard (team projects):

project/
├── CLAUDE.md
├── .claude/
│   ├── rules/
│   │   └── code-style.md
│   └── commands/
│       └── commit.md
└── [existing files]

Complete (enterprise):

project/
├── CLAUDE.md              # Shared project memory
├── CLAUDE.local.md        # Personal (git-ignored)
├── .claudeignore          # Files to protect
├── .claude/
│   ├── settings.json      # Hooks + permissions
│   ├── rules/
│   ├── skills/
│   ├── agents/
│   └── commands/
└── [existing files]

Reference Materials

Reference When to Read
examples.md Complete real-world examples
resource-discovery.md Find existing skills & MCP servers
advanced-patterns.md Migrations, team collab, monorepos
claude-md-patterns.md Creating CLAUDE.md
rules-patterns.md Module-specific rules
skills-patterns.md Domain knowledge
agents-patterns.md Task specialists
commands-patterns.md Quick prompts
hooks-patterns.md Auto-formatting
mcp-patterns.md External tools

Templates & Bundled Skills

Templates:

  • assets/CLAUDE.md.template - Project memory template
  • assets/settings.json.template - Hooks configuration
  • assets/claudeignore.template - File ignore patterns

Bundled skills to install in target project:

  • assets/skill-creator/ - For creating new project-specific skills
  • assets/skill-downloader/ - For downloading additional skills
  • assets/resource-scout/ - For discovering existing skills & MCP servers

Installing Bundled Skills

Copy these skills to the target project's .claude/skills/ directory:

cp -r assets/skill-creator [target-project]/.claude/skills/
cp -r assets/skill-downloader [target-project]/.claude/skills/
cp -r assets/resource-scout [target-project]/.claude/skills/

This enables the target project to:

  1. resource-scout - Discover existing skills & MCP servers before building custom
  2. skill-downloader - Download and install skills from GitHub or archives
  3. skill-creator - Create custom skills tailored to their domain

Language Quick Reference

TypeScript/JavaScript

  • Extract: eslint, prettier, tsconfig
  • Hooks: prettier auto-format
  • Skills: API patterns, component patterns

Python

  • Extract: black, ruff, mypy, pyproject.toml
  • Hooks: black/ruff auto-format
  • Skills: API patterns, ORM patterns

Go

  • Extract: gofmt, golangci-lint
  • Hooks: gofmt auto-format
  • Skills: error handling patterns

Rust

  • Extract: rustfmt, clippy
  • Hooks: rustfmt auto-format
  • Skills: error handling, async patterns
how to use legacy-to-ai-ready

How to use legacy-to-ai-ready 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 legacy-to-ai-ready
2

Execute installation command

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

$npx skills add https://github.com/nicepkg/ai-workflow --skill legacy-to-ai-ready

The skills CLI fetches legacy-to-ai-ready from GitHub repository nicepkg/ai-workflow 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/legacy-to-ai-ready

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

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.825 reviews
  • Dhruvi Jain· Dec 20, 2024

    legacy-to-ai-ready reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kofi White· Dec 16, 2024

    legacy-to-ai-ready reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Evelyn Gill· Dec 12, 2024

    We added legacy-to-ai-ready from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Oshnikdeep· Nov 11, 2024

    I recommend legacy-to-ai-ready for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Nia Chawla· Nov 7, 2024

    I recommend legacy-to-ai-ready for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Benjamin Tandon· Nov 3, 2024

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

  • James Bhatia· Oct 26, 2024

    Useful defaults in legacy-to-ai-ready — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Xiao Mehta· Oct 22, 2024

    legacy-to-ai-ready is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ganesh Mohane· Oct 2, 2024

    Useful defaults in legacy-to-ai-ready — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Michael Sharma· Jul 27, 2024

    Solid pick for teams standardizing on skills: legacy-to-ai-ready is focused, and the summary matches what you get after install.

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