Agent skill

Legacy To AI Ready

by nicepkg in nicepkg/ai-workflow

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

MITAuto-check: notesAgent Workflows

Install Legacy To AI Ready

skills CLI
$ npx skills add nicepkg/ai-workflow --skill legacy-to-ai-ready -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nicepkg/ai-workflow legacy-to-ai-ready --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nicepkg/ai-workflow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/legacy-to-ai-ready .claude/skills/legacy-to-ai-ready && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
legacy-to-ai-ready
GitHub stars
285
Token cost
~2.2k tokens
SKILL.md length
709 words
Files
15 (incl. scripts, references, assets)
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 5 steps: Automated Analysis → Context Gathering → 5: Discover Existing Resources → …
  • Analyzing old projects to generate AI coding configurations
  • SKILL.md covers Quick Start (5-Minute Setup), Interactive Discovery, Configuration Decision Tree and Generated Configurations, plus 5 more sections
  • Runs Python scripts from its folder; calls python

What it does

Legacy To AI Ready is an agent skill from nicepkg/ai-workflow. Transform legacy codebases into AI-ready projects with Claude Code configurations. Use when (1) analyzing old projects to generate AI coding configurations, (2) creating CLAUDE.md, skills, subagents, slash commands, hooks, or rules for existing projects, (3) user wants to enable vibe coding for a codebase, (4) onboarding new team members with AI-assisted development, (5) user mentions "make project AI-ready", "generate Claude config", or "create coding standards for AI".

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts, reference files and assets (for example `references/advanced-patterns.md`, `references/agents-patterns.md` and `references/claude-md-patterns.md`).

It sits in Agent Workflows, covering Hooks and plugins, Agent instruction files and Code quality. The repository describes itself as: 🚀 170+ pre-built skills for Claude Code, Cursor, Codex & 14+ AI tools. Stop re-teaching your AI the same things. One command → instant domain expertise. Marketing, SEO, Trading… The licence is MIT.

When your agent uses it

  • Analyzing old projects to generate AI coding configurations
  • Creating CLAUDE.md
  • Rules for existing projects
  • User wants to enable vibe coding for a codebase

Example prompts

  • “make project AI-ready”
  • “generate Claude config”
  • “create coding standards for AI”
  • “/legacy-to-ai-ready”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Automated Analysis
  2. Context Gathering
  3. 5: Discover Existing Resources
  4. Generate Configurations
  5. Validate

What it can do on your machine

Read from SKILL.md and the folder at commit d167b41. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Legacy To AI Ready loads about 2.2k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 124 tokens; SKILL.md has 709 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~124
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~17k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:87
    - **Sensitive files** (warns about .env, credentials)

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from nicepkg/ai-workflow at commit d167b41, republished under its MIT licence (© nicepkg). 709 words, ~2,247 tokens.

Download SKILL.mdSave it as .claude/skills/legacy-to-ai-ready/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.
name
legacy-to-ai-ready
description
Transform legacy codebases into AI-ready projects with Claude Code configurations. Use when (1) analyzing old projects to generate AI coding configurations, (2) creating CLAUDE.md, skills, subagents, slash commands, hooks, or rules for existing projects, (3) user wants to enable vibe coding for a codebase, (4) onboarding new team members with AI-assisted development, (5) user mentions "make project AI-ready", "generate Claude config", or "create coding standards for AI".

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

ConfigPurposeWhen to Create
CLAUDE.mdProject memory (shared)Always (required)
CLAUDE.local.mdPersonal preferences (git-ignored)Individual customization
.claudeignoreFiles Claude should not accessSensitive files exist
.claude/rules/Path-specific rulesMulti-module projects
.claude/skills/Domain knowledgeComplex business logic
.claude/agents/Task specialistsRepeated review/debug tasks
.claude/commands/Quick promptsCommon workflows
.claude/settings.jsonHooks + permissionsAuto-formatting, security
MCP serversExternal toolsDatabase/API integrations

Workflow

Phase 1: Automated Analysis
bash
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.

Show full SKILL.md (291 more words)Show less
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

ReferenceWhen to Read
examples.mdComplete real-world examples
resource-discovery.mdFind existing skills & MCP servers
advanced-patterns.mdMigrations, team collab, monorepos
claude-md-patterns.mdCreating CLAUDE.md
rules-patterns.mdModule-specific rules
skills-patterns.mdDomain knowledge
agents-patterns.mdTask specialists
commands-patterns.mdQuick prompts
hooks-patterns.mdAuto-formatting
mcp-patterns.mdExternal 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:

bash
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

© nicepkg, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 14 other files (scripts, references, assets) in .claude/skills/legacy-to-ai-ready of nicepkg/ai-workflow.

  • SKILL.md
  • assets/CLAUDE.md.template
  • assets/claudeignore.template
  • assets/settings.json.template
  • references/advanced-patterns.md
  • references/agents-patterns.md
  • references/claude-md-patterns.md
  • references/commands-patterns.md
  • references/examples.md
  • references/hooks-patterns.md
  • references/mcp-patterns.md
  • references/resource-discovery.md
  • references/rules-patterns.md
  • references/skills-patterns.md
  • scripts/analyze_codebase.py

Open the folder on GitHubat commit d167b41

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Categories

Questions about Legacy To AI Ready

What does Legacy To AI Ready do?

Transform legacy codebases into AI-ready projects with Claude Code configurations. Legacy To AI Ready is an agent skill from nicepkg/ai-workflow. Transform legacy codebases into AI-ready projects with Claude Code configurations.

When should I use Legacy To AI Ready?

Legacy To AI Ready fits situations like: analyzing old projects to generate AI coding configurations; creating CLAUDE.md; rules for existing projects; user wants to enable vibe coding for a codebase.

How do I install Legacy To AI Ready in Claude Code?

Run `npx skills add nicepkg/ai-workflow --skill legacy-to-ai-ready -a claude-code`. Or copy the skill folder (.claude/skills/legacy-to-ai-ready in nicepkg/ai-workflow) into .claude/skills/legacy-to-ai-ready in your project. Claude Code loads it when a task matches its description.

How do I install Legacy To AI Ready in Codex?

Run `npx skills add nicepkg/ai-workflow --skill legacy-to-ai-ready -a codex`. Or copy the skill folder (.claude/skills/legacy-to-ai-ready in nicepkg/ai-workflow) into .agents/skills/legacy-to-ai-ready in your project. Codex loads it when a task matches its description.

Can I use Legacy To AI Ready in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nicepkg/ai-workflow --skill legacy-to-ai-ready -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/legacy-to-ai-ready, .gemini/skills/legacy-to-ai-ready, .github/skills/legacy-to-ai-ready and .opencode/skills/legacy-to-ai-ready in your project.

What does Legacy To AI Ready need to run?

Going by SKILL.md and its folder, Legacy To AI Ready needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Legacy To AI Ready access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Legacy To AI Ready safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Legacy To AI Ready use?

Legacy To AI Ready is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Legacy To AI Ready use?

About 2.2k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 15k tokens, read only when the agent opens those files.

What are the alternatives to Legacy To AI Ready?

Skills that share tags, products or a category with Legacy To AI Ready: Agent Setup Health Audit (tw93/Waza, 7.2k stars), Claude Code Mastery Squad (ohmyjahh/xquads-squads, 277 stars), Audit Plugin (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and Claude Code Agent Development (anthropics/claude-plugins-official, 38k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Legacy To AI Ready?

nicepkg (a GitHub organization) maintains it in nicepkg/ai-workflow, which has 285 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on January 20, 2026.

Source: nicepkg/ai-workflow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.