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Analyzing Projects

CommunityPopular
CloudAI-X
analyzing-projects

Analyzes codebases to understand structure, tech stack, patterns, and conventions. Use when onboarding to a new project, exploring unfamiliar code, or when asked "how does this work?" or "what's the architecture?"

Overview

PublisherCloudAI-X
Repositoryclaude-workflow-v2
Skill nameanalyzing-projects
Stars
1.4K
Forks
188
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by CloudAI-X on GitHub. Read the source before you install it.

Installation

Install the Analyzing Projects AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/CloudAI-X/claude-workflow-v2.git /tmp/claude-workflow-v2
mkdir -p .claude/skills
cp -r /tmp/claude-workflow-v2/skills/analyzing-projects .claude/skills/analyzing-projects
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Projects in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyzing Projects on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyzing Projects is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analyzing Projects

When to Load

  • Trigger: Onboarding to a new project, "how does this work" questions, codebase exploration, understanding unfamiliar code
  • Skip: Already familiar with the project structure and patterns

Project Analysis Workflow

Copy this checklist and track progress:

Project Analysis Progress:
- [ ] Step 1: Quick overview (README, root files)
- [ ] Step 2: Detect tech stack
- [ ] Step 3: Map project structure
- [ ] Step 4: Identify key patterns
- [ ] Step 5: Find development workflow
- [ ] Step 6: Generate summary report

Step 1: Quick Overview

bash
# Check for common project markers
ls -la
cat README.md 2>/dev/null | head -50

Step 2: Tech Stack Detection

Package Managers & Dependencies

  • package.json → Node.js/JavaScript/TypeScript
  • requirements.txt / pyproject.toml / setup.py → Python
  • go.mod → Go
  • Cargo.toml → Rust
  • pom.xml / build.gradle → Java
  • Gemfile → Ruby

Frameworks (from dependencies)

  • React, Vue, Angular, Next.js, Nuxt
  • Express, FastAPI, Django, Flask, Rails
  • Spring Boot, Gin, Echo

Infrastructure

  • Dockerfile, docker-compose.yml → Containerized
  • kubernetes/, k8s/ → Kubernetes
  • terraform/, .tf files → IaC
  • serverless.yml → Serverless Framework
  • .github/workflows/ → GitHub Actions

Step 3: Project Structure Analysis

Present as a tree with annotations:

project/
├── src/              # Source code
│   ├── components/   # UI components (React/Vue)
│   ├── services/     # Business logic
│   ├── models/       # Data models
│   └── utils/        # Shared utilities
├── tests/            # Test files
├── docs/             # Documentation
└── config/           # Configuration

Step 4: Key Patterns Identification

Look for and report:

  • Architecture: Monolith, Microservices, Serverless, Monorepo
  • API Style: REST, GraphQL, gRPC, tRPC
  • State Management: Redux, Zustand, MobX, Context
  • Database: SQL, NoSQL, ORM used
  • Authentication: JWT, OAuth, Sessions
  • Testing: Jest, Pytest, Go test, etc.

Step 5: Development Workflow

Check for:

  • .eslintrc, .prettierrc → Linting/Formatting
  • .husky/ → Git hooks
  • Makefile → Build commands
  • scripts/ in package.json → NPM scripts

Step 6: Output Format

Generate a summary using this template:

markdown
# Project: [Name]

## Overview

[1-2 sentence description]

## Tech Stack

| Category  | Technology |
| --------- | ---------- |
| Language  | TypeScript |
| Framework | Next.js 14 |
| Database  | PostgreSQL |
| ...       | ...        |

## Architecture

[Description with simple ASCII diagram if helpful]

## Key Directories

- `src/` - [purpose]
- `lib/` - [purpose]

## Entry Points

- Main: `src/index.ts`
- API: `src/api/`
- Tests: `npm test`

## Conventions

- [Naming conventions]
- [File organization patterns]
- [Code style preferences]

## Quick Commands

| Action  | Command         |
| ------- | --------------- |
| Install | `npm install`   |
| Dev     | `npm run dev`   |
| Test    | `npm test`      |
| Build   | `npm run build` |

Analysis Validation

After completing analysis, verify:

Analysis Validation:
- [ ] All major directories explained
- [ ] Tech stack accurately identified
- [ ] Entry points documented
- [ ] Development commands verified working
- [ ] No assumptions made without evidence

If any items cannot be verified, note them as "needs clarification" in the report.

Frequently asked questions

What does the Analyzing Projects AI skill do?

Analyzes codebases to understand structure, tech stack, patterns, and conventions. Use when onboarding to a new project, exploring unfamiliar code, or when asked "how does this work?" or "what's the architecture?"

Why use Analyzing Projects on TypingMind?

Because you install it once and use it with any model. Analyzing Projects is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyzing Projects in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/CloudAI-X/claude-workflow-v2/tree/main/skills/analyzing-projects. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analyzing Projects?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyzing Projects?

As many as you like. As long as a model supports skills, you can use Analyzing Projects with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyzing Projects AI skill free?

Yes. It is published on GitHub by CloudAI-X under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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