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Deep Analysis

Organization
MadAppGang
deep-analysis

⚡ PRIMARY SKILL for: 'how does X work', 'investigate', 'analyze architecture', 'trace flow', 'find implementations'. PREREQUISITE: code-search-selector must validate tool choice. Launches codebase-detective with claudemem INDEXED MEMORY.

Overview

PublisherMadAppGang
Repositoryclaude-code
Skill namedeep-analysis
Stars
281
Forks
26
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 MadAppGang on GitHub. Read the source before you install it.

Installation

Install the Deep Analysis 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/MadAppGang/claude-code.git /tmp/claude-code
mkdir -p .claude/skills
cp -r /tmp/claude-code/plugins/code-analysis/skills/deep-analysis .claude/skills/deep-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Analysis 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 Deep Analysis 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 Deep Analysis 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.

Deep Code Analysis

This Skill provides comprehensive codebase investigation capabilities using the codebase-detective agent with semantic search and pattern matching.

Prerequisites (MANDATORY)

╔══════════════════════════════════════════════════════════════════════════════╗
║                        BEFORE INVOKING THIS SKILL                             ║
╠══════════════════════════════════════════════════════════════════════════════╣
║                                                                              ║
║  1. INVOKE code-search-selector skill FIRST                                  ║
║     → Validates tool selection (claudemem vs grep)                           ║
║     → Checks if claudemem is indexed                                         ║
║     → Prevents tool familiarity bias                                         ║
║                                                                              ║
║  2. VERIFY claudemem status                                                  ║
║     → Run: claudemem status                                                  ║
║     → If not indexed: claudemem index -y                                     ║
║                                                                              ║
║  3. DO NOT start with Read/Glob                                              ║
║     → Even if file paths are mentioned in the prompt                         ║
║     → Semantic search first, Read specific lines after                       ║
║                                                                              ║
╚══════════════════════════════════════════════════════════════════════════════╝

When to use this Skill

Claude should invoke this Skill when:

  • User asks "how does [feature] work?"
  • User wants to understand code architecture or patterns
  • User is debugging and needs to trace code flow
  • User asks "where is [functionality] implemented?"
  • User needs to find all usages of a component/service
  • User wants to understand dependencies between files
  • User mentions: "investigate", "analyze", "find", "trace", "understand"
  • User is exploring an unfamiliar codebase
  • User needs to understand complex multi-file functionality

Instructions

Phase 1: Determine Investigation Scope

Understand what the user wants to investigate:

  1. Specific Feature: "How does user authentication work?"
  2. Find Implementation: "Where is the payment processing logic?"
  3. Trace Flow: "What happens when I click the submit button?"
  4. Debug Issue: "Why is the profile page showing undefined?"
  5. Find Patterns: "Where are all the API calls made?"
  6. Analyze Architecture: "What's the structure of the data layer?"

Phase 2: Invoke codebase-detective Agent

Use the Task tool to launch the codebase-detective agent with comprehensive instructions:

Use Task tool with:
- subagent_type: "code-analysis:detective"
- description: "Investigate [brief summary]"
- prompt: [Detailed investigation instructions]

Prompt structure for codebase-detective:

markdown
# Code Investigation Task

## Investigation Target
[What needs to be investigated - be specific]

## Context
- Working Directory: [current working directory]
- Purpose: [debugging/learning/refactoring/etc]
- User's Question: [original user question]

## Investigation Steps

1. **Initial Search** (CLAUDEMEM REQUIRED):
   - FIRST: Check `claudemem status` - is index available?
   - ALWAYS: Use `claudemem search "semantic query"` for investigation
   - NEVER: Use grep/glob for semantic understanding tasks
   - Search for: [concepts, functionality, patterns by meaning]

2. **Code Location**:
   - Find exact file paths and line numbers
   - Identify entry points and main implementations
   - Note related files and dependencies

3. **Code Flow Analysis**:
   - Trace how data/control flows through the code
   - Identify key functions and their roles
   - Map out component/service relationships

4. **Pattern Recognition**:
   - Identify architectural patterns used
   - Note code conventions and styles
   - Find similar implementations for reference

## Deliverables

Provide a comprehensive report including:

1. **📍 Primary Locations**:
   - Main implementation files with line numbers
   - Entry points and key functions
   - Configuration and setup files

2. **🔍 Code Flow**:
   - Step-by-step flow explanation
   - How components interact
   - Data transformation points

3. **🗺️ Architecture Map**:
   - High-level structure diagram
   - Component relationships
   - Dependency graph

4. **📝 Code Snippets**:
   - Key implementations (show important code)
   - Patterns and conventions used
   - Notable details or gotchas

5. **🚀 Navigation Guide**:
   - How to explore the code further
   - Related files to examine
   - Commands to run for testing

6. **💡 Insights**:
   - Why the code is structured this way
   - Potential issues or improvements
   - Best practices observed

## Search Strategy

### ⚠️ CRITICAL: Tool Selection

**BEFORE ANY SEARCH, CHECK CLAUDEMEM STATUS:**
```bash
claudemem status

✅ PRIMARY METHOD: claudemem (Indexed Memory)

bash
# Index if needed
claudemem index -y

# Semantic search (ALWAYS use this for investigation)
claudemem search "authentication login session" -n 15
claudemem search "API endpoint handler route" -n 20
claudemem search "data transformation pipeline" -n 10

Why claudemem is REQUIRED for investigation:

  • Understands code MEANING, not just text patterns
  • Finds related code even with different terminology
  • Returns ranked, relevant results
  • AST-aware (understands code structure)

❌ WHEN NOT TO USE GREP

User Request❌ DON'T✅ DO
"How does auth work?"grep -r "auth" src/claudemem search "authentication flow"
"Find API endpoints"grep -r "router" src/claudemem search "API endpoint handler"
"Trace data flow"grep -r "transform" src/claudemem search "data transformation"
"Audit architecture"ls -la src/claudemem search "architecture layers"

⚠️ DEGRADED FALLBACK (Only if claudemem unavailable)

Only use grep/find if:

  1. claudemem is NOT installed, AND
  2. User explicitly accepts degraded mode
bash
# DEGRADED MODE - inferior results expected
grep -r "pattern" src/  # Text match only, no semantic understanding
find . -name "*.ts"     # File discovery only

Always warn user: "Using grep fallback - results will be less accurate than semantic search."

Output Format

Structure your findings clearly with:

  • File paths using backticks: src/auth/login.ts:45
  • Code blocks for snippets
  • Clear headings and sections
  • Actionable next steps

### Phase 3: Present Analysis Results

After the agent completes, present results to the user:

1. **Executive Summary** (2-3 sentences):
   - What was found
   - Where it's located
   - Key insight

2. **Detailed Findings**:
   - Primary file locations with line numbers
   - Code flow explanation
   - Architecture overview

3. **Visual Structure** (if complex):

EntryPoint (file:line) ├── Validator (file:line) ├── BusinessLogic (file:line) │ └── DataAccess (file:line) └── ResponseHandler (file:line)


4. **Code Examples**:
- Show key code snippets inline
- Highlight important patterns

5. **Next Steps**:
- Suggest follow-up investigations
- Offer to dive deeper into specific parts
- Provide commands to test/run the code

### Phase 4: Offer Follow-up

Ask the user:
- "Would you like me to investigate any specific part in more detail?"
- "Do you want to see how [related feature] works?"
- "Should I trace [specific function] further?"

## Example Scenarios

### Example 1: Understanding Authentication

User: "How does login work in this app?"

Skill invokes codebase-detective agent with: "Investigate user authentication and login flow:

  1. Find login API endpoint or form handler
  2. Trace authentication logic
  3. Identify token generation/storage
  4. Find session management
  5. Locate authentication middleware"

Agent provides:

  • src/api/auth/login.ts:34-78 (login endpoint)
  • src/services/authService.ts:12-45 (JWT generation)
  • src/middleware/authMiddleware.ts:23 (token validation)
  • Flow: Form → API → Service → Middleware → Protected Routes

### Example 2: Debugging Undefined Error

User: "The dashboard shows 'undefined' for user name"

Skill invokes codebase-detective agent with: "Debug undefined user name in dashboard:

  1. Find Dashboard component
  2. Locate where user name is rendered
  3. Trace user data fetching
  4. Check data transformation/mapping
  5. Identify where undefined is introduced"

Agent provides:

  • src/components/Dashboard.tsx:156 renders user.name
  • src/hooks/useUser.ts:45 fetches user data
  • Issue: API returns 'full_name' but code expects 'name'
  • Fix: Map 'full_name' to 'name' in useUser hook

### Example 3: Finding All API Calls

User: "Where are all the API calls made?"

Skill invokes codebase-detective agent with: "Find all API call locations:

  1. Search for fetch, axios, http client usage
  2. Identify API client/service files
  3. List all endpoints used
  4. Note patterns (REST, GraphQL, etc)
  5. Find error handling approach"

Agent provides:

  • 23 API calls across 8 files
  • Centralized in src/services/*
  • Using axios with interceptors
  • Base URL in src/config/api.ts
  • Error handling in src/utils/errorHandler.ts

## Success Criteria

The Skill is successful when:

1. ✅ User's question is comprehensively answered
2. ✅ Exact code locations provided with line numbers
3. ✅ Code relationships and flow clearly explained
4. ✅ User can navigate to code and understand it
5. ✅ Architecture patterns identified and explained
6. ✅ Follow-up questions anticipated

## Tips for Optimal Results

1. **Be Comprehensive**: Don't just find one file, map the entire flow
2. **Provide Context**: Explain why code is structured this way
3. **Show Examples**: Include actual code snippets
4. **Think Holistically**: Connect related pieces across files
5. **Anticipate Questions**: Answer follow-up questions proactively

## Integration with Other Tools

This Skill works well with:

- **claudemem CLI**: For local semantic code search with Tree-sitter parsing
- **MCP gopls**: For Go-specific analysis
- **Standard CLI tools**: grep, ripgrep, find, git
- **Project-specific tools**: Use project's search/navigation tools

## Notes

- The codebase-detective agent uses extended thinking for complex analysis
- **claudemem is REQUIRED** - grep/find produce inferior results
- Fallback to grep ONLY if claudemem unavailable AND user accepts degraded mode
- claudemem requires OpenRouter API key (https://openrouter.ai)
- Default model: `voyage/voyage-code-3` (best code understanding)
- Run `claudemem --models` to see all options and pricing
- Results are actionable and navigable
- Great for onboarding to new codebases
- Helps prevent incorrect assumptions about code

## Tool Selection Quick Reference

┌─────────────────────────────────────────────────────────────────────┐ │ BEFORE ANY CODE INVESTIGATION: │ │ │ │ 1. INVOKE code-search-selector skill │ │ 2. Run: claudemem status │ │ 3. If indexed → USE claudemem search │ │ 4. If not indexed → Index first OR ask user │ │ 5. NEVER default to grep when claudemem available │ │ 6. NEVER start with Read/Glob for semantic questions │ │ │ │ grep is for EXACT STRING MATCHES only, NOT semantic understanding │ └─────────────────────────────────────────────────────────────────────┘


---

**Maintained by:** MadAppGang
**Plugin:** code-analysis v2.2.0
**Last Updated:** December 2025

Frequently asked questions

What does the Deep Analysis AI skill do?

⚡ PRIMARY SKILL for: 'how does X work', 'investigate', 'analyze architecture', 'trace flow', 'find implementations'. PREREQUISITE: code-search-selector must validate tool choice. Launches codebase-detective with claudemem INDEXED MEMORY.

Why use Deep Analysis on TypingMind?

Because you install it once and use it with any model. Deep Analysis 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 Deep Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MadAppGang/claude-code/tree/main/plugins/code-analysis/skills/deep-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deep Analysis?

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 Deep Analysis?

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

Is the Deep Analysis AI skill free?

Yes. It is published on GitHub by MadAppGang 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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