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Analyzing Projects

  • 6 installs
  • 250 repo stars
  • Updated January 10, 2026
  • cloudai-x/opencode-workflow

Helps with ai & agent building tasks.

About

analyzing-projects is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • analyzing-projects
  • AI & Agent Building
  • AI-coding skill

Analyzing Projects by the numbers

  • 6 all-time installs (skills.sh)
  • Ranked #12,756 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs6
repo stars250
Last updatedJanuary 10, 2026
Repositorycloudai-x/opencode-workflow

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Analyzing Projects

Systematic approaches to understanding codebases, identifying patterns, and mapping system architecture.

When to Use This Skill

  • Onboarding to a new codebase
  • Understanding unfamiliar code before making changes
  • Investigating how features are implemented
  • Mapping dependencies between modules
  • Identifying architectural patterns in use

---

Core Analysis Framework

The 5-Layer Discovery Process

Layer 1: Surface Scan
  └─ Entry points, config files, directory structure

Layer 2: Dependency Mapping
  └─ Package managers, imports, module relationships

Layer 3: Architecture Recognition
  └─ Patterns (MVC, hexagonal, microservices)

Layer 4: Flow Tracing
  └─ Request paths, data flow, state management

Layer 5: Quality Assessment
  └─ Test coverage, code health, technical debt

---

Phase 1: Surface Scan

Entry Point Discovery

Start by identifying how the application launches:

1. Look for standard entry files:

  • main.*, index.*, app.*, server.*
  • cmd/ directory (Go)
  • src/main/ (Java)
  • bin/ scripts

2. Check configuration files:

  • package.json (scripts.start, main)
  • Makefile, Taskfile
  • Docker/Compose files
  • CI/CD configs (.github/workflows/)

3. Map directory structure:

   Quick heuristics:
   ├── src/           → Source code
   ├── lib/           → Internal libraries
   ├── pkg/           → Public packages (Go)
   ├── internal/      → Private packages (Go)
   ├── tests/         → Test files
   ├── docs/          → Documentation
   ├── scripts/       → Build/deploy scripts
   └── config/        → Configuration

Initial Questions to Answer

  • What language(s) and framework(s)?
  • What's the build system?
  • How is the app deployed?
  • Where are the main entry points?

---

Phase 2: Dependency Mapping

Package Manager Analysis

FileEcosystemKey Sections
package.jsonNode.jsdependencies, devDependencies
requirements.txt / pyproject.tomlPythondirect dependencies
go.modGorequire blocks
Cargo.tomlRustdependencies
pom.xml / build.gradleJavadependencies

Internal Module Relationships

1. Trace imports from entry points 2. Build a mental model of layers:

   Presentation Layer (routes, controllers, views)
         ↓
   Application Layer (services, use cases)
         ↓
   Domain Layer (entities, business logic)
         ↓
   Infrastructure Layer (database, external APIs)

3. Identify shared utilities imported across modules

---

Phase 3: Architecture Recognition

Common Patterns to Identify

PatternIndicatorsTypical Structure
MVCcontrollers/, models/, views/Clear separation of concerns
Hexagonalports/, adapters/, domain/Dependency inversion
Microservicesservices/, docker-composeIndependent deployable units
MonolithSingle large app, shared DBEverything in one deployment
Serverlessfunctions/, handlers/Event-driven, stateless

Architecture Questions

  • How are concerns separated?
  • Where does business logic live?
  • How are external dependencies abstracted?
  • What's the data access pattern?

---

Phase 4: Flow Tracing

Request Path Analysis

For web applications, trace a request end-to-end:

HTTP Request
    ↓
Router/Routes (maps URL → handler)
    ↓
Middleware (auth, logging, validation)
    ↓
Controller/Handler (orchestrates)
    ↓
Service/Use Case (business logic)
    ↓
Repository/DAO (data access)
    ↓
Database/External API

State Management Analysis

For frontend applications:

  • Where is state stored? (Redux, Zustand, Context)
  • How does data flow? (unidirectional, bidirectional)
  • What triggers re-renders?

---

Phase 5: Quality Assessment

Code Health Indicators

IndicatorGood SignWarning Sign
Test coverage>70% coverageNo tests, or tests ignored
DependenciesRecent versionsMajor versions behind
DocumentationREADME updatedStale or missing docs
Build timeUnder 2 minutesOver 10 minutes
Error handlingConsistent patternsSwallowed exceptions

Technical Debt Markers

  • TODO/FIXME comments
  • Disabled tests
  • Large functions (>50 lines)
  • Deep nesting (>4 levels)
  • Duplicated code blocks
  • Hardcoded values

---

Analysis Strategies by Goal

Goal: Make a Bug Fix

1. Find where the bug manifests 2. Trace back to the root cause 3. Understand the affected area only 4. Check for related tests

Goal: Add a New Feature

1. Find similar existing features 2. Understand the patterns they use 3. Map the modules that need changes 4. Identify integration points

Goal: Full Codebase Understanding

1. Complete all 5 phases 2. Document architecture decisions 3. Create a mental map of key flows 4. Identify ownership areas

---

Parallel Analysis Pattern

When exploring a large codebase, parallelize by module:

Spawn subagents for each major area:
├─ Subagent 1: Analyze src/auth (authentication module)
├─ Subagent 2: Analyze src/api (API layer)
├─ Subagent 3: Analyze src/db (data layer)
├─ Subagent 4: Analyze src/ui (frontend)
└─ Subagent 5: Analyze tests/ (test patterns)

Synthesize findings into unified architecture view.

---

Output Templates

Quick Architecture Summary

## Project Overview
- **Language**: [Primary language]
- **Framework**: [Main framework]
- **Architecture**: [Pattern identified]
- **Entry Point**: [Main file]

## Key Modules
| Module | Responsibility | Key Files |
|--------|----------------|-----------|
| [Name] | [What it does] | [Files]   |

## Data Flow
[Request lifecycle diagram]

## Notable Patterns
- [Pattern 1]: [Where/how used]
- [Pattern 2]: [Where/how used]

Onboarding Checklist

## Getting Started
- [ ] Clone and install dependencies
- [ ] Run the app locally
- [ ] Run the test suite
- [ ] Trace one request end-to-end
- [ ] Find where [core feature] is implemented

---

Anti-Patterns to Avoid

1. Analysis Paralysis - Don't try to understand everything before starting 2. Ignoring Tests - Tests often document expected behavior 3. Skipping Config - Configuration reveals deployment context 4. Surface-Only - Don't stop at directory structure 5. Assuming Patterns - Verify patterns, don't assume from naming

---

Quick Reference

SURFACE SCAN:
  entry points → config files → directory structure

DEPENDENCY MAP:
  package manager → import tracing → layer identification

ARCHITECTURE:
  pattern recognition → separation of concerns → abstractions

FLOW TRACING:
  request path → data flow → state management

QUALITY CHECK:
  test coverage → code health → technical debt

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