
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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| Installs | 6 |
|---|---|
| repo stars | ★ 250 |
| Last updated | January 10, 2026 |
| Repository | cloudai-x/opencode-workflow ↗ |
What it does
Helps with ai & agent building tasks.
Files
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/ → ConfigurationInitial 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
| File | Ecosystem | Key Sections |
|---|---|---|
package.json | Node.js | dependencies, devDependencies |
requirements.txt / pyproject.toml | Python | direct dependencies |
go.mod | Go | require blocks |
Cargo.toml | Rust | dependencies |
pom.xml / build.gradle | Java | dependencies |
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
| Pattern | Indicators | Typical Structure |
|---|---|---|
| MVC | controllers/, models/, views/ | Clear separation of concerns |
| Hexagonal | ports/, adapters/, domain/ | Dependency inversion |
| Microservices | services/, docker-compose | Independent deployable units |
| Monolith | Single large app, shared DB | Everything in one deployment |
| Serverless | functions/, 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 APIState 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
| Indicator | Good Sign | Warning Sign |
|---|---|---|
| Test coverage | >70% coverage | No tests, or tests ignored |
| Dependencies | Recent versions | Major versions behind |
| Documentation | README updated | Stale or missing docs |
| Build time | Under 2 minutes | Over 10 minutes |
| Error handling | Consistent patterns | Swallowed 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