Archify Diagrams
tt-a1i/archify
Creates interactive architecture, workflow, sequence, data-flow and lifecycle diagrams as standalone HTML with inline SVG, themes and image or video export.
Guides systematic project analysis, codebase exploration, and architecture pattern recognition.
Install Analyzing Projects
$ npx skills add CloudAI-X/opencode-workflow --skill analyzing-projects -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CloudAI-X/opencode-workflow analyzing-projects --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/CloudAI-X/opencode-workflow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyzing-projects .claude/skills/analyzing-projects && rm -rf skills-srcUse ~/.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/
Install the "analyzing-projects" agent skill from https://github.com/CloudAI-X/opencode-workflow/tree/main/skills/analyzing-projects into .claude/skills/analyzing-projects/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-projects", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/CloudAI-X/opencode-workflow/tree/main/skills/analyzing-projectsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add CloudAI-X/opencode-workflow --skill analyzing-projects -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CloudAI-X/opencode-workflow analyzing-projects --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CloudAI-X/opencode-workflow.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/analyzing-projects .agents/skills/analyzing-projects && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analyzing-projects" agent skill from https://github.com/CloudAI-X/opencode-workflow/tree/main/skills/analyzing-projects into .agents/skills/analyzing-projects/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-projects", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CloudAI-X/opencode-workflow --skill analyzing-projects -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CloudAI-X/opencode-workflow analyzing-projects --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CloudAI-X/opencode-workflow.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/analyzing-projects .cursor/skills/analyzing-projects && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analyzing-projects" agent skill from https://github.com/CloudAI-X/opencode-workflow/tree/main/skills/analyzing-projects into .cursor/skills/analyzing-projects/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-projects", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/CloudAI-X/opencode-workflow.git --path skills/analyzing-projects--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add CloudAI-X/opencode-workflow --skill analyzing-projects -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CloudAI-X/opencode-workflow analyzing-projects --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CloudAI-X/opencode-workflow.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/analyzing-projects .gemini/skills/analyzing-projects && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analyzing-projects" agent skill from https://github.com/CloudAI-X/opencode-workflow/tree/main/skills/analyzing-projects into .gemini/skills/analyzing-projects/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-projects", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install CloudAI-X/opencode-workflow analyzing-projectsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add CloudAI-X/opencode-workflow --skill analyzing-projects -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CloudAI-X/opencode-workflow.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/analyzing-projects .github/skills/analyzing-projects && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analyzing-projects" agent skill from https://github.com/CloudAI-X/opencode-workflow/tree/main/skills/analyzing-projects into .github/skills/analyzing-projects/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-projects", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CloudAI-X/opencode-workflow --skill analyzing-projects -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CloudAI-X/opencode-workflow analyzing-projects --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CloudAI-X/opencode-workflow.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/analyzing-projects .opencode/skills/analyzing-projects && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analyzing-projects" agent skill from https://github.com/CloudAI-X/opencode-workflow/tree/main/skills/analyzing-projects into .opencode/skills/analyzing-projects/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-projects", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
analyzing-projects- GitHub stars
- 275
- Used in
- 1 other repo
- Token cost
- ~1.8k tokens
- SKILL.md length
- 497 words
- Files
- 1
- Skills in repo
- 7
- Repo updated
- First seen
- Licence
- MIT
At a glance
Guides systematic project analysis, codebase exploration, and architecture pattern recognition.
- Works in 5 steps: Surface Scan → Dependency Mapping → Architecture Recognition → …
- Understanding new codebases
- SKILL.md covers When to Use This Skill, Core Analysis Framework, Phase 1: Surface Scan and Phase 2: Dependency Mapping, plus 8 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Analyzing Projects is an agent skill from CloudAI-X/opencode-workflow. Guides systematic project analysis, codebase exploration, and architecture pattern recognition. Use when understanding new codebases, onboarding to projects, or investigating system structure.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: opencode
It sits in Development, covering Software architecture. The licence is MIT.
When your agent uses it
- Understanding new codebases
- Onboarding to projects
- Investigating system structure
Example prompts
- “Use the analyzing-projects skill to guide systematic project analysis, codebase exploration, and architecture pattern recognition”
- “/analyzing-projects”
Requirements
- Python 3
- Node.js
- Docker
- Compatibility (from SKILL.md): opencode
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 0128ca6. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
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.
Compatibility
opencode
From compatibility in the SKILL.md frontmatter.
Context cost
Analyzing Projects loads about 1.8k tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 497 words of instructions outside code blocks.
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 passed
The automated check found no risky patterns in SKILL.md.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from CloudAI-X/opencode-workflow at commit 0128ca6, republished under its MIT licence (© CloudAI-X). 497 words, ~1,815 tokens.
Download SKILL.mdSave it as
.claude/skills/analyzing-projects/SKILL.md (or your agent's skills folder).- name
- analyzing-projects
- description
- Guides systematic project analysis, codebase exploration, and architecture pattern recognition. Use when understanding new codebases, onboarding to projects, or investigating system structure.
- compatibility
- opencode
- license
- MIT
- metadata.category
- exploration
- metadata.audience
- developers
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 debtPhase 1: Surface Scan
Entry Point Discovery
Start by identifying how the application launches:
Look for standard entry files:
main.*,index.*,app.*,server.*cmd/directory (Go)src/main/(Java)bin/scripts
Check configuration files:
package.json(scripts.start, main)Makefile,Taskfile- Docker/Compose files
- CI/CD configs (
.github/workflows/)
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
| 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
Trace imports from entry points
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)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?
Show full SKILL.md (217 more words)Show less
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
- Find where the bug manifests
- Trace back to the root cause
- Understand the affected area only
- Check for related tests
Goal: Add a New Feature
- Find similar existing features
- Understand the patterns they use
- Map the modules that need changes
- Identify integration points
Goal: Full Codebase Understanding
- Complete all 5 phases
- Document architecture decisions
- Create a mental map of key flows
- 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 implementedAnti-Patterns to Avoid
- Analysis Paralysis - Don't try to understand everything before starting
- Ignoring Tests - Tests often document expected behavior
- Skipping Config - Configuration reveals deployment context
- Surface-Only - Don't stop at directory structure
- 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© CloudAI-X, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/analyzing-projects of CloudAI-X/opencode-workflow.
Open the folder on GitHubat commit 0128ca6
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in CloudAI-X/opencode-workflow, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Analyzing Projects next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analyzing Projects this skillCloudAI-X/opencode-workflow | 275 | 1 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Archify Diagramstt-a1i/archify | 79k | — | ~2.9k | Automated safety check: Pass | MIT | |
| Electron Multi-Process ArchitectureiOfficeAI/AionUi | 33k | 1 repos | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Backend Code Reviewlanggenius/dify | 158k | — | ~676 | Automated safety check: Pass | Custom licence | |
| Dark Architecture Diagram BuilderCocoon-AI/architecture-diagram-generator | 7.4k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Code Graph Mermaid Diagramstrailofbits/skills | 7.4k | 1 repos | ~1.7k | Automated safety check: Pass | CC-BY-SA-4.0 |
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Related
Categories
Questions about Analyzing Projects
What does Analyzing Projects do?
Guides systematic project analysis, codebase exploration, and architecture pattern recognition. Analyzing Projects is an agent skill from CloudAI-X/opencode-workflow. Guides systematic project analysis, codebase exploration, and architecture pattern recognition.
When should I use Analyzing Projects?
Analyzing Projects fits situations like: understanding new codebases; onboarding to projects; investigating system structure.
How do I install Analyzing Projects in Claude Code?
Run `npx skills add CloudAI-X/opencode-workflow --skill analyzing-projects -a claude-code`. Or copy the skill folder (skills/analyzing-projects in CloudAI-X/opencode-workflow) into .claude/skills/analyzing-projects in your project. Claude Code loads it when a task matches its description.
How do I install Analyzing Projects in Codex?
Run `npx skills add CloudAI-X/opencode-workflow --skill analyzing-projects -a codex`. Or copy the skill folder (skills/analyzing-projects in CloudAI-X/opencode-workflow) into .agents/skills/analyzing-projects in your project. Codex loads it when a task matches its description.
Can I use Analyzing Projects 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 CloudAI-X/opencode-workflow --skill analyzing-projects -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-projects, .gemini/skills/analyzing-projects, .github/skills/analyzing-projects and .opencode/skills/analyzing-projects in your project.
What does Analyzing Projects need to run?
SKILL.md names no scripts, command-line tools or credentials: Analyzing Projects is instructions for the agent only. Our summary lists: Python 3; Node.js; Docker. Compatibility (from SKILL.md): opencode.
Does Analyzing Projects 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 Analyzing Projects safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Analyzing Projects use?
Analyzing Projects is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Analyzing Projects use?
About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Analyzing Projects?
Skills that share tags, products or a category with Analyzing Projects: Archify Diagrams (tt-a1i/archify, 79k stars), Electron Multi-Process Architecture (iOfficeAI/AionUi, 33k stars), Backend Code Review (langgenius/dify, 158k stars) and Dark Architecture Diagram Builder (Cocoon-AI/architecture-diagram-generator, 7.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Analyzing Projects?
CloudAI-X (a GitHub user) maintains it in CloudAI-X/opencode-workflow, which has 275 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on January 10, 2026.
Source: CloudAI-X/opencode-workflow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.