workflow-generator

Scan any project and generate WORKFLOW.html — a dark-mode visual system diagram showing every component, how they talk to each other, and where your throughput ceiling actually is.

Works with Python, Node.js, Go, and mixed projects. No external dependencies for the core scanner. Vendored and generated directories (node_modules, venv, site-packages, dist, …) are never scanned, and capacity figures are clearly labeled as static-analysis estimates.

Live demo → — generated from fastapi/full-stack-fastapi-template, unmodified.

(real CLI output, unscripted — static screenshot if you'd rather not autoplay)

What it produces

Every generated page contains:

Section	What you get
Stat row	Workers · Concurrent I/O ceiling · Semaphore limit · Rate limit · Practical throughput
Architecture diagram	Layered flow: external sources → gateway → API → queues → AI → storage
Data flow cards	Write path, read/query path, background jobs — inferred from what's detected
Concurrency table	Every layer: model · ceiling · limiting factor
Bottleneck analysis	Ranked CRITICAL → LOW with mitigation notes

What it detects

Category	Examples
API frameworks	FastAPI, Flask, Django, Express, Nest.js, Gin
Gateways	nginx, Caddy, Traefik (with rate limits + worker_connections)
LLM providers	OpenAI, Anthropic Claude, Cohere, AWS Bedrock
Vector stores	Qdrant, Pinecone, Weaviate, ChromaDB, pgvector, FAISS, Milvus
Databases	PostgreSQL, MySQL, MongoDB, SQLite, Redis
Queues	Celery, BullMQ, Kafka, RabbitMQ, RQ, AWS SQS
Async primitives	asyncio.Semaphore, run_in_executor, asyncio.gather, asyncio.Lock
Workers	--workers N (uvicorn/gunicorn), replicas: (docker-compose), PM2 instances
External sources	Jira, Azure DevOps, Slack, GitHub, Stripe, Salesforce, Twilio
Evaluation	TruLens, RAGAS, LangSmith

---

Install

pip (CLI + MCP server)

pip install workflow-generator-mcp

workflow-generator . WORKFLOW.html       # CLI: scan and write the report
workflow-generator-mcp                    # stdio MCP server

With pip installed, any MCP host config reduces to:

{
  "mcpServers": {
    "workflow-generator": { "command": "workflow-generator-mcp" }
  }
}

Claude Code (skill)

mkdir -p ~/.claude/skills
git clone https://github.com/askuma/workflow-generator.git ~/.claude/skills/workflow-generator

Then in any Claude Code session:

/workflow-generator
/workflow-generator /path/to/project

MCP server (Claude Desktop, VS Code, Cursor, Zed, Windsurf, Continue)

1. Install the dependency:

pip install mcp

2. Add to your MCP host config (replace ~ with your actual home path):

Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json (Mac)
%APPDATA%\Claude\claude_desktop_config.json (Windows)

{
  "mcpServers": {
    "workflow-generator": {
      "command": "python3",
      "args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
    }
  }
}

VS Code

.vscode/mcp.json

{
  "servers": {
    "workflow-generator": {
      "type": "stdio",
      "command": "python3",
      "args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
    }
  }
}

Cursor

~/.cursor/mcp.json

{
  "mcpServers": {
    "workflow-generator": {
      "command": "python3",
      "args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
    }
  }
}

Zed

.zed/settings.json

{
  "context_servers": {
    "workflow-generator": {
      "command": {
        "path": "python3",
        "args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
      }
    }
  }
}

Windsurf

~/.windsurf/mcp_config.json

{
  "mcpServers": {
    "workflow-generator": {
      "command": "python3",
      "args": ["~/.claude/skills/workflow-generator/mcp/server.py"]
    }
  }
}

3. Restart your tool, then ask:

generate a workflow diagram for this project
how many concurrent requests can this handle?
show me the system architecture

MCP tools exposed:

• generate_workflow — scans project, writes WORKFLOW.html, optionally opens in browser
• analyze_workflow — returns structured JSON summary (no file written)

Command line (standalone)

No install needed beyond Python 3.8+:

python3 ~/.claude/skills/workflow-generator/scripts/analyze.py . ~/WORKFLOW.html
# then open ~/WORKFLOW.html

---

Example output (terminal)

Written: /your/project/WORKFLOW.html
Framework: FastAPI · Workers: 8 · Concurrent I/O: ~800
Practical throughput: ~50–200 req/min
Bottleneck: OpenAI (LLM latency 3–30s per call)
Gateway: nginx · 2 rate limit zone(s)
LLM: OpenAI · eval: TruLens RAG Triad
Storage: Qdrant, Redis
External sources: Jira, Azure DevOps, Slack

---

Repo layout

workflow-generator/
├── SKILL.md              ← Claude Code skill definition
├── INSTALL.md            ← detailed per-platform install guide
├── scripts/
│   └── analyze.py        ← core scanner + HTML renderer (stdlib only)
├── mcp/
│   ├── server.py         ← MCP stdio server
│   └── requirements.txt  ← pip install mcp
└── copilot/
    ├── index.js          ← GitHub Copilot Extension (Express)
    ├── package.json
    └── openai_function.json

---

License

MIT