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workflow-generator

by askuma
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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.

WORKFLOW.html generated for full-stack-fastapi-template

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

Related MCP server: composer-mcp

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):

~/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"]
    }
  }
}

.vscode/mcp.json

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

~/.cursor/mcp.json

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

.zed/settings.json

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

~/.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

Install Server
A
license - permissive license
A
quality
A
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
0dRelease cycle
2Releases (12mo)
Commit activity

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Tools

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