sickn33 Open Dynamic Workflows
Overview
Open Dynamic Workflows (ODW) is an open-source dynamic multi-agent workflow engine for AI coding agents such as OpenCode, Codex, Antigravity, and VS Code. It lets you plan a task, orchestrate multiple agents working in parallel, and adversarially verify their output before it lands. ODW ships a Codex/Antigravity skill folder (SKILL.md plus a daemon bridge) and an OpenCode plugin, and it is bring-your-own-model (Anthropic, OpenAI-compatible, or Ollama). This skill is adapted from the community project at Suraj1235/open-dynamic-workflows.
When to Use This Skill
- Use when you need to decompose a coding task into independent subtasks and run multiple agents in parallel.
- Use when working across more than one AI coding tool (OpenCode, Codex, Antigravity, VS Code) and want a single orchestration layer.
- Use when the user asks for adversarial review or verification of agent-generated changes before merging.
How It Works
Step 1: Plan
ODW takes a high-level goal and produces a dynamic workflow graph of subtasks, identifying which can run in parallel and which have dependencies.
Step 2: Orchestrate
The engine dispatches subtasks to parallel agents through the OpenCode plugin or the Codex/Antigravity daemon bridge, using your configured model provider (Anthropic, OpenAI-compatible, or Ollama).
Step 3: Adversarially Verify
Completed work is routed through an adversarial verification pass that challenges the output before results are synthesized and returned.
Examples
Example 1: Run a parallel workflow
ODW is installed from source (clone the repo, then npm install). The CLI is
odw-daemon — run it as npm run odw -- <args> from inside the repo, or as
npx odw-daemon <args> / a global odw-daemon if you link the bin.
# Configure your model provider (bring-your-own-model)
export ANTHROPIC_API_KEY=... # or an OpenAI-compatible / Ollama endpoint
# One-time setup: generate ~/.odw/config.json
npm run setup
# Start the local workflow daemon (once)
npm run odw -- start
# Plan, orchestrate, and verify a task across parallel agents
npm run odw -- run --prompt "refactor the auth module and add tests"
Example 2: Use the Codex/Antigravity skill bridge
# ODW ships a SKILL.md + daemon bridge consumed by Codex / Antigravity.
# Start the daemon, then run a saved orchestration script through it:
npm run odw -- start
npm run odw -- run --script examples/workflows/studio-prime.workflow.js --cwd .
Best Practices
- ✅ Scope each subtask so agents can run without shared state.
- ✅ Keep the adversarial verification pass enabled before merging agent output.
- ❌ Don't run interdependent subtasks in parallel without declaring their dependencies.
- ❌ Don't commit provider API keys; use environment variables or a secrets manager.
Limitations
- This skill does not replace environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, or safety boundaries are missing.
Security & Safety Notes
- ODW executes agent-generated code and shell commands; run it only in an authorized, local, or sandboxed environment.
- Model provider credentials (Anthropic / OpenAI-compatible / Ollama) must be supplied via environment variables, never committed to source.
- Review adversarial-verification output before applying changes to a production branch.
Common Pitfalls
- Problem: Parallel agents collide on the same files. Solution: Give each subtask exclusive file/module ownership and run conflicting tasks sequentially.
Related Skills
@multi-agent-orchestration- When coordinating multiple agents on one goal.@code-review- How adversarial verification complements human review.