102 lines
4.4 KiB
Markdown
102 lines
4.4 KiB
Markdown
---
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name: open-dynamic-workflows
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description: "Plan, orchestrate, and adversarially verify parallel AI coding agents with a dynamic multi-agent workflow engine."
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category: ai-agents
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risk: critical
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source: community
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source_repo: Suraj1235/open-dynamic-workflows
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source_type: community
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date_added: "2026-06-06"
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author: Suraj1235
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tags: [multi-agent, orchestration, workflow, adversarial-verification, coding-agents]
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tools: [claude, cursor, codex, gemini, antigravity]
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# Optional: declare the upstream license if source_repo is set
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license: "MIT"
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license_source: "https://github.com/Suraj1235/open-dynamic-workflows/blob/main/LICENSE"
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---
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# Open Dynamic Workflows
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## Overview
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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`.
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## When to Use This Skill
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- Use when you need to decompose a coding task into independent subtasks and run multiple agents in parallel.
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- Use when working across more than one AI coding tool (OpenCode, Codex, Antigravity, VS Code) and want a single orchestration layer.
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- Use when the user asks for adversarial review or verification of agent-generated changes before merging.
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## How It Works
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### Step 1: Plan
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ODW takes a high-level goal and produces a dynamic workflow graph of subtasks, identifying which can run in parallel and which have dependencies.
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### Step 2: Orchestrate
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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).
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### Step 3: Adversarially Verify
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Completed work is routed through an adversarial verification pass that challenges the output before results are synthesized and returned.
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## Examples
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### Example 1: Run a parallel workflow
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ODW is installed from source (clone the repo, then `npm install`). The CLI is
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`odw-daemon` — run it as `npm run odw -- <args>` from inside the repo, or as
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`npx odw-daemon <args>` / a global `odw-daemon` if you link the bin.
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```bash
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# Configure your model provider (bring-your-own-model)
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export ANTHROPIC_API_KEY=... # or an OpenAI-compatible / Ollama endpoint
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# One-time setup: generate ~/.odw/config.json
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npm run setup
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# Start the local workflow daemon (once)
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npm run odw -- start
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# Plan, orchestrate, and verify a task across parallel agents
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npm run odw -- run --prompt "refactor the auth module and add tests"
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```
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### Example 2: Use the Codex/Antigravity skill bridge
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```bash
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# ODW ships a SKILL.md + daemon bridge consumed by Codex / Antigravity.
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# Start the daemon, then run a saved orchestration script through it:
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npm run odw -- start
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npm run odw -- run --script examples/workflows/studio-prime.workflow.js --cwd .
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```
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## Best Practices
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- ✅ Scope each subtask so agents can run without shared state.
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- ✅ Keep the adversarial verification pass enabled before merging agent output.
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- ❌ Don't run interdependent subtasks in parallel without declaring their dependencies.
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- ❌ Don't commit provider API keys; use environment variables or a secrets manager.
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## Limitations
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- This skill does not replace environment-specific validation, testing, or expert review.
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- Stop and ask for clarification if required inputs, permissions, or safety boundaries are missing.
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## Security & Safety Notes
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- ODW executes agent-generated code and shell commands; run it only in an authorized, local, or sandboxed environment.
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- Model provider credentials (Anthropic / OpenAI-compatible / Ollama) must be supplied via environment variables, never committed to source.
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- Review adversarial-verification output before applying changes to a production branch.
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## Common Pitfalls
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- **Problem:** Parallel agents collide on the same files.
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**Solution:** Give each subtask exclusive file/module ownership and run conflicting tasks sequentially.
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## Related Skills
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- `@multi-agent-orchestration` - When coordinating multiple agents on one goal.
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- `@code-review` - How adversarial verification complements human review.
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