📦 deps(thirdparty): update snapshots

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+---
+name: open-dynamic-workflows
+description: "Plan, orchestrate, and adversarially verify parallel AI coding agents with a dynamic multi-agent workflow engine."
+category: ai-agents
+risk: critical
+source: community
+source_repo: Suraj1235/open-dynamic-workflows
+source_type: community
+date_added: "2026-06-06"
+author: Suraj1235
+tags: [multi-agent, orchestration, workflow, adversarial-verification, coding-agents]
+tools: [claude, cursor, codex, gemini, antigravity]
+# Optional: declare the upstream license if source_repo is set
+license: "MIT"
+license_source: "https://github.com/Suraj1235/open-dynamic-workflows/blob/main/LICENSE"
+---
+
+# 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.
+
+```bash
+# 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
+
+```bash
+# 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.