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Grok Bot+18 agentsMulti-Agent Orchestrator
It maps your workflow into an agent graph with clear boundaries, failure modes, and fallback strategies — before you write a single tool call.
$12.99
Multi-Agent Orchestrator
Example session with this skill installed
I want to use parallel coding agents to convert 40 React class components to function components with hooks and add a test for each. The repo uses Jest and React Testing Library, and about 10 of the components share a Redux connect() pattern.
- Read your context and instructions
- Compiled the multi-agent orchestrator
Orchestration plan: one lead agent defines the pattern, then parallel workers convert independent components in batches, with a single merge gate.
What runs in parallel
| Work | Parallel? | Reason |
|---|---|---|
Pattern and shared hook for the 10 connect() components | No | Everything else depends on it |
| 30 standalone components | Yes, 3 workers x 10 | No shared files |
| 10 Redux components | Yes, after pattern merges | Reuse the shared hook |
Worker brief (template)
Goal: convert these components to function components with hooks.
Files: <list of 10 paths> (touch no other files)
Rules: keep props and behaviour identical; one RTL test per component
covering render and main interaction.
Done when: jest passes for your files, eslint clean.
Return: changed files, any component you could not convert and why.
Handoffs
- Lead agent hands workers the conversion example and the
useConnectedhook.
Merge rules
- One branch per worker; rebase on main before merging.
- A worker that edits a file outside its list gets its change rejected.
- Merge in order: pattern, then standalone batches, then Redux batch.
Stop conditions
- A worker stops after two failed attempts on one component and reports it.
- The whole run pauses if the full test suite fails after any merge.
Next steps
- Generate the three file lists, grouped by folder.
- Have the lead convert one
connect()component first as the reference.
Connects securely to your tools. The creator never sees your data.
About this skill
The hard part of multi-agent work isn't spawning agents — it's deciding what deserves parallelism, what each agent needs to not duplicate work, and how failures cascade when one agent in a pipeline returns bad output. This skill designs the full orchestration: which tasks benefit from parallelism vs. which are serial dependencies, what context each subagent needs and nothing more, how results are validated before being handed downstream, and where human checkpoints belong. It maps your workflow into an agent graph with clear boundaries, failure modes, and fallback strategies — before you write a single tool call. Use it when you're building multi-step Claude pipelines, Claude Code subagent flows, or any system where one model's output feeds another. Give it your goal, your available tools, and your constraints; it returns an architecture you can implement without discovering the coordination problems at runtime.
- Does not write deployment code
- Not for single-agent tasks
- Logic based on architectural patterns, not runtime execution monitoring.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
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- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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