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Grok
Grok Bot+18 agentsagent workflow architect
Transform disconnected specialist agents into a coordinated pipeline with defined handoffs, gates, and recovery paths.
$5
agent workflow architect
Example session with this skill installed
Design a workflow for an automated blog engine. I have a 'Researcher' agent, a 'Writer' agent, and an 'SEO-Auditor' agent. The final output must be a Markdown file ready for Hugo.
- Read your context and instructions
- Compiled the agent workflow architect
- Generated the UI component
I have mapped the coordination logic for your automated blog engine. The workflow prioritizes factual grounding and SEO compliance before the final export.
Automated Blog Engine Specification
Workflow Topology
graph TD
A[Start: Topic/Keywords] --> B(Research Stage)
B --> C{Quality Gate 1}
C -- Fail --> B
C -- Pass --> D(Drafting Stage)
D --> E(SEO Audit Stage)
E --> F{Quality Gate 2}
F -- Revision Needed --> D
F -- Approved --> G[Final Markdown Export]
Stage Definitions
| Stage | Owner | Input | Output | Success Criteria |
|---|---|---|---|---|
| Research | Researcher | Primary Keywords | Verified Fact Sheet | Min 5 cited sources; no dead links. |
| Drafting | Writer | Fact Sheet | Hugo-formatted Draft | Front-matter present; matches Fact Sheet. |
| SEO Audit | SEO-Auditor | Markdown Draft | Audit Report | Score > 80; keyword density 1-2%. |
Handoff & Shared Context
- Shared Context Object: Contains
target_keywords,source_links, andaudit_score.
Rejection Loop: If SEO-Auditor fails the draft, it must provide a diff of missing keywords to the Writer for the revision stage.
*
Final Acceptance: Final artifact must be a single .md file with Hugo-compatible YAML front-matter (date, title, tags).
Next steps
- Define the specific prompt templates for the Researcher to ensure source URLs are captured.
- Configure the SEO-Auditor threshold for the "Revision Needed" branch.
- Implement the orchestration logic in your preferred framework using these state transitions.
agent-workflow-architect.tsx
TSX · React component
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
Stop guessing how your multi-agent system should actually communicate. This skill solves the "black box" problem of agent orchestration by generating concrete, low-level workflow specifications. It defines the exact handoffs, shared context, and quality gates required to turn a collection of disconnected AI tools or specialist agents into a functional, reliable pipeline.
What it does
- Architects coordination by defining how specialist agents interact, transfer data, and maintain shared state.
- Maps stage ownership to ensure every task has an accountable role, specific entry conditions, and defined outputs.
- Designs quality gates that implement evidence-based checks and automated rejection loops to prevent hallucination propagation.
- Specifies handoff protocols including artifact versions, required context, and resolution steps for incompatible data formats.
- Formalizes recovery paths with retry limits and escalation triggers for when agents hit API failures or logic loops.
How it works
- Define outcomes by providing the final goal, available specialist agents, and existing tool constraints.
- Review dependencies to identify which tasks can run in parallel and which require linear integration.
- Map the logic using the skill to generate a structured specification document detailing every transition and gate.
- Walkthrough simulation to verify the path from input to acceptance, identifying potential blockers before implementation.
Frameworks & tools
Works with any agentic framework including LangGraph, AutoGen, CrewAI, or custom Pydantic-based orchestrators. Outputs are framework-agnostic Markdown and Mermaid diagrams.
Why this beats prompting it yourself
Generic prompts often result in vague "work together" instructions that fail when data formats don't match or agents loop indefinitely. This skill enforces strict architectural rigor, specifically addressing state management, race conditions in parallel tasks, and error handling boundaries that manual prompting usually misses.
Use cases
- Drafting a multi-stage RAG pipeline with separate retrieval, synthesis, and fact-checking agents.
- Designing a software development lifecycle (SDLC) workflow for autonomous PR reviews and testing.
- Organizing marketing content chains that require handoffs between research, drafting, and SEO optimization.
- Building complex data extraction flows that need human-in-the-loop (HITL) quality gates.
Known limitations
Does not execute the workflow or write the actual orchestration code. Requires pre-defined specialist agent capabilities to be useful.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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