Fetches fabro-workflow-factory from aradotso/trending-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/fabro-workflow-factory
Restart Cursor to activate fabro-workflow-factory. Access via /fabro-workflow-factory in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Fabro is an open source AI coding workflow orchestrator written in Rust. It lets you define agent pipelines as Graphviz DOT graphs — with branching, loops, human approval gates, multi-model routing, and cloud sandbox execution — then run them as a persistent service. You define the process; agents execute it; you intervene only where it matters.
Installation
# Via Claude Code (recommended)curl-fsSL https://fabro.sh/install.md | claude
# Via Codexcodex "$(curl-fsSL https://fabro.sh/install.md)"# Via Bashcurl-fsSL https://fabro.sh/install.sh |bash
After installation, run one-time setup and per-project initialization:
# Workflow managementfabro run <workflow.dot># execute a workflowfabro run <workflow.dot>--watch# stream live outputfabro runs # list all runsfabro runs show <run-id># inspect a specific run# Human-in-the-loopfabro approve <run-id># approve a pending gatefabro reject <run-id># reject / revise a pending gate# Sandbox accessfabro ssh<run-id># shell into a running sandboxfabro preview <run-id><port># expose a sandbox port locally# Retrospectivesfabro retro <run-id># view run retrospective (cost, duration, narrative)# Configfabro config # view current configurationfabro config set<key><value># set a config value
Workflow Definition (Graphviz DOT)
Workflows are .dot files using the Graphviz DOT language with Fabro-specific attributes.
Node Types
Shape
Meaning
Mdiamond
Start node
Msquare
Exit node
rectangle (default)
Agent node (LLM turn)
hexagon
Human gate (pauses for approval)
Minimal Hello World
// hello.dotdigraphHelloWorld{graph[goal="Say hello and write a greeting file"model_stylesheet="
* { model: claude-haiku-4-5; }
"]start[shape=Mdiamond,label="Start"]exit[shape=Msquare,label="Exit"]greet[label="Greet",prompt="Write a friendly greeting to hello.txt"]start->greet->exit}
fabro run hello.dot
Multi-Model Routing with Stylesheets
Fabro uses CSS-like model_stylesheet declarations on the graph to route nodes to models. Use classes to target groups of nodes.
digraphPlanImplementReview{graph[goal="Plan, implement, and review a feature"model_stylesheet="
* { model: claude-haiku-4-5; reasoning_effort: low; }
.planning { model: claude-opus-4-5; reasoning_effort: high; }
.coding { model: claude-sonnet-4-5; reasoning_effort: high; }
.review { model: gpt-4o; }
"]start[shape=Mdiamond,label="Start"]exit[shape=Msquare,label="Exit"]plan[label="Plan",class="planning",prompt="Analyze the codebase and write plan.md"]implement[label="Implement",class="coding",prompt="Read plan.md and implement every step"]review[label="Review",class="review",prompt="Cross-review the implementation for bugs and clarity"]start->plan->implement->review->exit}
Use shape=hexagon to pause execution for human approval. Transitions are labeled with [A] (approve) and [R] (revise/reject).
digraphPlanApproveImplement{graph[goal="Plan and implement with human approval"model_stylesheet="
* { model: claude-sonnet-4-5; }
"]start[shape=Mdiamond,label="Start"]exit[shape=Msquare,label="Exit"]plan[label="Plan",prompt="Write a detailed implementation plan to plan.md"]approve[shape=hexagon,label="Approve Plan"]implement[label="Implement",prompt="Read plan.md and implement every step exactly"]start->plan->approveapprove->implement[label="[A] Approve"]approve->plan[label="[R] Revise"]implement->exit}
Approve or reject from the CLI:
fabro runs # find the paused run-idfabro approve <run-id># continue with implementationfabro reject <run-id>--note"Add error handling to the plan"
Loops and Fix Cycles
Use labeled transitions to build automatic retry/fix loops:
digraphImplementAndTest{graph[goal="Implement a feature and fix failing tests automatically"model_stylesheet="
* { model: claude-haiku-4-5; }
.coding { model: claude-sonnet-4-5; reasoning_effort: high; }
"]start[shape=Mdiamond,label="Start"]exit[shape=Msquare,label="Exit"]implement[label="Implement",class="coding",prompt="Implement the feature described in TASK.md"]test[label="Run Tests",prompt="Run the test suite with `cargo test`. Report pass/fail."]fix[label="Fix",class="coding",prompt="Read the test failures and fix the code. Do not change tests."
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
