fabro-workflow-factory

name	fabro-workflow-factory
description	Skill for using Fabro, the open source AI coding workflow orchestrator that lets you define agent pipelines as Graphviz DOT graphs with human gates, multi-model routing, and cloud sandboxes.
triggers	["set up fabro for my project","create a fabro workflow","define an ai agent pipeline with human approval","run coding agents with fabro","configure multi-model routing in fabro","write a dot graph workflow for fabro","add human-in-the-loop gates to my ai workflow","orchestrate ai agents with fabro"]

Fabro Workflow Factory

Skill by ara.so — Daily 2026 Skills collection.

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 Codex
codex "$(curl -fsSL https://fabro.sh/install.md)"

# Via Bash
curl -fsSL https://fabro.sh/install.sh | bash

After installation, run one-time setup and per-project initialization:

fabro install      # global one-time setup
cd my-project
fabro init         # per-project setup (creates .fabro/ config)

---

Key CLI Commands

# Workflow management
fabro run <workflow.dot>          # execute a workflow
fabro run <workflow.dot> --watch  # stream live output
fabro runs                        # list all runs
fabro runs show <run-id>          # inspect a specific run

# Human-in-the-loop
fabro approve <run-id>            # approve a pending gate
fabro reject <run-id>             # reject / revise a pending gate

# Sandbox access
fabro ssh <run-id>                # shell into a running sandbox
fabro preview <run-id> <port>     # expose a sandbox port locally

# Retrospectives
fabro retro <run-id>              # view run retrospective (cost, duration, narrative)

# Config
fabro config                      # view current configuration
fabro 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.dot
digraph HelloWorld {
    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.

digraph PlanImplementReview {
    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
}

Supported Model Stylesheet Properties

model: <model-id>           # e.g. claude-sonnet-4-5, gpt-4o, gemini-2-flash
reasoning_effort: low|medium|high
provider: anthropic|openai|google

---

Human Gates (Approval Nodes)

Use shape=hexagon to pause execution for human approval. Transitions are labeled with [A] (approve) and [R] (revise/reject).

digraph PlanApproveImplement {
    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 -> approve
    approve -> implement [label="[A] Approve"]
    approve -> plan      [label="[R] Revise"]
    implement -> exit
}

Approve or reject from the CLI:

fabro runs                          # find the paused run-id
fabro approve <run-id>              # continue with implementation
fabro reject <run-id> --note "Add error handling to the plan"

---

Loops and Fix Cycles

Use labeled transitions to build automatic retry/fix loops:

digraph ImplementAndTest {
    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."]

    start -> implement -> test
    test -> exit [label="[P] Pass"]
    test -> fix  [label="[F] Fail"]
    fix  -> test
}

---

Parallel Nodes

Run multiple agent nodes concurrently by forking edges from a single source:

digraph ParallelReview {
    graph [
        goal="Implement then review from multiple perspectives in parallel"
        model_stylesheet="
            *         { model: claude-haiku-4-5; }
            .coding   { model: claude-sonnet-4-5; }
            .critique { model: gpt-4o; }
        "
    ]

    start     [shape=Mdiamond, label="Start"]
    exit      [shape=Msquare,  label="Exit"]

    implement [label="Implement",      class="coding",
               prompt="Implement the task in TASK.md"]
    sec_review  [label="Security Review",  class="critique",
                 prompt="Review the implementation for security issues"]
    perf_review [label="Perf Review",      class="critique",
                 prompt="Review the implementation for performance issues"]
    summarize   [label="Summarize",
                 prompt="Combine the security and performance reviews into REVIEW.md"]

    start -> implement
    implement -> sec_review
    implement -> perf_review
    sec_review  -> summarize
    perf_review -> summarize
    summarize -> exit
}

---

Variables and Dynamic Prompts

Use {variable} interpolation in prompts. Pass variables at run time:

digraph FeatureWorkflow {
    graph [
        goal="Implement {feature_name} from the spec"
        model_stylesheet="* { model: claude-sonnet-4-5; }"
    ]

    start [shape=Mdiamond, label="Start"]
    exit  [shape=Msquare,  label="Exit"]

    implement [label="Implement {feature_name}",
               prompt="Read specs/{feature_name}.md and implement the feature completely."]

    start -> implement -> exit
}

fabro run feature.dot --var feature_name=oauth-login

---

Cloud Sandboxes (Daytona)

To run agents in isolated cloud VMs instead of locally, configure a Daytona sandbox:

fabro config set sandbox.provider daytona
fabro config set sandbox.api_key $DAYTONA_API_KEY
fabro config set sandbox.region us-east-1

Then add sandbox config to your workflow graph:

digraph SandboxedWorkflow {
    graph [
        goal="Implement and test in an isolated environment"
        sandbox="daytona"
        model_stylesheet="* { model: claude-sonnet-4-5; }"
    ]

    start [shape=Mdiamond, label="Start"]
    exit  [shape=Msquare,  label="Exit"]

    implement [label="Implement", prompt="Implement the feature in TASK.md"]
    test      [label="Test",      prompt="Run the full test suite and report results"]

    start -> implement -> test -> exit
}

fabro run sandboxed.dot          # spins up cloud VM, runs workflow, tears it down
fabro ssh <run-id>               # shell into the running sandbox for debugging
fabro preview <run-id> 3000      # forward sandbox port 3000 locally

---

Git Checkpointing

Fabro automatically commits code changes and execution metadata to Git branches at each stage. To inspect or resume:

fabro runs show <run-id>         # see branch names per stage
git checkout fabro/<run-id>/implement   # inspect the code at a specific stage
git diff fabro/<run-id>/plan fabro/<run-id>/implement  # diff between stages

---

Retrospectives

After every run, Fabro generates a retrospective with cost, duration, files changed, and an LLM-written narrative:

fabro retro <run-id>

Example output:

Run: implement-oauth-2024
Duration:  4m 32s
Cost:      $0.043
Files:     src/auth.rs (+142), src/lib.rs (+8), tests/auth_test.rs (+67)

Narrative:
  The agent successfully implemented OAuth2 PKCE flow. It created the auth
  module, integrated with the existing middleware, and added integration tests.
  One fix loop was needed after the token refresh test failed.

---

REST API and SSE Streaming

Fabro runs an API server for programmatic use:

fabro serve --port 8080

Trigger a run via API

curl -X POST http://localhost:8080/api/runs \
  -H "Content-Type: application/json" \
  -d '{
    "workflow": "workflows/plan-implement.dot",
    "variables": { "feature_name": "dark-mode" }
  }'

Stream run events via SSE

curl -N http://localhost:8080/api/runs/<run-id>/events

Approve a gate via API

curl -X POST http://localhost:8080/api/runs/<run-id>/approve \
  -H "Content-Type: application/json" \
  -d '{ "decision": "approve" }'

---

Environment Variables

# Required — at least one LLM provider key
export ANTHROPIC_API_KEY=...
export OPENAI_API_KEY=...
export GOOGLE_API_KEY=...

# Optional — cloud sandboxes
export DAYTONA_API_KEY=...

# Optional — Fabro API server auth
export FABRO_API_TOKEN=...

---

Project Structure Convention

my-project/
├── .fabro/               # Fabro config (created by `fabro init`)
│   └── config.toml
├── workflows/            # Your DOT workflow definitions
│   ├── plan-implement.dot
│   ├── fix-loop.dot
│   └── ensemble-review.dot
├── specs/                # Natural language specs referenced by prompts
│   └── feature-name.md
└── src/                  # Your actual source code

---

Common Patterns

Pattern: Spec-driven implementation

digraph SpecDriven {
    graph [
        goal="Implement from spec with LLM-as-judge verification"
        model_stylesheet="
            * { model: claude-sonnet-4-5; }
        "
    ]

    start  [shape=Mdiamond, label="Start"]
    exit   [shape=Msquare,  label="Exit"]

    implement [label="Implement",
               prompt="Read specs/feature.md and implement it completely"]
    judge     [label="Judge",
               prompt="Compare the implementation against specs/feature.md. Does it conform? Reply PASS or FAIL with reasons."]
    fix       [label="Fix",
               prompt="Read the judge feedback and fix the implementation"]

    start -> implement -> judge
    judge -> exit [label="[P] PASS"]
    judge -> fix  [label="[F] FAIL"]
    fix -> judge
}

Pattern: Cheap draft, expensive refine

digraph CheapThenExpensive {
    graph [
        goal="Draft cheaply, refine with a frontier model"
        model_stylesheet="
            *        { model: claude-haiku-4-5; }
            .premium { model: claude-opus-4-5; reasoning_effort: high; }
        "
    ]

    start  [shape=Mdiamond, label="Start"]
    exit   [shape=Msquare,  label="Exit"]

    draft  [label="Draft",  prompt="Write a first draft implementation of the task"]
    refine [label="Refine", class="premium",
            prompt="Review and substantially improve the draft for correctness and clarity"]

    start -> draft -> refine -> exit
}

---

Troubleshooting

fabro: command not found

• Re-run the install script and ensure ~/.local/bin (or the install prefix) is on your $PATH.
• Try source ~/.bashrc or source ~/.zshrc after installation.

Agent gets stuck in a loop

• Add a maximum iteration guard: use a counter variable and a conditional transition to force exit after N iterations.
• Check your prompt — ambiguous exit conditions cause looping.

Human gate never pauses

• Confirm the node uses shape=hexagon, not just a label containing "approve".
• Check fabro runs show <run-id> to confirm the run reached that node.

Sandbox fails to start

• Verify DAYTONA_API_KEY is set and valid.
• Run fabro config to confirm sandbox.provider is set to daytona.
• Check fabro runs show <run-id> for sandbox error details.

Model not found / API error

• Ensure the correct provider API key is exported (ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.).
• Check the model: value in your stylesheet matches the provider's exact model ID.

Run exits immediately without doing work

• Verify the DOT file has a valid path from start (shape=Mdiamond) to exit (shape=Msquare).
• Run dot -Tsvg workflow.dot -o workflow.svg to visually inspect the graph for disconnected nodes.

---

Resources

• Documentation
• Why Fabro
• DOT Language Reference
• API Reference
• Tutorials
• Bug Reports
• Feature Requests