From aradotso-trending-skills-37
Orchestrates AI coding workflows using Fabro by defining agent pipelines as Graphviz DOT graphs with human gates, multi-model routing, and cloud sandboxes. Useful for complex agentic processes with human intervention.
npx claudepluginhub joshuarweaver/cascade-ai-ml-agents-misc-1 --plugin aradotso-trending-skills-37This skill uses the workspace's default tool permissions.
> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).
Generates original PNG/PDF visual art via design philosophy manifestos for posters, graphics, and static designs on user request.
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.
# 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)
# 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
Workflows are .dot files using the Graphviz DOT language with Fabro-specific attributes.
| Shape | Meaning |
|---|---|
Mdiamond | Start node |
Msquare | Exit node |
rectangle (default) | Agent node (LLM turn) |
hexagon | Human gate (pauses for approval) |
// 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
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
}
model: <model-id> # e.g. claude-sonnet-4-5, gpt-4o, gemini-2-flash
reasoning_effort: low|medium|high
provider: anthropic|openai|google
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"
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
}
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
}
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
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
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
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.
Fabro runs an API server for programmatic use:
fabro serve --port 8080
curl -X POST http://localhost:8080/api/runs \
-H "Content-Type: application/json" \
-d '{
"workflow": "workflows/plan-implement.dot",
"variables": { "feature_name": "dark-mode" }
}'
curl -N http://localhost:8080/api/runs/<run-id>/events
curl -X POST http://localhost:8080/api/runs/<run-id>/approve \
-H "Content-Type: application/json" \
-d '{ "decision": "approve" }'
# 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=...
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
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
}
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
}
fabro: command not found
~/.local/bin (or the install prefix) is on your $PATH.source ~/.bashrc or source ~/.zshrc after installation.Agent gets stuck in a loop
Human gate never pauses
shape=hexagon, not just a label containing "approve".fabro runs show <run-id> to confirm the run reached that node.Sandbox fails to start
DAYTONA_API_KEY is set and valid.fabro config to confirm sandbox.provider is set to daytona.fabro runs show <run-id> for sandbox error details.Model not found / API error
ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.).model: value in your stylesheet matches the provider's exact model ID.Run exits immediately without doing work
start (shape=Mdiamond) to exit (shape=Msquare).dot -Tsvg workflow.dot -o workflow.svg to visually inspect the graph for disconnected nodes.