fabro-workflow
Skill6 starsChanged 4 months agoMITView on GitHub
Author, edit, validate, and debug Fabro workflows — Graphviz DOT graphs (.fabro) paired with a workflow.toml run config. Use when writing or changing a .fabro file, a workflow.toml, a .fabro/workflows/<name>/ package, or when a Fabro run fails and needs diagnosing via fabro validate/preflight/events/logs.
In this repository14 other files it gives its agents.
- strict-code-reviewSkill
- fastapi-proSkill
- industry-standardizeSkill
- issue-decomposerSkill
- issue-triage-loopSkill
- martin-clean-codeSkill
- pr-code-reviewSkill
- prd-reviewSkill
- rebase-on-mainSkill
- request-to-prdSkill
- startup-analystSkill
- two-axis-reviewSkill
- un-rgeSkill
- unslopSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 45
Run these in order after every edit.
- Very specific: line 55
When a node behaves as the wrong type or an attribute seems ignored, fabro parse <file.fabro> prints the parsed AST so you can see what …
- Very specific: line 78
Read the routing decision, not just the failure: fabro events -p shows which edge was selected and why.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Fabro workflows
A workflow is a package directory, not a single file:
.fabro/workflows/<name>/
├── workflow.toml # run config: graph path, inputs, model, environment, artifacts
├── workflow.fabro # the DOT graph (name must match [workflow].graph)
├── prompts/*.md(.j2) # long prompts, referenced as prompt="@prompts/foo.md"
└── schemas/*.json # JSON Schemas, referenced as output_schema="@schemas/x.json"Minimal pair:
# workflow.toml
_version = 1
[workflow]
graph = "workflow.fabro"// workflow.fabro
digraph Hello {
graph [goal="Say hello and demonstrate a basic Fabro workflow"]
rankdir=LR
start [shape=Mdiamond, label="Start"]
exit [shape=Msquare, label="Exit"]
greet [label="Greet", prompt="Add a haiku to the README"]
start -> greet -> exit
}The loop
Run these in order after every edit. Do not skip to fabro run.
fabro validate .fabro/workflows/<name>/workflow.fabro # structure, conditions, @file refs
fabro preflight .fabro/workflows/<name>/workflow.toml # + resolves inputs/vars/model/env
fabro graph .fabro/workflows/<name>/workflow.toml -o /tmp/wf.svg # eyeball the routing
fabro run <name> --dry-run # simulate execution
fabro run <name> --auto-approve -d # real run, detached, skip human gatesWhen a node behaves as the wrong type or an attribute seems ignored, fabro parse <file.fabro>
prints the parsed AST so you can see what Fabro actually inferred. It is hidden from --help
but present.
--auto-approve skips human gates; drop it when you are testing the gates themselves.
--preserve-sandbox keeps the sandbox alive for post-mortem fabro sandbox ssh.
Override inputs per-run with repeatable -I key=value (sparse per-key merge, unlike TOML
[run.inputs], which replaces the whole map).
Debugging a run
fabro inspect <run> # status, current node, outcome summary
fabro events <run> -p # pretty event log — start here; -f to follow, --since 10m
fabro logs <run> -n 200 # raw worker tracing when events aren't enough
fabro attach <run> # live terminal view
fabro steer <run> "..." # correct a running agent mid-execution (--interrupt to cut in)
fabro dump <run> -o ./dump # full durable state for offline analysis
fabro fork <run> # re-run from an earlier checkpoint after fixing the graph<run> accepts a run ID prefix or a workflow name (resolves to its most recent run).
Read the routing decision, not just the failure: fabro events -p shows which edge was
selected and why. If the wrong edge was taken, the cause is almost always the
transition cascade, not the node itself.
Gotchas that cause most breakage
- Templates render in exactly three attributes: graph
goal, nodeprompt, and the rootmodel_stylesheet.scriptgets value substitution only ({{ goal }},{{ inputs.X }},{{ vars.X }}— no{% if %}, no filters). Every other attribute, includinglabel,condition,model, and all edge attributes, is literal text;{{ }}there produces adetemplated_attributewarning and silently does nothing. - Shape determines the handler. Omitting
shape/typegives an agent node, unless the node setsscript(then: command). A prompt node must sayshape=tab— agent and prompt take identical attributes and are otherwise indistinguishable. - Setting both
scriptandprompton one node is an error. thread_idrequiresfidelity="full"— validation rejects the pair otherwise.selection="random"cannot coexist with conditional edges on the same node.- Every node named by an edge needs its own declaration, or validation fails.
- A failed human gate never falls through an unconditional edge. Route it explicitly
with
condition="outcome=failed". auto_status=trueis deprecated — writeon_failure="succeed".- Exactly one start (
shape=Mdiamond) and one exit (shape=Msquare); all nodes must be reachable from start.
Reference
Full attribute tables (node types, all node/edge/graph attributes, condition grammar,
workflow.toml sections, imports, stylesheets, parallel fan-out): see
REFERENCE.md.
Canonical in-repo examples worth copying from: .fabro/workflows/hello (minimal),
.fabro/workflows/code-review (prompts, schemas, scripts, custom environment image,
artifacts, GitHub permissions), docs/internal/demo/*.fabro (one pattern per file).
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

