dag-library

Use this skill when the user wants to KEEP a dag definition and run it again later — the graph is an asset, not a one-off. For authoring a brand-new graph, read mass-ulw first; this skill covers the storage-and-rerun half.

The shape

A stored definition is a plain dag definition JSON file named <name>.json in one of the library dirs. First hit wins:

1. $OMO_DAG_LIBRARY (multiple dirs, separated by : — or by ; on Windows, so drive-letter paths survive)
2. $PWD/.omo/dags
3. $HOME/.omo/dags

{
  "key": "nightly-audit",
  "name": "Nightly audit",
  "nodes": [
    { "id": "audit", "category": "unspecified-low", "prompt": "Audit docs/ for stale claims; write findings to /tmp/audit-{{key}}.md." },
    { "id": "verify", "category": "quick", "prompt": "Verify each finding in /tmp/audit-{{key}}.md against src/.", "dependsOn": ["audit"] }
  ]
}

String values may carry placeholders, filled at load time: {{key}} (the final rotated key — use it in file paths so reruns never clobber each other), {{date}} (UTC YYYYMMDD), {{datetime}} (UTC YYYYMMDD-HHmmss). Node prompts must still stand alone: dependsOn is ordering only, so pass data between nodes through files, exactly as in mass-ulw.

Running it — JS eval cell, two lines

The extension publishes library.js next to sdk.js at OMO_DAG_SDK_ROOT:

const lib = await import(`${env("OMO_DAG_SDK_ROOT")}/library.js`)
const run = await lib.start("nightly-audit")
const result = await run.done()

await lib.load(name) returns the filled definition without starting it; await lib.start(name) loads and starts in one call and returns the same handle shape as sdk.start (run_id, done(), cancel(reason)). Both are async — the kernel's read global is async, so never call them un-awaited.

Key rotation — the one rule that matters

The dag engine keys idempotency on key + graph fingerprint: re-starting the same key with the same graph REUSES the old run instead of running again. So the library treats the stored key as a BASE key and rotates it on every load:

• lib.start("nightly-audit") → key becomes nightly-audit-<UTC YYYYMMDD-HHmmss>: every call is a fresh run. This is the default because wanting a fresh run is the common case.
• lib.start("nightly-audit", { suffix: "20260818" }) → key becomes nightly-audit-20260818: explicit suffix, so a retry of the same logical run reuses it (idempotent recovery), while a new day gets a new run.
• lib.start("nightly-audit", { suffix: "" }) → key stays nightly-audit: full idempotency; only reach for this when reusing the previous result is exactly what you want.

Python cells

Python cannot import the ESM library. Reproduce the same semantics with plain dicts — read the file, rotate the key, fill placeholders, call tool.dag:

import json
from datetime import datetime, timezone
defn = json.loads(read(f"{env('HOME')}/.omo/dags/nightly-audit.json"))
stamp = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S")
defn["key"] = f"{defn['key']}-{stamp}"
text = json.dumps(defn).replace("{{key}}", defn["key"]).replace("{{date}}", stamp[:8]).replace("{{datetime}}", stamp)
run = tool.dag({"action": "start", "definition": json.loads(text)})
result = tool.dag({"action": "wait", "run_id": run["run_id"]})

Saving a new definition

When the user asks to save the current graph: write it as <name>.json into $HOME/.omo/dags (user-level, survives cwd changes) or <repo>/.omo/dags (project-level, shareable through git if the team commits it), then confirm by running it once via lib.start. Names are letters, digits, dot, dash, underscore — the library rejects path-shaped names.