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dag-library skill

by code-yeongyu·code-yeongyu/oh-my-openagent·70k stars

Stores a DAG definition once and re-runs it by name, instead of pasting the definition into every run. Use when the user wants to save a DAG, run a saved one, or schedule the same multi-agent graph repeatedly.

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Install the dag-library skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/code-yeongyu/oh-my-openagent.git /tmp/oh-my-openagent
mkdir -p ~/.claude/skills
cp -r /tmp/oh-my-openagent/packages/omo-senpi/skills/dag-library ~/.claude/skills/dag-library
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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 .json in one of the library dirs. First hit wins:

  1. $OMODAGLIBRARY (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 OMODAGSDK_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-: 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 re-running the same logical run reuses it (idempotent recovery), while a new day gets a new run. Recovering a FAILED node inside such a run is retry/amend on that run id, not a new suffix.
  • 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.workflow:

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.workflow({"action": "start", "definition": json.loads(text)})
result = tool.workflow({"action": "wait", "run_id": run["run_id"], "detach": False})  # detach=False keeps the cell-blocking wait; the bare tool action detaches against a live run

Saving a new definition

When the user asks to save the current graph: write it as .json into $HOME/.omo/dags (user-level, survives cwd changes) or /.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.

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