dag-library
Stores a DAG definition once and re-runs it by name, instead of pasting the definition into every run
Install / Use
npx skills add code-yeongyu/oh-my-openagent --skill dag-libraryInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Tags
Our assessment of dag-library
dag-library scores 89/100 on our quality scale, 1529th of 4,648 Development & Engineering skills we index (top 33%).
Its SKILL.md is 4.2 KB long, split into 6 sections with 3 code examples: a solid amount of guidance for an agent.
With 69,362 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated about 2 months ago, so dag-library is actively maintained.
- Our last check on 2026-10-31 found the source still online.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-26. Automated pattern scan on 2026-09-26. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
dag-library compared with similar skills
All 4 of these similar skills score higher than dag-library; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| dag-library (this skill)by code-yeongyu | 89 | 69.4k | 45d ago | SKILL.md |
| taste-skillby Leonxlnx | 100 | 112.0k | 1d ago | CLAUDE.md |
| ai-job-searchby ai-job-search-dev | 100 | 70.5k | today | CLAUDE.md |
| claude-howtoby luongnv89 | 100 | 46.7k | 5d ago | CLAUDE.md |
| lobehubby lobehub | 100 | 84.4k | today | MCP Server |
Frequently asked questions
- How do I install dag-library?
- Run
npx skills add code-yeongyu/oh-my-openagent --skill dag-library. The install tabs above show the steps for each supported agent. - Which AI agents does dag-library work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is dag-library safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is dag-library still maintained?
- The repository was last updated about 2 months ago, so dag-library is actively maintained.
Skill content
View source on GitHubname: dag-library description: "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." metadata: short-description: Store and re-run named dag definitions
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:
$OMO_DAG_LIBRARY(multiple dirs, separated by:— or by;on Windows, so drive-letter paths survive)$PWD/.omo/dags$HOME/.omo/dags
{
"key": "nightly-audit",
"name": "Nightly audit",
"nodes": [
{ "id": "audit", "category": "unspecified-low", "prompt": "Audit docs/ for stale claims; write findings to /tmp/audit-{{key}}.md." },
{ "id": "verify", "category": "quick", "prompt": "Verify each finding in /tmp/audit-{{key}}.md against src/.", "dependsOn": ["audit"] }
]
}
String values may carry placeholders, filled at load time: {{key}} (the final rotated key — use it in file paths so reruns never clobber each other), {{date}} (UTC YYYYMMDD), {{datetime}} (UTC YYYYMMDD-HHmmss). Node prompts must still stand alone: dependsOn is ordering only, so pass data between nodes through files, exactly as in mass-ulw.
Running it — JS eval cell, two lines
The extension publishes library.js next to sdk.js at OMO_DAG_SDK_ROOT:
const lib = await import(`${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 becomesnightly-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 becomesnightly-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 isretry/amendon that run id, not a new suffix.lib.start("nightly-audit", { suffix: "" })→ key staysnightly-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 <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.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
