mass-ulw
GitHub用于构建和执行多智能体任务DAG的编排技能,支持基于依赖关系的并行与串行执行。
Trigger Scenarios
Install
npx skills add code-yeongyu/oh-my-openagent --skill mass-ulw -g -y
SKILL.md
Frontmatter
{
"name": "mass-ulw",
"metadata": {
"short-description": "Dependency-graph orchestration of child agents"
},
"description": "Run a dependency graph of child agents in one call with the native dag tool. Use when the user asks for mass-ulw, a DAG of tasks, fan-out\/fan-in work, or multi-agent execution where some tasks must wait on others."
}
mass-ulw
Use this skill when the user asks for mass-ulw, a task DAG, staged fan-out, or any multi-agent job where real dependencies exist: task C needs A and B finished first. For fully independent workers, plain parallel task spawns are simpler. Reach for dag when the ordering itself is the point.
Planning - MANDATORY first step
Before defining ANY graph, read references/planning.md (relative to this skill's own directory) IN FULL. Do not call sdk.define, sdk.start, or tool.dag with action: "start" before reading it. It carries the working doctrine this file deliberately omits: how to decompose the request into nodes, how to route each node's category, how to keep parallel write scopes disjoint, the node prompt contract, the verification wave, and the failure playbook. A graph defined without it is unplanned work.
The shape
A run is a declarative definition: a stable key (idempotency: re-starting the same key with the same graph reuses the run), a human name, and nodes. Each node has an id, a self-contained English prompt, a category that routes it to the right kind of worker, and optional dependsOn listing node ids that must finish first. dependsOn is ordering ONLY: no upstream output is substituted into a downstream prompt, so write every prompt to stand alone. Optional per-node extras: label, task_summary, description, and load_skills (skill names prepended to that node's prompt).
Route every node by category using the routing table in references/planning.md; the run executes nodes in parallel waves as their dependencies clear.
Running a dag - eval is the default
Build and run every dag INSIDE an eval cell. The eval kernel installs the tool.dag proxy and the extension publishes a small JS SDK at OMO_DAG_SDK_ROOT; driving runs from a cell is what unlocks the orchestration patterns in references/planning.md (data-driven graph construction, multi-run composition, concurrent runs, adaptive retries).
JS cells import the SDK from the path the extension publishes:
const sdk = await import(`${env("OMO_DAG_SDK_ROOT")}/sdk.js`)
const dag = sdk.define({ key: "docs-refresh", name: "Docs refresh" })
dag.node({ id: "audit", category: "unspecified-low", prompt: "Audit docs/ for stale API references and list each stale file with the outdated claim." })
dag.node({ id: "rewrite", category: "writing", prompt: "Rewrite every stale page under docs/ against the current API surface in src/.", dependsOn: ["audit"] })
dag.node({ id: "verify", category: "quick", prompt: "Check every code sample under docs/ compiles and every internal link resolves.", dependsOn: ["rewrite"] })
const run = await sdk.start(dag)
const result = await sdk.wait(run.run_id)
define builds the definition and rejects duplicate node ids locally, before anything is started. start, attach, snapshot, wait, and cancel are the whole surface.
Python cells cannot import the ESM SDK; call tool.dag({...}) directly with the same payload shape the SDK produces. Prefer a JS cell whenever the run involves any orchestration beyond a single start + wait.
Run lifecycle
start returns a run_id and a snapshot; keep the id. From there:
const sdk = await import(`${env("OMO_DAG_SDK_ROOT")}/sdk.js`)
const runId = "run_stub_1"
await sdk.attach(runId)
await sdk.snapshot(runId)
await sdk.cancel(runId, "superseded by a new plan")
attachre-binds to a live run you already own, for example after your own context was rebuilt.snapshotis a cheap read of status and node counts; poll it instead ofwaitwhen you have other work to do.waitblocks until the run settles and returns the final result.cancelstops the run; pass a reason so the record says why.
Resume across a restart
Runs are journaled. When the session dies mid-run, the run pauses instead of being lost; on restart the extension resumes paused runs it owns, reusing outputs of nodes that already finished so completed work is never redone. Your side of the contract: start with the same key and definition returns the existing run (reused: true) instead of forking a duplicate, or attach with the stored run_id. Never re-issue a changed definition under an old key; that's a definition conflict.
Observing a run
- The TUI status widget shows live runs with per-node progress.
/dagopens the detail view: node states, waves, and failures for each run in the session.- External viewers subscribe to the RPC channels
omo.dag.event(journaled, sequenced),omo.dag.updated(full snapshots),omo.dag.heartbeat, andomo.dag.activity.
Version History
- ec3d5af Current 2026-08-20 11:15


