graph
FeaturedGraph engineering for parallel task execution: convert a task, PRD, SPEC, or issue set into a dependency graph (DAG), layer it into supersteps, then implement each independent node concurrently with subagents — each node runs /goal → /review-it → /ship-it in an isolated git worktree, with a fan-in barrier between waves. Triggers on: graph, graph engineering, build a graph, task graph, dependency graph, DAG, parallel implement, 并发实现, 并行实现, 任务图, 把任务变成图, fan-out fan-in, superstep, dynamic workflow.
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Quality Score: 92/100
Skill Content
Details
- Author
- smallnest
- Repository
- smallnest/goal-workflow
- Created
- 3 months ago
- Last Updated
- 5 days ago
- Language
- HTML
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
graph-engineering
Design multi-agent work as a dependency graph instead of a linear script — nodes, edges, fan-out/fan-in, verification gates, and mandatory per-node model/effort tiering. Use when designing or reviewing any multi-agent orchestration, workflow script, or parallel task decomposition. Also trigger when the user's prompt sequences steps with "and then" / "next" / "after that" — ask whether the steps truly depend on each other and can be graph-engineered instead of run as a linear pipeline.
graph-node-and-edge-design
Design the nodes, edges, and shared state object for a multi-agent graph — specialized work units, routing between them (straight, conditional, fan-out, fan-in), and an explicit state schema — once loop-vs-graph-decision has confirmed a graph is actually warranted. Use when architecting a multi-agent system, when designing fan-out/fan-in parallelism, or when a graph's "shared state" is vaguely defined instead of an explicit schema.
graphkit
Run a long-horizon coding task as a small graph of agent nodes instead of one drifting loop. Generates an executor node (works against a single-source-of-truth ledger) and a clean-context supervisor node that audits from outside the executor's context — re-verifying claimed-done work, checkpoint-committing what passes, and correcting drift through a one-way directives file. Use for multi-round tasks where an agent tends to scope-creep, fake "done", or quietly lower the bar. Nodes share no context, so the executor runs on a cheap/fast model while a strong model supervises.