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proof

Decompose multi-step tasks into a DAG of parallel subtasks.

64|2|Updated Nov 8, 2021
Search Tags:#workflow automation#parallel execution#subagents#task decomposition#dag#live status#cursor sdk
One-click install
npx skills add https://github.com/FlatbreadLabs/flatbread --skill proof-flatbreadlabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: proof
Source: https://github.com/FlatbreadLabs/flatbread/tree/main/.cursor/skills/proof
Command: npx skills add https://github.com/FlatbreadLabs/flatbread --skill proof-flatbreadlabs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the slow, error-prone process of manually decomposing complex multi-step tasks, running subtasks sequentially, and losing visibility into parallel work progress.

Core Features & Use Cases

  • DAG Task Decomposition: Automatically breaks complex user requests into a directed acyclic graph (DAG) of independent and dependent subtasks, maximizing parallel execution.
  • Parallel Subagent Execution: Runs subtasks concurrently via Cursor SDK local subagents, assigning each the appropriate model based on task complexity (HIGH/MED/LOW).
  • Live Status Tracking: Renders real-time streaming progress of all subagents to a live-updating canvas file, so you can watch tasks move through PENDING → RUNNING → FINISHED/ERROR states as they complete.
  • Use Case: For example, if you need to audit a codebase's architecture, generate documentation, and write tests for a new feature, this Skill can fan out those independent tasks to run in parallel, then merge their outputs into a final summary, cutting total workflow time dramatically.

Quick Start

Use the proof skill to decompose your multi-step project audit request into a parallel DAG of subtasks and track all subagent progress in real time via the live canvas.

Frequently Asked Questions about proof

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I decompose a complex codebase audit into parallel subtasks?▼

Decompose a complex codebase audit into parallel subtasks by mapping independent and dependent operations into a directed acyclic graph (DAG), which eliminates sequential execution bottlenecks and drastically reduces total workflow time.

What is DAG task decomposition for workflow automation?▼

DAG task decomposition for workflow automation is the process of breaking complex multi-step requests into a directed acyclic graph of subtasks, maximizing parallel execution for processes like migration planning and multi-file implementation.

Can I run parallel subagents with the Cursor SDK?▼

Yes, you can run parallel subagents with the Cursor SDK by fanning out subtasks to local subagents concurrently, assigning each the appropriate model based on task complexity to execute workflows efficiently.

How does live status tracking work for parallel task execution?▼

Live status tracking for parallel task execution works by rendering real-time streaming progress to a hot-reloading canvas file, allowing you to watch subagents move through PENDING, RUNNING, FINISHED, or ERROR states.

Are there limitations to using DAG decomposition for multi-file implementation tasks?▼

DAG decomposition for multi-file implementation tasks requires clear dependency relationships between operations to effectively map the execution graph; tasks with ambiguous or highly intertwined dependencies may limit parallel execution gains.

What is the best way to track subagent progress during multi-file implementation?▼

The best way to track subagent progress during multi-file implementation is rendering real-time streaming status to a live-updating canvas file, providing full artifact traceability and visibility into resumable runs.

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