autosearch:graph-search-plan

Name: autosearch:graph-search-plan
Availability: InStock
Author: Mario

Plan research tasks as directed-acyclic graphs with parallel execution.

40|6|Updated Mar 26, 2026

Search Tags:#workflow #planning #research #graph #dag #dependency

Ready to run

npx skills add https://github.com/0xmariowu/Autosearch --skill autosearch-graph-search-plan

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autosearch:graph-search-plan
Source: https://github.com/0xmariowu/Autosearch/tree/main/autosearch/skills/meta/graph-search-plan
Command: npx skills add https://github.com/0xmariowu/Autosearch --skill autosearch-graph-search-plan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

DAG-based planning for research tasks by representing sub-questions as nodes and dependencies as edges, enabling parallel execution and traceable progress.

Core Features & Use Cases

DAG graph model with root, nodes, and depends_on semantics
Execution policy that schedules ready tasks with controlled parallelism and tracks status
Use Case: plan and monitor a multi-step literature review and experiment planning with dependency-aware task orchestration

Quick Start

Provide a runnable DAG planning graph for a given research question and start executing independent subquestions in parallel.

Frequently Asked Questions about autosearch:graph-search-plan

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

FAQPage Schema

How do I plan research tasks with dependency constraints?▼

Plan research tasks with dependency constraints by representing sub-questions as nodes and dependencies as edges in a directed acyclic graph (DAG). This structure tracks dependencies and enables parallel execution of independent tasks.

What is a DAG-based workflow for research planning?▼

A DAG-based workflow for research planning is a directed acyclic graph model that represents sub-questions as nodes and dependencies as edges. It schedules ready tasks with controlled parallelism and tracks status for traceable progress.

How do I execute parallel tasks in a literature review?▼

Execute parallel tasks in a literature review by building a runnable DAG planning graph for your research question. The execution policy dispatches independent subquestions in parallel while tracking dependency status.

Can I re-plan a research workflow at runtime if dependencies change?▼

Yes, you can re-plan a research workflow at runtime. The DAG-based workflow supports runtime re-planning, allowing you to adjust the directed acyclic graph dynamically when dependency constraints or task statuses change.

Does this approach validate acyclic dependencies before task dispatch?▼

Yes, the approach validates acyclic dependencies before task dispatch. It performs acyclic validation on the graph model to ensure root-based plan generation and dependency-aware task orchestration function correctly.

What is the best way to monitor multi-step experimental planning?▼

The best way to monitor multi-step experimental planning is using a DAG graph model with root and nodes. It provides dependency-aware task orchestration, tracks execution status, and enables traceable progress across parallel tasks.