PlannerSkill

Decompose natural language goals into structured executable plans with conditional dependencies.

Updated Feb 25, 2026
One-click install
npx skills add https://github.com/PixnBits/SeedClaw --skill plannerskill
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: PlannerSkill
Source: https://github.com/PixnBits/SeedClaw/tree/main/src/skills/agent/planning
Command: npx skills add https://github.com/PixnBits/SeedClaw --skill plannerskill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill breaks down complex goals or user requests into a series of manageable, ordered subtasks, creating executable plans for an AI agent.

Core Features & Use Cases

  • Task Decomposition: Transforms high-level objectives into structured plans (DAGs or linear sequences).
  • Skill Assignment: Assigns subtasks to available skills or flags them for escalation.
  • Contingency Planning: Includes branches for handling failures, retries, and reflections.
  • Use Case: Given a goal like "Research and summarize the latest advancements in quantum computing," the PlannerSkill would generate a plan with steps like "Identify relevant research papers," "Extract key findings from each paper," "Synthesize findings into a summary," and assign these to appropriate skills.

Quick Start

Use the planner skill to create a plan for the goal "Write a blog post about the benefits of local-first AI".

Frequently Asked Questions about PlannerSkill

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

FAQPage Schema
How do I decompose natural language goals into structured AI agent workflows?

Task decomposition for AI agents transforms high-level objectives into ordered subtasks, creating executable DAGs or linear sequences. It assigns each step to specific skills, enabling automated orchestration of complex workflows.

What is task decomposition with conditional dependencies for agent orchestration?

Conditional dependency task decomposition breaks goals into subtasks with contingency branches for failures and retries. This ensures safe agent orchestration by dynamically managing parallel operations and enforcing strict invariants.

How do I create contingency plans for AI agent task failures?

Generate contingency plans by structuring workflows with dedicated branches for handling retries and reflections. This allows AI agents to dynamically manage errors, adjust execution paths, and safely continue the orchestration process.

Can I build parallelized DAGs from text descriptions for automated execution?

Yes, you can build parallelized DAGs by processing text descriptions of your goals. The system maps out parallel execution paths and skill assignments, generating structured plans optimized for automated agent processing.

Does agent task planning work without outbound network access?

Yes, agent task planning operates exclusively via message hub with zero outbound network access. This isolated environment ensures safe orchestration while decomposing goals and assigning subtasks to available skills.

When should I use a dedicated planning skill over linear task execution?

Use a dedicated planning skill when goals require parallelization, conditional dependencies, or contingency branches. It is essential for safely orchestrating multiple skills when simple sequential execution cannot handle dynamic failures.