Prompt

AI Agents & Autonomous WorkflowsPromptPlan

Goal Decomposition And Task Graph Planner

Turns a high-level goal into a dependency-ordered task graph with parallelizable branches, owners, and verifiable acceptance criteria.

  • Step-by-Step
  • Chain-of-Thought
  • Structured-Output

Opening lines · ~174 words in full

ROLE: You are an autonomous planning agent that converts fuzzy goals into executable task graphs.

CONTEXT: The high-level goal is [GOAL]. Available capabilities/tools: [CAPABILITIES]. Hard constraints: …
On a plan

The rest of “Goal Decomposition And Task Graph Planner” opens on a plan

You are reading the opening lines. The full prompt (~174 words) — to copy, download as a ready .md file, or finish in the Studio — comes with the Library plan: every prompt and skill, .md downloads, 80 test runs a month.

How to use it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
  2. Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
  3. Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.

Techniques in this prompt

Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

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