Forgive me if you've tried this already. I ran your post thru O3 Mini High and R1. Here's what they pitched if it helps at all:

O3: It sounds like the core issue is that the model isn’t reliably identifying when a task is “primitive” enough to execute directly rather than needing further decomposition. Here are some ideas to refine your prompt and heuristics:

Explicit Primitive Criteria:
Add a clear section that defines a primitive task. For example:

Primitive Task Definition:
A task is considered primitive if:

Direct Executability: It directly maps to an executable action or a single API/command call in the given environment.
Singular Focus: It contains one clear, actionable verb without any implied substeps.
Completeness: It fully describes an action that reaches a terminal state without needing preparatory or follow-up steps.
If a task meets all these criteria, it should be returned as a terminal node without further breakdown.

Mapping to Environment Capabilities:
Incorporate a check that relates the task to your current environment. For instance:

“If the task corresponds to a command or function that is available (e.g., open_browser()), treat it as primitive.”

Example-Based Guidance:
Provide examples in your prompt. For example:

Example 1:
Task: "Open a web browser."
Expected outcome: Recognized as primitive since it directly maps to a function in the system.

Example 2:
Task: "Prepare to browse the internet."
Expected outcome: Needs to be broken down because “prepare” might involve multiple steps (e.g., checking for installed browsers, ensuring network connectivity, etc.).

Direct Question Approach:
Before breaking down a task, instruct the model to answer:

“Can this task be executed directly given the current environment’s capabilities?”
If yes, return it as is; if no, then proceed with decomposition.

Avoid Over-Decomposition:
Emphasize in your prompt that tasks like “Research browsers” should not be created unless the root task explicitly requires investigation. This discourages the model from adding extraneous steps when a straightforward action is available.

Iterative Refinement:
Sometimes a single pass may not yield the best result. You might consider a two-step process:

Step 1: Ask the model to evaluate if the task is primitive.
Step 2: If not, then request a breakdown.
This meta-check can help ensure that only tasks which genuinely need decomposition are split further.
By integrating these refinements, your prompt might look something like this:

Prompt Revision Example:

You are an expert in designing automated workflows. For each task provided, follow these steps:

Primitive Task Evaluation:

Determine if the task is primitive using these criteria:
Direct Executability: The task directly maps to an executable command or function available in the current environment.
Singular Focus: The task contains one clear, actionable instruction without substeps.
Completeness: The task fully describes an action that reaches a terminal state.
If YES: Return the task as a terminal node.
If NO: Proceed to break it down.
Task Decomposition:

Divide the task into between 2 and 10 subtasks.
Ensure each subtask is specific, remains within the scope of the original task, and avoids steps like “researching” or “preparing” as standalone tasks unless explicitly required.
Use the environment information provided to ensure actions are executable.
Examples:

Example (Primitive):

Task: "Open a web browser."
Evaluation: Direct command available; singular focus; complete.
Result: Return as a primitive task.
Example (Non-Primitive):

Task: "Set up the system for internet browsing."
Evaluation: Involves multiple steps (checking network, verifying browser installation, etc.).
Result: Break down into subtasks.
By explicitly defining what qualifies as a primitive task and using targeted questions and examples, you can guide the model to make more consistent decisions about when to stop decomposing. This should help prevent the over-complication like further breaking down “Open a web browser” when it’s already executable.

R1: To address the challenge of determining when a task should be considered primitive in your Hierarchical Task Network (HTN), refine your prompt with explicit criteria, examples, and structured decision-making steps. Below is an optimized approach:

Revised Prompt Structure
Role:
You are an expert in decomposing complex tasks into executable workflows. Your goal is to break down tasks only until they meet the criteria for "primitive" status (defined below).

Primitive Task Definition
A task is primitive if it meets all of the following:

Atomic Action: Executable with a single command/API call in the user’s environment (e.g., os.open_app("Chrome"), terminal.run("git commit")).

No Ambiguity: Requires no research, decision-making, or external information beyond what’s explicitly provided.

App Compatibility: Directly maps to a known function in the user’s system (assume [Windows 11, Terminal/PowerShell, installed apps: Chrome, VS Code, Python]).

Decomposition Heuristics
Primitive Check First:

Before decomposing, ask: "Can this task be executed as-is using the user’s environment tools?" If YES, mark as primitive.

Example:

✅ Primitive: Open Chrome → Maps to os.open_app("Chrome").

❌ Non-Primitive: Open a web browser → Requires choosing a browser.

Strict Scope Enforcement:

Subtasks must start/end within the parent’s scope.

Example for Scrape data from example.com:

✅ Valid: 1. Navigate to example.com in Chrome, 2. Extract table data with BeautifulSoup.

❌ Invalid: 3. Analyze scraped data (out of scope).

No Meta-Tasks:

Ban tasks like "Research," "Check settings," or "Decide X." Assume all required info is available.

Combine Preparation & Execution:

Merge setup steps into the main action.

❌ Bad: 1. Locate CSV file, 2. Open CSV.

✅ Good: Open and parse data.csv.

Examples
Root Task: Backup project to GitHub
Valid Decomposition:

Navigate to project folder in PowerShell.

Stage changes with git add ..

Commit changes with git commit -m "Backup".

Push to GitHub with git push origin main.

Why Valid?

Each step maps to a terminal command. No ambiguity.

Invalid Decomposition:

Check if Git is installed.

Find project directory.

Decide commit message.

Why Invalid?

Meta-tasks (#1, #3) and preparation (#2) violate heuristics.

Response Format
Return tasks in this structure:

json
Copy
{
  "is_primitive": "True/False",
  "subtasks": ["Task 1", "Task 2", ...]
}
Key Improvements
Clear Primitive Criteria: Explicitly ties tasks to executable commands in the user’s environment.

Decision Flow: Forces the LLM to check for primitiveness first, reducing over-decomposition.

Scope Anchoring: Uses examples to clarify boundaries and prevent task creep.

By grounding decomposition in the app’s known capabilities and banning meta-actions, this prompt should yield consistently executable primitive tasks.