Args:
        question: The raw user ask to unpack.
        context: Optional background knowledge or situational frame.
        constraints: Explicit limits or success criteria.

    Returns:
        A structured prompt guiding the model to restate intent, surface
        constraints, and prepare clarifying questions before acting.
    

    Args:
        claim: The headline answer or assertion to validate.
        reasoning_trace: The supporting chain-of-thought or proof steps.
        constraints: Optional guardrails (requirements, risk limits).

    Returns:
        Structured prompt that audits assumptions, inference steps, and
        evidence, then proposes patches for any defects.
    

    Args:
        objective: Overall goal to satisfy.
        failed_step: The step or subgoal that failed.
        trace: Optional reasoning trace leading to the failure.
        constraints: Guardrails or requirements to respect.

    Returns:
        Structured prompt that rewinds to last stable state, explores
        alternatives, and proposes a patched plan.
    

    Args:
        expression: The raw text or equation to abstract.
        mapping_hint: Optional guidance for token-to-symbol mapping.
        goal: Optional downstream task (e.g., simplify, prove, generalize).

    Returns:
        Structured prompt that maps tokens to symbols, restates the problem
        abstractly, and provides a reversible mapping table.
    

Architects a custom context system based on a high-level goal (The Architect).
Returns a blueprint of Sutra components (Molecules, Cells, Organs, Thinking Models).

Use this when the user wants to build a persistent agent or complex workflow
rather than solving a single immediate task.

Args:
    goal: The user's objective (e.g., "Build a writing assistant that learns my style").
    constraints: Optional limits (e.g., "Must be lightweight").

Returns a guide to available Context Engineering techniques (The Librarian).
Use this to discover the best tool for a given task.

Args:
    category: Filter by 'reasoning', 'workflow', 'code', 'project', or 'all'.

Analyzes a task to recommend the most efficient tool (The Router).

Args:
    task_description: The user's prompt or task.

Returns a Protocol Shell. Can return a specific pre-defined template or a blank shell.

Args:
    name: The name of the protocol (e.g., 'reasoning.systematic') OR a custom name.
    intent: (Optional) The intent if creating a custom shell.

Returns a functional pseudo-code prompt template (Module 07).

Args:
    program_type: The type of program ('math', 'debate').

Returns a cell protocol template describing memory behaviors.

Args:
    name: Identifier of the cell protocol (key_value, windowed, episodic).

Returns an organ template for multi-agent orchestration (Layer 4).

Organs combine programs and cells into cohesive workflows for complex tasks
requiring multi-perspective analysis or collaborative reasoning.

Args:
    name: Identifier of the organ ('debate_council' for multi-perspective debate).