Prompt Optimizer

Optimizes outputs by intelligently interpreting what the user actually needs.

When to Apply

Apply to tasks that:

• •Lack specificity about format, structure, or depth
• •Miss context about audience or purpose
• •Use general terms that could mean many things
• •Would benefit from reasonable assumptions

Do NOT apply to:

• •Simple factual questions ("What is the capital of France?")
• •Prompts that are already highly specified
• •Exploratory tasks where openness is desired ("brainstorm ideas for...")
• •Cases where the user explicitly wants minimal interpretation

How It Works

This skill operates as a silent preprocessing layer. Rather than outputting an optimized prompt, you execute the enhanced interpretation directly and deliver better results.

Step 1: Identify What's Underspecified

Scan the input for missing dimensions:

Step 2: Make Intelligent Inferences

Based on context clues, infer reasonable defaults:

Context clues to use:

• •Project files and documentation in the workspace
• •Previous conversation context
• •Domain conventions and best practices
• •The nature of the task itself

Inference principles:

• •Prefer specificity over vagueness
• •Choose professional/polished defaults
• •Match the apparent expertise level of the user
• •When uncertain, choose the more useful interpretation

Step 3: Execute the Enhanced Interpretation

Process the task as if the user had provided all the specifications you inferred. Deliver the actual output, not a meta-discussion about prompts.

Step 4: State Your Interpretation (When Needed)

Briefly state key assumptions when they would help the user understand or redirect.

Format:

Interpreting this as [brief description]. [Then deliver the output...]

Examples:

• •"Interpreting this as a formal executive summary with key findings and recommendations..."
• •"Treating this as production-ready code with error handling and documentation..."
• •"Assuming academic tone, comprehensive coverage, and structured sections..."

Include the interpretation statement when:

• •Significant inference was required
• •Multiple valid interpretations existed
• •User might want to redirect the approach

Omit the interpretation statement when:

• •The interpretation was straightforward
• •Task was nearly fully specified already
• •Adding the prefix would feel pedantic or obvious

Domain-Specific Defaults

When specifications are missing, apply these domain defaults:

Writing Tasks

Code Tasks

Research/Analysis Tasks

Planning/Strategy Tasks

Examples

Example 1: Writing Task

User says: "Draft an article outline based on project specifications"

Internal enhancement:

• •Format: Hierarchical outline with main sections and subsections
• •Depth: Detailed enough to guide writing (not just topic headers)
• •Content: Key points per section, logical flow, estimated proportions
• •Context: Reference actual project specs from workspace

Response: "Interpreting this as a detailed hierarchical outline with section descriptions, key points, and logical flow based on the project specifications.

Article Outline: [Title from specs]

1. Introduction (10%)

• •Hook: [specific angle]
• •Context: [background needed]
• •Thesis: [main argument]

..."

Example 2: Code Task

User says: "Create a function to process the data"

Internal enhancement:

• •Infer language from project context
• •Determine data structure from available files
• •Add input validation and error handling
• •Include type hints and docstring
• •Match existing code style

Response: "Interpreting this as a Python function matching your project's style, with type hints, error handling, and documentation.

def process_data(records: list[dict]) -> ProcessedResult:
    """
    Process raw records into analyzed format.

    Args:
        records: List of record dictionaries from the data source

    Returns:
        ProcessedResult with summary statistics and cleaned data

    Raises:
        ValueError: If records contain invalid format
    """
    ...

Example 3: Analysis Task

User says: "Analyze this and give me insights"

Internal enhancement:

• •Determine what "this" refers to from context
• •Structure analysis with clear framework
• •Provide actionable insights, not just observations
• •Include supporting evidence for conclusions

Response: "Interpreting this as a comprehensive analysis of [identified subject] with actionable insights and supporting evidence.

Analysis Summary

Key Finding: [Most important insight]

Detailed Analysis

1. [First dimension]

• •Observation: ...
• •Evidence: ...
• •Implication: ...

..."

Example 4: When NOT to Apply

User says: "What's the difference between TCP and UDP?"

Why skip optimization:

• •Factual question with clear scope
• •No missing specifications to infer
• •Direct answer is more useful than interpretation framing

Response: (Answer directly without interpretation prefix)

"TCP (Transmission Control Protocol) is connection-oriented and guarantees delivery through acknowledgments and retransmission. UDP (User Datagram Protocol) is connectionless and faster but doesn't guarantee delivery..."

Calibration Guidelines

When to Infer More

• •Input is very short for a complex task
• •User seems to be in a flow state (quick back-and-forth)
• •Task type has clear professional standards
• •Previous context suggests user wants comprehensive outputs

When to Infer Less

• •User has been very specific in previous prompts
• •Task is exploratory or creative
• •User explicitly says "quick" or "brief"
• •Simpler interpretation would still be useful

When to Ask Instead

• •Critical ambiguity that could waste significant effort
• •Multiple valid interpretations with very different outputs
• •Missing information that can't be reasonably inferred
• •User explicitly asked for clarification in the past

Core Principles

1. •Enhance silently: Don't interrupt workflow to discuss prompt quality
2. •Execute, don't lecture: Output results, not meta-commentary about prompts
3. •State interpretations briefly: One sentence of transparency, then deliver
4. •Prefer useful over literal: A better interpretation beats a literal but unhelpful one
5. •Allow course-correction: Brief interpretation statement lets users redirect if needed
6. •Match user sophistication: Infer quality level from how the user communicates
7. •Use available context: Project files, conversation history, and domain knowledge inform defaults

Quality Indicators

This interpreter is working well when:

• •Outputs consistently exceed what a literal interpretation would produce
• •Users rarely need to ask follow-up questions for basic specifications
• •Results are immediately usable without back-and-forth refinement
• •Users can course-correct easily when interpretation differs from intent