Prompt Optimizer

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:

| Dimension | Questions to Answer | | ------------------- | --------------------------------------------------- | | Format | What structure? What length? What sections? | | Depth | Surface-level or comprehensive? How much detail? | | Audience | Who is this for? What's their expertise level? | | Purpose | What will this be used for? What outcome is needed? | | Tone | Formal, casual, technical, accessible? | | Constraints | Any limits on scope, approach, or content? | | Quality markers | What makes this "good"? What are success criteria? |

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

| Missing | Default Inference | | --------- | ----------------------------------------------------------------- | | Length | Match complexity: simple topic = concise, complex = comprehensive | | Structure | Use clear sections with headers for anything over 300 words | | Tone | Professional and accessible unless context suggests otherwise | | Audience | Knowledgeable non-expert (explain jargon, don't oversimplify) |

Code Tasks

| Missing | Default Inference | | -------- | ------------------------------------------------------------------ | | Language | Infer from project context or ask if ambiguous | | Style | Follow existing codebase conventions | | Quality | Include error handling, types, and brief comments | | Testing | Mention what should be tested, include examples if straightforward |

Research/Analysis Tasks

| Missing | Default Inference | | --------- | --------------------------------------------------- | | Depth | Comprehensive enough to be actionable | | Sources | Cite specific evidence, not vague generalizations | | Structure | Executive summary + detailed findings + conclusions | | Output | Actionable insights, not just information |

Planning/Strategy Tasks

| Missing | Default Inference | | ------------ | ------------------------------------------------------- | | Scope | Cover immediate next steps + longer-term considerations | | Format | Structured with clear phases or priorities | | Practicality | Focus on actionable steps, not abstract principles | | Tradeoffs | Acknowledge alternatives and their pros/cons |

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: joaocarlos
• Source: joaocarlos/prompt-optimizer-skill
• License: MIT

Install and usage instructions live in the source repository linked above.