Prompt Optimization

Description

Provide systematic prompt optimization methodology, including optimization principles, common problem diagnosis, rewriting techniques, and quality checklists. Enable AI agent prompts to achieve optimal instruction effectiveness while preserving original role definitions.

Belongs To

This skill belongs exclusively to agents/optimization/prompt-optimizer.md

Optimization Principles

Principle 1: Fidelity

Optimize the expression method, not the content essence.

Checklist:

• •Is the role's core identity unchanged?
• •Is the responsibility scope exactly the same?
• •Are collaboration relationships maintained?
• •Are boundary definitions consistent?

Principle 2: Specificity

Transform abstract descriptions into actionable specific instructions.

Rewriting formula:

Abstract description → Specific action + Verifiable result

Principle 3: Conciseness

Remove words that don't add information value.

Checklist:

• •Are there sentences that repeat the same meaning?
• •Are there modifiers that can be removed?
• •Is there unnecessary explanatory text?

Principle 4: Directiveness

Use imperative sentences, directly tell the AI what to do.

Common Problems and Optimization Patterns

Problem 1: Vague Verbs

Symptom: Using verbs like "handle", "manage", "be responsible for" that have no specific behavioral direction

Before:

Responsible for handling user feedback

After:

Collect user feedback, classify into bug/feature/question categories, transfer bugs to developers, record features in backlog, respond directly to questions

Problem 2: Implicit Assumptions

Symptom: Assuming readers know certain unstated information

Before:

Review according to standard process

After:

Review using the following process: 1. Check format completeness 2. Verify reference paths 3. Confirm terminology consistency

Problem 3: Redundant Modifiers

Symptom: Using adjectives or adverbs that don't add information value

Before:

Carefully and thoroughly check every detail to ensure high-quality output

After:

Check each field against template requirements

Problem 4: Passive Voice

Symptom: Using passive structures like "is done by", "will be"

Before:

Tasks will be assigned to the corresponding executor

After:

Assign tasks to the corresponding executor

Problem 5: Vague Conditions

Symptom: Using conditions that cannot be evaluated like "if needed", "when appropriate"

Before:

Perform additional validation if needed

After:

When input data comes from external sources, execute format validation

Problem 6: Repeated Definitions

Symptom: Same thing described in multiple ways in different places

Before:

You are a content reviewer. Your role is to review content. You are responsible for ensuring content quality.

After:

You are a content reviewer, responsible for checking and flagging non-compliant content according to quality standards.

Problem 7: Over-explanation

Symptom: Explaining obvious things, or repeatedly explaining common knowledge the AI already knows

Before:

JSON is a data format that uses key-value pairs to organize data. You need to output results in JSON format.

After:

Output format: JSON

Optimization Checklist

For each .md file, check sequentially:

Structural Level

• • YAML frontmatter complete (name, description, model)
• • Only one h1 heading
• • Section order matches template requirements
• • Reference paths exist and are correct

Language Level

• • Uses imperative sentences ("You must" not "should")
• • No prohibited vague words (try to, appropriately, reasonably, as needed)
• • Verbs are specific and actionable
• • No passive voice
• • No redundant modifiers

Content Level

• • Role identity is clear (one paragraph explaining who they are)
• • Responsibilities are specific and actionable (each has a clear action)
• • Boundaries are explicit (lists things not done)
• • No repeated definitions
• • No implicit assumptions

Consistency Level

• • Terminology is unified within the file
• • Terminology is consistent with other files
• • Collaboration relationships are bidirectionally symmetric

Optimization Priority

When time is limited, process in this priority order:

1. •Fix errors: Reference path errors, format non-compliance
2. •Eliminate ambiguity: Vague words, implicit assumptions
3. •Strengthen instructions: Passive→active, abstract→specific
4. •Refine language: Remove redundancy

Example

Input

## Responsibilities

This role is mainly responsible for managing the team's daily work. They handle various task assignments and ensure work can proceed smoothly. If problems are encountered, they also coordinate appropriately.

Output

## Responsibilities

1. Receive upstream tasks and decompose into assignable subtasks
2. Assign subtasks based on each agent's responsibility scope
3. Track completion status of each subtask
4. When dependency conflicts exist between subtasks, adjust execution order or reassign
5. Aggregate all subtask outputs, verify completeness, and deliver downstream

Optimization Explanation