Prompt Library V2

📝 Prompt Library

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/prompt-library from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

📝 Prompt Library > A comprehensive collection of battle-tested prompts inspired by awesome-chatgpt-prompts and community best practices.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Prompt Categories, Prompt Engineering Techniques, Prompt Improvement Checklist, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Needs ready-to-use prompt templates
• Wants role-based prompts (act as X)
• Asks for prompt examples or inspiration
• Needs task-specific prompt patterns
• Wants to improve their prompting
• Use when the request clearly matches the imported source intent: A comprehensive collection of battle-tested prompts inspired by awesome-chatgpt-prompts and community best practices.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Prompt Categories

🎭 Role-Based Prompts

Expert Developer

Act as an expert software developer with 15+ years of experience. You specialize in clean code, SOLID principles, and pragmatic architecture. When reviewing code:
1. Identify bugs and potential issues
2. Suggest performance improvements
3. Recommend better patterns
4. Explain your reasoning clearly
Always prioritize readability and maintainability over cleverness.

Code Reviewer

Act as a senior code reviewer. Your role is to:
1. Check for bugs, edge cases, and error handling
2. Evaluate code structure and organization
3. Assess naming conventions and readability
4. Identify potential security issues
5. Suggest improvements with specific examples

Format your review as:
🔴 Critical Issues (must fix)
🟡 Suggestions (should consider)
🟢 Praise (what's done well)

Technical Writer

Act as a technical documentation expert. Transform complex technical concepts into clear, accessible documentation. Follow these principles:
- Use simple language, avoid jargon
- Include practical examples
- Structure with clear headings
- Add code snippets where helpful
- Consider the reader's experience level

System Architect

Act as a senior system architect designing for scale. Consider:
- Scalability (horizontal and vertical)
- Reliability (fault tolerance, redundancy)
- Maintainability (modularity, clear boundaries)
- Performance (latency, throughput)
- Cost efficiency

Provide architecture decisions with trade-off analysis.

🛠️ Task-Specific Prompts

Debug This Code

Debug the following code. Your analysis should include:

1. **Problem Identification**: What exactly is failing?
2. **Root Cause**: Why is it failing?
3. **Fix**: Provide corrected code
4. **Prevention**: How to prevent similar bugs

Show your debugging thought process step by step.

Explain Like I'm 5 (ELI5)

Explain [CONCEPT] as if I'm 5 years old. Use:
- Simple everyday analogies
- No technical jargon
- Short sentences
- Relatable examples from daily life
- A fun, engaging tone

Code Refactoring

Refactor this code following these priorities:
1. Readability first
2. Remove duplication (DRY)
3. Single responsibility per function
4. Meaningful names
5. Add comments only where necessary

Show before/after with explanation of changes.

Write Tests

Write comprehensive tests for this code:
1. Happy path scenarios
2. Edge cases
3. Error conditions
4. Boundary values

Use [FRAMEWORK] testing conventions. Include:
- Descriptive test names
- Arrange-Act-Assert pattern
- Mocking where appropriate

API Documentation

Generate API documentation for this endpoint including:
- Endpoint URL and method
- Request parameters (path, query, body)
- Request/response examples
- Error codes and meanings
- Authentication requirements
- Rate limits if applicable

Format as OpenAPI/Swagger or Markdown.

📊 Analysis Prompts

Code Complexity Analysis

Analyze the complexity of this codebase:

1. **Cyclomatic Complexity**: Identify complex functions
2. **Coupling**: Find tightly coupled components
3. **Cohesion**: Assess module cohesion
4. **Dependencies**: Map critical dependencies
5. **Technical Debt**: Highlight areas needing refactoring

Rate each area and provide actionable recommendations.

Performance Analysis

Analyze this code for performance issues:

1. **Time Complexity**: Big O analysis
2. **Space Complexity**: Memory usage patterns
3. **I/O Bottlenecks**: Database, network, disk
4. **Algorithmic Issues**: Inefficient patterns
5. **Quick Wins**: Easy optimizations

Prioritize findings by impact.

Security Review

Perform a security review of this code:

1. **Input Validation**: Check all inputs
2. **Authentication/Authorization**: Access control
3. **Data Protection**: Sensitive data handling
4. **Injection Vulnerabilities**: SQL, XSS, etc.
5. **Dependencies**: Known vulnerabilities

Classify issues by severity (Critical/High/Medium/Low).

🎨 Creative Prompts

Brainstorm Features

Brainstorm features for [PRODUCT]:

For each feature, provide:
- Name and one-line description
- User value proposition
- Implementation complexity (Low/Med/High)
- Dependencies on other features

Generate 10 ideas, then rank top 3 by impact/effort ratio.

Name Generator

Generate names for [PROJECT/FEATURE]:

Provide 10 options in these categories:
- Descriptive (what it does)
- Evocative (how it feels)
- Acronyms (memorable abbreviations)
- Metaphorical (analogies)

For each, explain the reasoning and check domain availability patterns.

🔄 Transformation Prompts

Migrate Code

Migrate this code from [SOURCE] to [TARGET]:

1. Identify equivalent constructs
2. Handle incompatible features
3. Preserve functionality exactly
4. Follow target language idioms
5. Add necessary dependencies

Show the migration step by step with explanations.

Convert Format

Convert this [SOURCE_FORMAT] to [TARGET_FORMAT]:

Requirements:
- Preserve all data
- Use idiomatic target format
- Handle edge cases
- Validate the output
- Provide sample verification

Examples

Example 1: Ask for the upstream workflow directly

Use @prompt-library-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @prompt-library-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @prompt-library-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @prompt-library-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/prompt-library, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Resources

• awesome-chatgpt-prompts
• prompts.chat
• Learn Prompting

💡 Tip: The best prompts are specific, provide context, and include examples of desired output.

Imported: Prompt Engineering Techniques

Chain of Thought (CoT)

Let's solve this step by step:
1. First, I'll understand the problem
2. Then, I'll identify the key components
3. Next, I'll work through the logic
4. Finally, I'll verify the solution

[Your question here]

Few-Shot Learning

Here are some examples of the task:

Example 1:
Input: [example input 1]
Output: [example output 1]

Example 2:
Input: [example input 2]
Output: [example output 2]

Now complete this:
Input: [actual input]
Output:

Persona Pattern

You are [PERSONA] with [TRAITS].
Your communication style is [STYLE].
You prioritize [VALUES].

When responding:
- [Behavior 1]
- [Behavior 2]
- [Behavior 3]

Structured Output

Respond in the following JSON format:
{
  "analysis": "your analysis here",
  "recommendations": ["rec1", "rec2"],
  "confidence": 0.0-1.0,
  "caveats": ["caveat1"]
}

Imported: Prompt Improvement Checklist

When crafting prompts, ensure:

• Clear objective: What exactly do you want?
• Context provided: Background information included?
• Format specified: How should output be structured?
• Examples given: Are there reference examples?
• Constraints defined: Any limitations or requirements?
• Success criteria: How do you measure good output?

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.