📦 deps(thirdparty): update snapshots
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ci[bot]
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{
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"name": "antigravity-bundle-aas-agent-mcp-builder",
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"version": "12.9.0",
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"version": "13.0.0",
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"description": "Editorial \"AAS Agent & MCP Builder\" bundle for Claude Code from Antigravity Awesome Skills.",
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"author": {
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"name": "sickn33 and contributors",
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{
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"name": "agyb-aas-agent-mcp-builder",
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"version": "12.9.0",
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"description": "Install the \"AAS Agent & MCP Builder\" editorial skill bundle from Antigravity Awesome Skills.",
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"version": "13.0.0",
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"description": "Install the \"AAS Agent & MCP Builder\" workflow plugin from Antigravity Awesome Skills.",
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"author": {
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"name": "sickn33 and contributors",
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"url": "https://github.com/sickn33/antigravity-awesome-skills"
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@@ -19,8 +19,8 @@
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"skills": "./skills/",
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"interface": {
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"displayName": "AAS Agent & MCP Builder",
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"shortDescription": "Specialized Product Plugins · 9 curated skills",
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"longDescription": "Developers building agentic apps, MCP tools, RAG systems, and evaluation loops. Covers AI Agents Architect, Agent Evaluation, and 7 more skills.",
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"shortDescription": "Build agentic apps, MCP tools, RAG systems, eval loops, tracing, prompts, and context-aware workflows.",
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"longDescription": "Build agentic apps, MCP tools, RAG systems, eval loops, tracing, prompts, and context-aware workflows. Maps directly to plugin-based agent workflows because it can grow from skills into MCP server configuration and app/tool integrations. Recommended for: Agent engineers, MCP tool builders, LLM app teams. Not for: Marketing-only content work, Manual office document tasks. Covers AI Agents Architect, Agent Evaluation, and 8 more skills.",
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"developerName": "sickn33 and contributors",
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"category": "Specialized Product Plugins",
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"capabilities": [
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"Write"
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],
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"websiteURL": "https://github.com/sickn33/antigravity-awesome-skills",
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"brandColor": "#111827"
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"brandColor": "#111827",
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"defaultPrompt": [
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"Use this plugin to design an MCP server for this workflow, including tools, schemas, and test cases.",
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"Use this plugin to create an eval plan for this agent and define reliability metrics.",
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"Use this plugin to review this RAG or LangGraph architecture for tool, memory, prompt, and observability gaps."
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]
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}
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}
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---
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name: prompt-engineering
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description: "Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior."
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risk: unknown
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source: community
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date_added: "2026-02-27"
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---
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# Prompt Engineering Patterns
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Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
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## Core Capabilities
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### 1. Few-Shot Learning
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Teach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity.
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**Example:**
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```markdown
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Extract key information from support tickets:
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Input: "My login doesn't work and I keep getting error 403"
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Output: {"issue": "authentication", "error_code": "403", "priority": "high"}
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Input: "Feature request: add dark mode to settings"
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Output: {"issue": "feature_request", "error_code": null, "priority": "low"}
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Now process: "Can't upload files larger than 10MB, getting timeout"
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```
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### 2. Chain-of-Thought Prompting
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Request step-by-step reasoning before the final answer. Add "Let's think step by step" (zero-shot) or include example reasoning traces (few-shot). Use for complex problems requiring multi-step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30-50%.
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**Example:**
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```markdown
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Analyze this bug report and determine root cause.
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Think step by step:
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1. What is the expected behavior?
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2. What is the actual behavior?
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3. What changed recently that could cause this?
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4. What components are involved?
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5. What is the most likely root cause?
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Bug: "Users can't save drafts after the cache update deployed yesterday"
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```
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### 3. Prompt Optimization
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Systematically improve prompts through testing and refinement. Start simple, measure performance (accuracy, consistency, token usage), then iterate. Test on diverse inputs including edge cases. Use A/B testing to compare variations. Critical for production prompts where consistency and cost matter.
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**Example:**
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```markdown
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Version 1 (Simple): "Summarize this article"
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→ Result: Inconsistent length, misses key points
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Version 2 (Add constraints): "Summarize in 3 bullet points"
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→ Result: Better structure, but still misses nuance
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Version 3 (Add reasoning): "Identify the 3 main findings, then summarize each"
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→ Result: Consistent, accurate, captures key information
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```
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### 4. Template Systems
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Build reusable prompt structures with variables, conditional sections, and modular components. Use for multi-turn conversations, role-based interactions, or when the same pattern applies to different inputs. Reduces duplication and ensures consistency across similar tasks.
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**Example:**
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```python
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# Reusable code review template
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template = """
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Review this {language} code for {focus_area}.
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Code:
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{code_block}
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Provide feedback on:
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{checklist}
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"""
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# Usage
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prompt = template.format(
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language="Python",
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focus_area="security vulnerabilities",
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code_block=user_code,
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checklist="1. SQL injection\n2. XSS risks\n3. Authentication"
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)
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```
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### 5. System Prompt Design
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Set global behavior and constraints that persist across the conversation. Define the model's role, expertise level, output format, and safety guidelines. Use system prompts for stable instructions that shouldn't change turn-to-turn, freeing up user message tokens for variable content.
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**Example:**
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```markdown
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System: You are a senior backend engineer specializing in API design.
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Rules:
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- Always consider scalability and performance
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- Suggest RESTful patterns by default
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- Flag security concerns immediately
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- Provide code examples in Python
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- Use early return pattern
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Format responses as:
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1. Analysis
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2. Recommendation
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3. Code example
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4. Trade-offs
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```
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## Key Patterns
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### Progressive Disclosure
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Start with simple prompts, add complexity only when needed:
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1. **Level 1**: Direct instruction
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- "Summarize this article"
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2. **Level 2**: Add constraints
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- "Summarize this article in 3 bullet points, focusing on key findings"
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3. **Level 3**: Add reasoning
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- "Read this article, identify the main findings, then summarize in 3 bullet points"
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4. **Level 4**: Add examples
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- Include 2-3 example summaries with input-output pairs
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### Instruction Hierarchy
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```
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[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]
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```
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### Error Recovery
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Build prompts that gracefully handle failures:
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- Include fallback instructions
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- Request confidence scores
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- Ask for alternative interpretations when uncertain
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- Specify how to indicate missing information
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## Best Practices
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1. **Be Specific**: Vague prompts produce inconsistent results
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2. **Show, Don't Tell**: Examples are more effective than descriptions
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3. **Test Extensively**: Evaluate on diverse, representative inputs
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4. **Iterate Rapidly**: Small changes can have large impacts
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5. **Monitor Performance**: Track metrics in production
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6. **Version Control**: Treat prompts as code with proper versioning
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7. **Document Intent**: Explain why prompts are structured as they are
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## Common Pitfalls
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- **Over-engineering**: Starting with complex prompts before trying simple ones
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- **Example pollution**: Using examples that don't match the target task
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- **Context overflow**: Exceeding token limits with excessive examples
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- **Ambiguous instructions**: Leaving room for multiple interpretations
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- **Ignoring edge cases**: Not testing on unusual or boundary inputs
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## When to Use
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This skill is applicable to execute the workflow or actions described in the overview.
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## Limitations
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- Use this skill only when the task clearly matches the scope described above.
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- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
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- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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