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Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.","aneeba-pixel\u002Fclaude-skills","aneeba-pixel","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F218186338?v=4","01M78GRXBTG70217SH410P543Y","Aneeba Saeed","01M78GRXCDZ6TA0F8H2CZMDGSS",0,"MIT","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fengineering-team\u002Fskills\u002Fsenior-prompt-engineer\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: \"senior-prompt-engineer\"\ndescription: This skill should be used when the user asks to \"optimize prompts\", \"design prompt templates\", \"evaluate LLM outputs\", \"build agentic systems\", \"implement RAG\", \"create few-shot examples\", \"analyze token usage\", or \"design AI workflows\". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.\n---\n\n# Senior Prompt Engineer\n\nPrompt engineering patterns, LLM evaluation frameworks, and agentic system design.\n\n## Table of Contents\n\n- [Quick Start](#quick-start)\n- [Tools Overview](#tools-overview)\n  - [Prompt Optimizer](#1-prompt-optimizer)\n  - [RAG Evaluator](#2-rag-evaluator)\n  - [Agent Orchestrator](#3-agent-orchestrator)\n- [Prompt Engineering Workflows](#prompt-engineering-workflows)\n  - [Prompt Optimization Workflow](#prompt-optimization-workflow)\n  - [Few-Shot Example Design](#few-shot-example-design-workflow)\n  - [Structured Output Design](#structured-output-design-workflow)\n- [Reference Documentation](#reference-documentation)\n- [Common Patterns Quick Reference](#common-patterns-quick-reference)\n\n---\n\n## Quick Start\n\n```bash\n# Analyze and optimize a prompt file\npython scripts\u002Fprompt_optimizer.py prompts\u002Fmy_prompt.txt --analyze\n\n# Evaluate RAG retrieval quality\npython scripts\u002Frag_evaluator.py --contexts contexts.json --questions questions.json\n\n# Visualize agent workflow from definition\npython scripts\u002Fagent_orchestrator.py agent_config.yaml --visualize\n```\n\n---\n\n## Tools Overview\n\n### 1. Prompt Optimizer\n\nAnalyzes prompts for token efficiency, clarity, and structure. Generates optimized versions.\n\n**Input:** Prompt text file or string\n**Output:** Analysis report with optimization suggestions\n\n**Usage:**\n```bash\n# Analyze a prompt file\npython scripts\u002Fprompt_optimizer.py prompt.txt --analyze\n\n# Output:\n# Token count: 847\n# Estimated cost: $0.0025 (GPT-4)\n# Clarity score: 72\u002F100\n# Issues found:\n#   - Ambiguous instruction at line 3\n#   - Missing output format specification\n#   - Redundant context (lines 12-15 repeat lines 5-8)\n# Suggestions:\n#   1. Add explicit output format: \"Respond in JSON with keys: ...\"\n#   2. Remove redundant context to save 89 tokens\n#   3. Clarify \"analyze\" -> \"list the top 3 issues with severity ratings\"\n\n# Generate optimized version\npython scripts\u002Fprompt_optimizer.py prompt.txt --optimize --output optimized.txt\n\n# Count tokens for cost estimation\npython scripts\u002Fprompt_optimizer.py prompt.txt --tokens --model gpt-4\n\n# Extract and manage few-shot examples\npython scripts\u002Fprompt_optimizer.py prompt.txt --extract-examples --output examples.json\n```\n\n---\n\n### 2. RAG Evaluator\n\nEvaluates Retrieval-Augmented Generation quality by measuring context relevance and answer faithfulness.\n\n**Input:** Retrieved contexts (JSON) and questions\u002Fanswers\n**Output:** Evaluation metrics and quality report\n\n**Usage:**\n```bash\n# Evaluate retrieval quality\npython scripts\u002Frag_evaluator.py --contexts retrieved.json --questions eval_set.json\n\n# Output:\n# === RAG Evaluation Report ===\n# Questions evaluated: 50\n#\n# Retrieval Metrics:\n#   Context Relevance: 0.78 (target: >0.80)\n#   Retrieval Precision@5: 0.72\n#   Coverage: 0.85\n#\n# Generation Metrics:\n#   Answer Faithfulness: 0.91\n#   Groundedness: 0.88\n#\n# Issues Found:\n#   - 8 questions had no relevant context in top-5\n#   - 3 answers contained information not in context\n#\n# Recommendations:\n#   1. Improve chunking strategy for technical documents\n#   2. Add metadata filtering for date-sensitive queries\n\n# Evaluate with custom metrics\npython scripts\u002Frag_evaluator.py --contexts retrieved.json --questions eval_set.json \\\n    --metrics relevance,faithfulness,coverage\n\n# Export detailed results\npython scripts\u002Frag_evaluator.py --contexts retrieved.json --questions eval_set.json \\\n    --output report.json --verbose\n```\n\n---\n\n### 3. Agent Orchestrator\n\nParses agent definitions and visualizes execution flows. Validates tool configurations.\n\n**Input:** Agent configuration (YAML\u002FJSON)\n**Output:** Workflow visualization, validation report\n\n**Usage:**\n```bash\n# Validate agent configuration\npython scripts\u002Fagent_orchestrator.py agent.yaml --validate\n\n# Output:\n# === Agent Validation Report ===\n# Agent: research_assistant\n# Pattern: ReAct\n#\n# Tools (4 registered):\n#   [OK] web_search - API key configured\n#   [OK] calculator - No config needed\n#   [WARN] file_reader - Missing allowed_paths\n#   [OK] summarizer - Prompt template valid\n#\n# Flow Analysis:\n#   Max depth: 5 iterations\n#   Estimated tokens\u002Frun: 2,400-4,800\n#   Potential infinite loop: No\n#\n# Recommendations:\n#   1. Add allowed_paths to file_reader for security\n#   2. Consider adding early exit condition for simple queries\n\n# Visualize agent workflow (ASCII)\npython scripts\u002Fagent_orchestrator.py agent.yaml --visualize\n\n# Output:\n# ┌─────────────────────────────────────────┐\n# │            research_assistant           │\n# │              (ReAct Pattern)            │\n# └─────────────────┬───────────────────────┘\n#                   │\n#          ┌────────▼────────┐\n#          │   User Query    │\n#          └────────┬────────┘\n#                   │\n#          ┌────────▼────────┐\n#          │     Think       │◄──────┐\n#          └────────┬────────┘       │\n#                   │                │\n#          ┌────────▼────────┐       │\n#          │   Select Tool   │       │\n#          └────────┬────────┘       │\n#                   │                │\n#     ┌─────────────┼─────────────┐  │\n#     ▼             ▼             ▼  │\n# [web_search] [calculator] [file_reader]\n#     │             │             │  │\n#     └─────────────┼─────────────┘  │\n#                   │                │\n#          ┌────────▼────────┐       │\n#          │    Observe      │───────┘\n#          └────────┬────────┘\n#                   │\n#          ┌────────▼────────┐\n#          │  Final Answer   │\n#          └─────────────────┘\n\n# Export workflow as Mermaid diagram\npython scripts\u002Fagent_orchestrator.py agent.yaml --visualize --format mermaid\n```\n\n---\n\n## Prompt Engineering Workflows\n\n### Prompt Optimization Workflow\n\nUse when improving an existing prompt's performance or reducing token costs.\n\n**Step 1: Baseline current prompt**\n```bash\npython scripts\u002Fprompt_optimizer.py current_prompt.txt --analyze --output baseline.json\n```\n\n**Step 2: Identify issues**\nReview the analysis report for:\n- Token waste (redundant instructions, verbose examples)\n- Ambiguous instructions (unclear output format, vague verbs)\n- Missing constraints (no length limits, no format specification)\n\n**Step 3: Apply optimization patterns**\n| Issue | Pattern to Apply |\n|-------|------------------|\n| Ambiguous output | Add explicit format specification |\n| Too verbose | Extract to few-shot examples |\n| Inconsistent results | Add role\u002Fpersona framing |\n| Missing edge cases | Add constraint boundaries |\n\n**Step 4: Generate optimized version**\n```bash\npython scripts\u002Fprompt_optimizer.py current_prompt.txt --optimize --output optimized.txt\n```\n\n**Step 5: Compare results**\n```bash\npython scripts\u002Fprompt_optimizer.py optimized.txt --analyze --compare baseline.json\n# Shows: token reduction, clarity improvement, issues resolved\n```\n\n**Step 6: Validate with test cases**\nRun both prompts against your evaluation set and compare outputs.\n\n---\n\n### Few-Shot Example Design Workflow\n\nUse when creating examples for in-context learning.\n\n**Step 1: Define the task clearly**\n```\nTask: Extract product entities from customer reviews\nInput: Review text\nOutput: JSON with {product_name, sentiment, features_mentioned}\n```\n\n**Step 2: Select diverse examples (3-5 recommended)**\n| Example Type | Purpose |\n|--------------|---------|\n| Simple case | Shows basic pattern |\n| Edge case | Handles ambiguity |\n| Complex case | Multiple entities |\n| Negative case | What NOT to extract |\n\n**Step 3: Format consistently**\n```\nExample 1:\nInput: \"Love my new iPhone 15, the camera is amazing!\"\nOutput: {\"product_name\": \"iPhone 15\", \"sentiment\": \"positive\", \"features_mentioned\": [\"camera\"]}\n\nExample 2:\nInput: \"The laptop was okay but battery life is terrible.\"\nOutput: {\"product_name\": \"laptop\", \"sentiment\": \"mixed\", \"features_mentioned\": [\"battery life\"]}\n```\n\n**Step 4: Validate example quality**\n```bash\npython scripts\u002Fprompt_optimizer.py prompt_with_examples.txt --validate-examples\n# Checks: consistency, coverage, format alignment\n```\n\n**Step 5: Test with held-out cases**\nEnsure model generalizes beyond your examples.\n\n---\n\n### Structured Output Design Workflow\n\nUse when you need reliable JSON\u002FXML\u002Fstructured responses.\n\n**Step 1: Define schema**\n```json\n{\n  \"type\": \"object\",\n  \"properties\": {\n    \"summary\": {\"type\": \"string\", \"maxLength\": 200},\n    \"sentiment\": {\"enum\": [\"positive\", \"negative\", \"neutral\"]},\n    \"confidence\": {\"type\": \"number\", \"minimum\": 0, \"maximum\": 1}\n  },\n  \"required\": [\"summary\", \"sentiment\"]\n}\n```\n\n**Step 2: Include schema in prompt**\n```\nRespond with JSON matching this schema:\n- summary (string, max 200 chars): Brief summary of the content\n- sentiment (enum): One of \"positive\", \"negative\", \"neutral\"\n- confidence (number 0-1): Your confidence in the sentiment\n```\n\n**Step 3: Add format enforcement**\n```\nIMPORTANT: Respond ONLY with valid JSON. No markdown, no explanation.\nStart your response with { and end with }\n```\n\n**Step 4: Validate outputs**\n```bash\npython scripts\u002Fprompt_optimizer.py structured_prompt.txt --validate-schema schema.json\n```\n\n---\n\n## Reference Documentation\n\n| File | Contains | Load when user asks about |\n|------|----------|---------------------------|\n| `references\u002Fprompt_engineering_patterns.md` | 10 prompt patterns with input\u002Foutput examples | \"which pattern?\", \"few-shot\", \"chain-of-thought\", \"role prompting\" |\n| `references\u002Fllm_evaluation_frameworks.md` | Evaluation metrics, scoring methods, A\u002FB testing | \"how to evaluate?\", \"measure quality\", \"compare prompts\" |\n| `references\u002Fagentic_system_design.md` | Agent architectures (ReAct, Plan-Execute, Tool Use) | \"build agent\", \"tool calling\", \"multi-agent\" |\n\n---\n\n## Common Patterns Quick Reference\n\n| Pattern | When to Use | Example |\n|---------|-------------|---------|\n| **Zero-shot** | Simple, well-defined tasks | \"Classify this email as spam or not spam\" |\n| **Few-shot** | Complex tasks, consistent format needed | Provide 3-5 examples before the task |\n| **Chain-of-Thought** | Reasoning, math, multi-step logic | \"Think step by step...\" |\n| **Role Prompting** | Expertise needed, specific perspective | \"You are an expert tax accountant...\" |\n| **Structured Output** | Need parseable JSON\u002FXML | Include schema + format enforcement |\n\n---\n\n## Common Commands\n\n```bash\n# Prompt Analysis\npython scripts\u002Fprompt_optimizer.py prompt.txt --analyze          # Full analysis\npython scripts\u002Fprompt_optimizer.py prompt.txt --tokens           # Token count only\npython scripts\u002Fprompt_optimizer.py prompt.txt --optimize         # Generate optimized version\n\n# RAG Evaluation\npython scripts\u002Frag_evaluator.py --contexts ctx.json --questions q.json  # Evaluate\npython scripts\u002Frag_evaluator.py --contexts ctx.json --compare baseline  # Compare to baseline\n\n# Agent Development\npython scripts\u002Fagent_orchestrator.py agent.yaml --validate       # Validate config\npython scripts\u002Fagent_orchestrator.py agent.yaml --visualize      # Show workflow\npython scripts\u002Fagent_orchestrator.py agent.yaml --estimate-cost  # Token estimation\n```\n","# Senior Prompt Engineer\n\nPrompt engineering patterns, LLM evaluation frameworks, and agentic system design.\n\n## Table of Contents\n\n- [Quick Start](#quick-start)\n- [Tools Overview](#tools-overview)\n  - [Prompt Optimizer](#1-prompt-optimizer)\n  - [RAG Evaluator](#2-rag-evaluator)\n  - [Agent Orchestrator](#3-agent-orchestrator)\n- [Prompt Engineering Workflows](#prompt-engineering-workflows)\n  - [Prompt Optimization Workflow](#prompt-optimization-workflow)\n  - [Few-Shot Example Design](#few-shot-example-design-workflow)\n  - [Structured Output Design](#structured-output-design-workflow)\n- [Reference Documentation](#reference-documentation)\n- [Common Patterns Quick Reference](#common-patterns-quick-reference)\n\n---\n\n## Quick Start\n\n```bash\n# Analyze and optimize a prompt file\npython scripts\u002Fprompt_optimizer.py prompts\u002Fmy_prompt.txt --analyze\n\n# Evaluate RAG retrieval quality\npython scripts\u002Frag_evaluator.py --contexts contexts.json --questions questions.json\n\n# Visualize agent workflow from definition\npython scripts\u002Fagent_orchestrator.py agent_config.yaml --visualize\n```\n\n---\n\n## Tools Overview\n\n### 1. Prompt Optimizer\n\nAnalyzes prompts for token efficiency, clarity, and structure. Generates optimized versions.\n\n**Input:** Prompt text file or string\n**Output:** Analysis report with optimization suggestions\n\n**Usage:**\n```bash\n# Analyze a prompt file\npython scripts\u002Fprompt_optimizer.py prompt.txt --analyze\n\n# Output:\n# Token count: 847\n# Estimated cost: $0.0025 (GPT-4)\n# Clarity score: 72\u002F100\n# Issues found:\n#   - Ambiguous instruction at line 3\n#   - Missing output format specification\n#   - Redundant context (lines 12-15 repeat lines 5-8)\n# Suggestions:\n#   1. Add explicit output format: \"Respond in JSON with keys: ...\"\n#   2. Remove redundant context to save 89 tokens\n#   3. Clarify \"analyze\" -> \"list the top 3 issues with severity ratings\"\n\n# Generate optimized version\npython scripts\u002Fprompt_optimizer.py prompt.txt --optimize --output optimized.txt\n\n# Count tokens for cost estimation\npython scripts\u002Fprompt_optimizer.py prompt.txt --tokens --model gpt-4\n\n# Extract and manage few-shot examples\npython scripts\u002Fprompt_optimizer.py prompt.txt --extract-examples --output examples.json\n```\n\n---\n\n### 2. RAG Evaluator\n\nEvaluates Retrieval-Augmented Generation quality by measuring context relevance and answer faithfulness.\n\n**Input:** Retrieved contexts (JSON) and questions\u002Fanswers\n**Output:** Evaluation metrics and quality report\n\n**Usage:**\n```bash\n# Evaluate retrieval quality\npython scripts\u002Frag_evaluator.py --contexts retrieved.json --questions eval_set.json\n\n# Output:\n# === RAG Evaluation Report ===\n# Questions evaluated: 50\n#\n# Retrieval Metrics:\n#   Context Relevance: 0.78 (target: >0.80)\n#   Retrieval Precision@5: 0.72\n#   Coverage: 0.85\n#\n# Generation Metrics:\n#   Answer Faithfulness: 0.91\n#   Groundedness: 0.88\n#\n# Issues Found:\n#   - 8 questions had no relevant context in top-5\n#   - 3 answers contained information not in context\n#\n# Recommendations:\n#   1. Improve chunking strategy for technical documents\n#   2. Add metadata filtering for date-sensitive queries\n\n# Evaluate with custom metrics\npython scripts\u002Frag_evaluator.py --contexts retrieved.json --questions eval_set.json \\\n    --metrics relevance,faithfulness,coverage\n\n# Export detailed results\npython scripts\u002Frag_evaluator.py --contexts retrieved.json --questions eval_set.json \\\n    --output report.json --verbose\n```\n\n---\n\n### 3. Agent Orchestrator\n\nParses agent definitions and visualizes execution flows. Validates tool configurations.\n\n**Input:** Agent configuration (YAML\u002FJSON)\n**Output:** Workflow visualization, validation report\n\n**Usage:**\n```bash\n# Validate agent configuration\npython scripts\u002Fagent_orchestrator.py agent.yaml --validate\n\n# Output:\n# === Agent Validation Report ===\n# Agent: research_assistant\n# Pattern: ReAct\n#\n# Tools (4 registered):\n#   [OK] web_search - API key configured\n#   [OK] calculator - No config needed\n#   [WARN] file_reader - Missing allowed_paths\n#   [OK] summarizer - Prompt template valid\n#\n# Flow Analysis:\n#   Max depth: 5 iterations\n#   Estimated tokens\u002Frun: 2,400-4,800\n#   Potential infinite loop: No\n#\n# Recommendations:\n#   1. Add allowed_paths to file_reader for security\n#   2. Consider adding early exit condition for simple queries\n\n# Visualize agent workflow (ASCII)\npython scripts\u002Fagent_orchestrator.py agent.yaml --visualize\n\n# Output:\n# ┌─────────────────────────────────────────┐\n# │            research_assistant           │\n# │              (ReAct Pattern)            │\n# └─────────────────┬───────────────────────┘\n#                   │\n#          ┌────────▼────────┐\n#          │   User Query    │\n#          └────────┬────────┘\n#                   │\n#          ┌────────▼────────┐\n#          │     Think       │◄──────┐\n#          └────────┬────────┘       │\n#                   │                │\n#          ┌────────▼────────┐       │\n#          │   Select Tool   │       │\n#          └────────┬────────┘       │\n#                   │                │\n#     ┌─────────────┼─────────────┐  │\n#     ▼             ▼             ▼  │\n# [web_search] [calculator] [file_reader]\n#     │             │             │  │\n#     └─────────────┼─────────────┘  │\n#                   │                │\n#          ┌────────▼────────┐       │\n#          │    Observe      │───────┘\n#          └────────┬────────┘\n#                   │\n#          ┌────────▼────────┐\n#          │  Final Answer   │\n#          └─────────────────┘\n\n# Export workflow as Mermaid diagram\npython scripts\u002Fagent_orchestrator.py agent.yaml --visualize --format mermaid\n```\n\n---\n\n## Prompt Engineering Workflows\n\n### Prompt Optimization Workflow\n\nUse when improving an existing prompt's performance or reducing token costs.\n\n**Step 1: Baseline current prompt**\n```bash\npython scripts\u002Fprompt_optimizer.py current_prompt.txt --analyze --output baseline.json\n```\n\n**Step 2: Identify issues**\nReview the analysis report for:\n- Token waste (redundant instructions, verbose examples)\n- Ambiguous instructions (unclear output format, vague verbs)\n- Missing constraints (no length limits, no format specification)\n\n**Step 3: Apply optimization patterns**\n| Issue | Pattern to Apply |\n|-------|------------------|\n| Ambiguous output | Add explicit format specification |\n| Too verbose | Extract to few-shot examples |\n| Inconsistent results | Add role\u002Fpersona framing |\n| Missing edge cases | Add constraint boundaries |\n\n**Step 4: Generate optimized version**\n```bash\npython scripts\u002Fprompt_optimizer.py current_prompt.txt --optimize --output optimized.txt\n```\n\n**Step 5: Compare results**\n```bash\npython scripts\u002Fprompt_optimizer.py optimized.txt --analyze --compare baseline.json\n# Shows: token reduction, clarity improvement, issues resolved\n```\n\n**Step 6: Validate with test cases**\nRun both prompts against your evaluation set and compare outputs.\n\n---\n\n### Few-Shot Example Design Workflow\n\nUse when creating examples for in-context learning.\n\n**Step 1: Define the task clearly**\n```\nTask: Extract product entities from customer reviews\nInput: Review text\nOutput: JSON with {product_name, sentiment, features_mentioned}\n```\n\n**Step 2: Select diverse examples (3-5 recommended)**\n| Example Type | Purpose |\n|--------------|---------|\n| Simple case | Shows basic pattern |\n| Edge case | Handles ambiguity |\n| Complex case | Multiple entities |\n| Negative case | What NOT to extract |\n\n**Step 3: Format consistently**\n```\nExample 1:\nInput: \"Love my new iPhone 15, the camera is amazing!\"\nOutput: {\"product_name\": \"iPhone 15\", \"sentiment\": \"positive\", \"features_mentioned\": [\"camera\"]}\n\nExample 2:\nInput: \"The laptop was okay but battery life is terrible.\"\nOutput: {\"product_name\": \"laptop\", \"sentiment\": \"mixed\", \"features_mentioned\": [\"battery life\"]}\n```\n\n**Step 4: Validate example quality**\n```bash\npython scripts\u002Fprompt_optimizer.py prompt_with_examples.txt --validate-examples\n# Checks: consistency, coverage, format alignment\n```\n\n**Step 5: Test with held-out cases**\nEnsure model generalizes beyond your examples.\n\n---\n\n### Structured Output Design Workflow\n\nUse when you need reliable JSON\u002FXML\u002Fstructured responses.\n\n**Step 1: Define schema**\n```json\n{\n  \"type\": \"object\",\n  \"properties\": {\n    \"summary\": {\"type\": \"string\", \"maxLength\": 200},\n    \"sentiment\": {\"enum\": [\"positive\", \"negative\", \"neutral\"]},\n    \"confidence\": {\"type\": \"number\", \"minimum\": 0, \"maximum\": 1}\n  },\n  \"required\": [\"summary\", \"sentiment\"]\n}\n```\n\n**Step 2: Include schema in prompt**\n```\nRespond with JSON matching this schema:\n- summary (string, max 200 chars): Brief summary of the content\n- sentiment (enum): One of \"positive\", \"negative\", \"neutral\"\n- confidence (number 0-1): Your confidence in the sentiment\n```\n\n**Step 3: Add format enforcement**\n```\nIMPORTANT: Respond ONLY with valid JSON. No markdown, no explanation.\nStart your response with { and end with }\n```\n\n**Step 4: Validate outputs**\n```bash\npython scripts\u002Fprompt_optimizer.py structured_prompt.txt --validate-schema schema.json\n```\n\n---\n\n## Reference Documentation\n\n| File | Contains | Load when user asks about |\n|------|----------|---------------------------|\n| `references\u002Fprompt_engineering_patterns.md` | 10 prompt patterns with input\u002Foutput examples | \"which pattern?\", \"few-shot\", \"chain-of-thought\", \"role prompting\" |\n| `references\u002Fllm_evaluation_frameworks.md` | Evaluation metrics, scoring methods, A\u002FB testing | \"how to evaluate?\", \"measure quality\", \"compare prompts\" |\n| `references\u002Fagentic_system_design.md` | Agent architectures (ReAct, Plan-Execute, Tool Use) | \"build agent\", \"tool calling\", \"multi-agent\" |\n\n---\n\n## Common Patterns Quick Reference\n\n| Pattern | When to Use | Example |\n|---------|-------------|---------|\n| **Zero-shot** | Simple, well-defined tasks | \"Classify this email as spam or not spam\" |\n| **Few-shot** | Complex tasks, consistent format needed | Provide 3-5 examples before the task |\n| **Chain-of-Thought** | Reasoning, math, multi-step logic | \"Think step by step...\" |\n| **Role Prompting** | Expertise needed, specific perspective | \"You are an expert tax accountant...\" |\n| **Structured Output** | Need parseable JSON\u002FXML | Include schema + format enforcement |\n\n---\n\n## Common Commands\n\n```bash\n# Prompt Analysis\npython scripts\u002Fprompt_optimizer.py prompt.txt --analyze          # Full analysis\npython scripts\u002Fprompt_optimizer.py prompt.txt --tokens           # Token count only\npython scripts\u002Fprompt_optimizer.py prompt.txt --optimize         # Generate optimized version\n\n# RAG Evaluation\npython scripts\u002Frag_evaluator.py --contexts ctx.json --questions q.json  # Evaluate\npython scripts\u002Frag_evaluator.py --contexts ctx.json --compare baseline  # Compare to baseline\n\n# Agent Development\npython scripts\u002Fagent_orchestrator.py agent.yaml --validate       # Validate config\npython scripts\u002Fagent_orchestrator.py agent.yaml --visualize      # Show workflow\npython scripts\u002Fagent_orchestrator.py agent.yaml --estimate-cost  # Token estimation\n```\n",{"name":5,"description":7},"claude\u002Fopen-prs-review-Of9HK","a48d8438efe8e36550acc4c2006bdcddf62911db","engineering-team\u002Fskills\u002Fsenior-prompt-engineer","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills","https:\u002F\u002Fgithub.com\u002Faneeba-pixel","Student at University of Gujrat","2026-07-14 21:41:17",{"source":8,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add aneeba-pixel\u002Fclaude-skills --skill senior-prompt-engineer",{"tool":37,"command":38},"bunx","bunx skills add aneeba-pixel\u002Fclaude-skills --skill senior-prompt-engineer",{"tool":40,"command":41},"pnpm","pnpm dlx skills add aneeba-pixel\u002Fclaude-skills --skill senior-prompt-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fengineering-team\u002Fskills\u002Fsenior-prompt-engineer\u002FSKILL.md",true,[45,51,55,59,65,69],{"path":46,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":49,"sourceHtmlUrl":50},"references\u002Fagentic_system_design.md","REFERENCE","参考文档",21700,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fengineering-team\u002Fskills\u002Fsenior-prompt-engineer\u002Freferences\u002Fagentic_system_design.md",{"path":52,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":53,"sourceHtmlUrl":54},"references\u002Fllm_evaluation_frameworks.md",13303,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fengineering-team\u002Fskills\u002Fsenior-prompt-engineer\u002Freferences\u002Fllm_evaluation_frameworks.md",{"path":56,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":57,"sourceHtmlUrl":58},"references\u002Fprompt_engineering_patterns.md",13636,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fengineering-team\u002Fskills\u002Fsenior-prompt-engineer\u002Freferences\u002Fprompt_engineering_patterns.md",{"path":60,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":63,"sourceHtmlUrl":64},"scripts\u002Fagent_orchestrator.py","SCRIPT","脚本",20488,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fengineering-team\u002Fskills\u002Fsenior-prompt-engineer\u002Fscripts\u002Fagent_orchestrator.py",{"path":66,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":67,"sourceHtmlUrl":68},"scripts\u002Fprompt_optimizer.py",17862,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fengineering-team\u002Fskills\u002Fsenior-prompt-engineer\u002Fscripts\u002Fprompt_optimizer.py",{"path":70,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":71,"sourceHtmlUrl":72},"scripts\u002Frag_evaluator.py",20362,"https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fengineering-team\u002Fskills\u002Fsenior-prompt-engineer\u002Fscripts\u002Frag_evaluator.py",[74,81,88,95,102,109],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":19},"01M78K120A3JKH3NH8GC19YG4M","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Triggers whenever a user uploads a syllabus, course outline, or curriculum document and wants supplementary readings. Also triggers on: 'syllabus reading list', 'find papers for my course', 'create a reading list from this syllabus', 'recent research for my class', 'supplementary readings', 'find journal articles for these topics', 'what recent papers cover this material', 'any new research on these course topics', 'update my syllabus with recent papers'. Even casual mentions when a syllabus is attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[],[],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":19},"01M78K0QS9AGDJ43CPQYEHM5S0","research","Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends\u002Fsentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Triggers — \"research [topic]\", \"look into [topic]\", \"what do we know about [topic]\", \"investigate [topic]\", \"find me information on [topic]\", \"do some research on [topic]\", \"I need to understand [topic]\", or any research request that doesn't obviously match a more-specific specialist skill. Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fresearch\u002Fskills\u002Fresearch\u002FSKILL.md",[],[],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":19},"01M78K0FV3HMJ94SK965CWMNNP","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[],[],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":19},"01M78K04E77B04KYBB6M01EV3J","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Triggers: 'prior art search for [invention]', 'patent search on [topic]', 'freedom to operate analysis', 'FTO for [product]', 'patent landscape for [field]', 'is [invention] novel', 'patents on [topic]', 'competitive patent analysis', 'prior art for litigation', 'patent diligence on [company]'. Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[],[],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":19},"01M78JZW8XEFZP6HSF53M8D4M1","notebooklm","Browser automation skill for controlling Google's NotebookLM. Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio Overview, infographics, slide decks, study guides, briefing docs, mind maps, timelines, FAQs), and creating new notebooks. Triggers on any phrase involving NotebookLM — 'open NotebookLM', 'check my [name] notebook', 'pull info from NotebookLM', 'ask my notebook about X', 'add [source] to NotebookLM', 'create an infographic in NotebookLM', 'use NotebookLM Studio', 'generate a slide deck from my notebook', or any variation where the goal involves NotebookLM. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[],[],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":19},"01M78JZHX45S2Y0GPY9Z5V6TQ3","litreview","Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a professionally formatted Word document (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — not a finished review, but an orientation guide that lets a researcher dive in confidently. Triggers: 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'. Do NOT trigger for single one-off paper searches where the user just wants a quick list — that's a plain Consensus search.","https:\u002F\u002Fgithub.com\u002Faneeba-pixel\u002Fclaude-skills\u002Fblob\u002Fclaude\u002Fopen-prs-review-Of9HK\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[],[]]