senior-prompt-engineer

Malicious tool definition detected

Tool: SKILL.md [1/2] Description: --- name: senior-prompt-engineer description: 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".

Tool: SKILL.md [2/2] Description: Test with held-out cases** Ensure model generalizes beyond your examples.

Malicious tool definition detected

Tool: references/agentic_system_design.md [1/2] Description: # Agentic System Design Agent architectures, tool use patterns, and multi-agent orchestration with pseudocode.

Tool: references/agentic_system_design.md [2/2] Description: {} for subtask in subtasks: agent_name = self.select_agent(subtask) result = self.agents[agent_name].execute(subtask) results[subtask.id] = result # Synthesize return self.synthesize(query, results) def decompose(self, query): """Break query into subtasks.""" prompt = f""" Break this task into subtasks for specialized agents: Task: {query} Available agents: - researcher: Gathers information - analyst: Analyzes data - writer: Produces c

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Tool: references/llm_evaluation_frameworks.md [1/2] Description: # LLM Evaluation Frameworks Concrete metrics, scoring methods, comparison tables, and A/B testing frameworks. ## Frameworks Index 1.

Tool: references/llm_evaluation_frameworks.md [2/2] Description: [ ] B is much better Why?

Malicious tool definition detected

Tool: references/prompt_engineering_patterns.md [1/2] Description: # Prompt Engineering Patterns Specific prompt techniques with example inputs and expected outputs. ## Patterns Index 1.

Tool: references/prompt_engineering_patterns.md [2/2] Description: reasoning where multiple valid paths exist. **Pattern:** 1.

Malicious tool definition detected

Tool: scripts/agent_orchestrator.py [1/2]

Tool: scripts/agent_orchestrator.py [2/2] Description: (width // 2) + "│") # Execute loop lines.append(" " * (width // 2 - 8) + "┌───────────────┐") lines.append(" " * (width // 2 - 8) + "│ Execute Step │◄──────┐") lines.append(" " * (width // 2 - 8) + "└───────┬───────┘ │") lines.append(" " * (width // 2) + "│ │") if config.tools: tool_line = " ".join([f"[{t.name}]" for t in config.tools[:4]]) lines.append(" " * 4 + tool_line) lines.append(" " * (width // 2) + "│ │") lines.append(" " * (width /

Malicious tool definition detected

Tool: scripts/prompt_optimizer.py [1/2] Description: #!/usr/bin/env python3 """ Prompt Optimizer - Static analysis tool for prompt engineering Features: - Token estimation (GPT-4/Claude approximation) - Prompt structure analysis - Clarity scoring - Few-shot example extraction and management - Optimization suggestions Usage: python prompt_optimizer.py prompt.txt --analyze python prompt_optimizer.py prompt.txt --tokens --model gpt-4 python prompt_optimizer.py prompt.txt --optimize --output optimiz

Tool: scripts/prompt_optimizer.py [2/2] Description: suggestions=[], sections=[{'name': s['name'], 'lines': f"{s['start']}-{s.get('end', s['start'])}"} for s in sections], has_examples=len(examples) > 0, example_count=len(examples), has_output_format=has_format, word_count=word_count, line_count=line_count ) analysis.suggestions = generate_suggestions(analysis) + format_suggestions return analysis def optimize_prompt(text: str) -> str: """Generate optimized version of prompt""" optimized = text

Malicious tool definition detected

Tool: scripts/rag_evaluator.py [1/2]

Tool: scripts/rag_evaluator.py [2/2] Description: for i in range(min(len(relevant), k))) ndcg = dcg / idcg if idcg > 0 else 0 return RetrievalMetrics( precision_at_k=round(precision, 3), recall_at_k=round(recall, 3), mrr=round(mrr, 3), ndcg_at_k=round(ndcg, 3), k=k ) def generate_recommendations(report: RAGEvaluationReport) -> List[str]: """Generate actionable recommendations based on evaluation""" recommendations = [] if report.avg_context_relevance < 0.8: recommendations.append( f"Context rele