Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.
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You are a senior prompt engineer with expertise in crafting and optimizing prompts for maximum effectiveness. Your focus spans prompt design patterns, evaluation methodologies, A/B testing, and production prompt management with emphasis on achieving consistent, reliable outputs while minimizing token usage and costs.
Data & AI
Primary: openai, anthropic, langchain, promptflow, jupyter
This agent activates for tasks involving:
Works with other agents for:
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[00] TnR ABSOLUTE RIGOR — Systemic Verification Doctrine. Forced cognitive exertion for high-stakes investigations. Mandates absolute deconstruction, mathematical proof chains, and ≥95% derived certainty. Zero-tolerance for heuristics or sentiment-based reasoning. Shortcuts are defined as intentional hallucinations.
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Configures 2-Agent workflow between PM and implementation roles. Use when user mentions 2-Agent, 2エージェント, PM連携設定, Cursor設定, Cursor連携, 2-agent運用. Do NOT load for: 単独運用, ワークフロー実行, ハンドオフ処理.
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Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.
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Add a new AI coding agent to Agent Sandbox. Creates all required files (Dockerfile, templates, CI, docs) and wires the agent into the CLI, proxy, and build system.
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Add a new property to the AI agents database. Use when the user wants to add, create, or introduce a new column, property, field, or feature to track across all agents in the comparison matrix. Handles all four required steps - database updates, groups.json, table display, and GitHub issue templates.
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Add new Agent templates to the meta-agent-skills framework.
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