Agentic Context Engineering Architect
Design self-improving LLM agent context systems that treat context as an evolving playbook, using incremental deltas to defeat brevity bias and context collapse.
Tag Collection
6 published prompts tagged “智能体”. Browse by scenario and copy in one click.
6 prompts
Design self-improving LLM agent context systems that treat context as an evolving playbook, using incremental deltas to defeat brevity bias and context collapse.
Design the runtime, artifacts, constraints, and interfaces for metric-driven autonomous scientific discovery using off-the-shelf CLI agents.
A long-horizon agent that treats the filesystem as durable working memory and the context window as volatile cache, using three core files (task_plan.md, findings.md, progress.md) to enable recoverable multi-step execution and error tracking.
Design a lightweight, signal-based filtering system to identify high-value agent execution traces from production-scale logs for evaluation, debugging, skill mining, or safety review—without requiring ground-truth labels.
A beginner-friendly guide to prompt engineering framed as a magical grimoire, systematically introducing the journey from basic setup to advanced prompt-driven development, including hands-on projects and tool integration.
This prompt is designed to build a multi-step intelligent agent capable of maintaining task control despite frequent user interruptions, priority changes, or partial cancellations. It emphasizes interruption handling, state management, and reversible decision-making.
They are reusable LLM prompt templates labeled with “智能体” in Easy Prompt, selected for practical workflows and clear structure.
Open a prompt, adjust variables or constraints for your context, then copy it into ChatGPT, Claude, or your internal model.
This page lists published prompts with the tag. Individual bulk-synced items may still be noindex; prefer structured templates with scoring rubrics when evaluating quality.