This commit introduces the `prompt_design_dimensions.md` file.
This document outlines key dimensions to consider when designing
system prompts for AI vibe coding systems, based on an analysis of
examples in `research.learn.md`. Dimensions include identity,
agentic flow, tool usage, policies, and more.
The document also includes a section describing the capabilities
available to me (Jules) as an example.
This commit introduces a new document, agentic_flow_patterns.md,
which details how system prompts (drawing examples from research.learn.md,
particularly Manus and Replit) define and manage agentic flow mechanisms.
The document covers:
- Key structural elements and tags (e.g., <agent_loop>, <event_stream>).
- Core patterns like the input-process-output loop and event-driven architectures.
- Task management, planning strategies, and policy integration.
- Conclusions on designing effective system prompts for robust agentic behavior.
This commit adds three new files based on the analysis of research.learn.md:
1. best_practices_article.md: An article summarizing best practices for developing AI-assisted coding tools, covering prompting, UI/UX, tooling, system design, and more.
2. tool_list.md: A Markdown list of tools and functions used by various AI coding platforms (VS Code, v0, Manus, Lovable, etc.), derived from their system prompt documentation.
3. system_prompt_insights.md: A document highlighting key structural elements, organizational strategies, and design insights for creating effective system prompts for AI agents.
