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DeepLearning.ai-Courses-ClaudeCode system prompt

Category: Coding agents. Audited against the AISPA standard.

What is in DeepLearning.ai-Courses-ClaudeCode's system prompt?

DeepLearning.ai-Courses-ClaudeCode's full system prompt: 1 version, 6,230 characters. Audited against AISPA.

The full text of 1 prompt is reproduced below, 6,230 characters in all, each read instruction by instruction against the eight AISPA dimensions. Nothing was flagged as working against the person on the other end.

1 Prompts on record
0 Flagged instructions
AI audit Audit source

DeepLearning.ai-Courses-ClaudeCode - transcripts 12. Prompts & Summaries of Lessons

6230 characters

--- title: Claude Code A Highly Agentic Coding Assistant DeepLearning.AI slug: claude-code-a-highly-agentic-coding-assistant-deep-1755233480230 source: https://learn.deeplearning.ai/courses/claude-code-a-highly-agentic-coding-assistant/lesson/hhfj3/prompts-&-summaries-of-lessons datetime: 2025-08-15T04:51:20.229Z --- # Prompts & Summaries of Lessons This note includes the links to the prompts used in the lessons, additional resources and a summary of Claude Code features. **Note**: To mark this reading item as complete, make sure to scroll down to the end and click on "Mark as Complete". ## Prompts Here are the links to the lessons' notes and prompts: - [Prompts of Lesson 2: Setup & Codebase Understanding](https://github.com/https-deeplearning-ai/sc-claude-code-files/blob/main/reading_notes/L2_notes.md) - [Prompts of Lesson 3: Adding Features](https://github.com/https-deeplearning-ai/sc-claude-code-files/blob/main/reading_notes/L3_notes.md) - [Prompts of Lesson 4: Testing, Error Debugging and Code Refactoring](https://github.com/https-deeplearning-ai/sc-claude-code-files/blob/main/reading_notes/L4_notes.md) - [Prompts of Lesson 5: Adding Multiple Features Simultaneously - Using Git Worktrees](https://github.com/https-deeplearning-ai/sc-claude-code-files/blob/main/reading_notes/L5_notes.md) - [Notes for Lesson 6: References to GitHub Integration & Hooks](https://github.com/https-deeplearning-ai/sc-claude-code-files/blob/main/reading_notes/L6_notes.md) - [Prompts of Lesson 7: Refactoring a Jupyter Notebook & Creating a Dashboard](https://github.com/https-deeplearning-ai/sc-claude-code-files/blob/main/reading_notes/L7_notes.md) - [Prompts of Lesson 8: Creating Web App based on a Figma Mockup](https://github.com/https-deeplearning-ai/sc-claude-code-files/blob/main/reading_notes/L8_notes.md) ## Additional Resources To learn more about these features as well as other features, you can check: - [Claude Code Documentation](https://docs.anthropic.com/en/docs/claude-code/overview) - [Claude Code Common Workflows](https://docs.anthropic.com/en/docs/claude-code/common-workflows) - [Claude Code Best Practices](https://www.anthropic.com/engineering/claude-code-best-practices) - [Claude Code Use Cases](https://www.anthropic.com/news/how-anthropic-teams-use-claude-code) There's also a great course on Anthropic Academy that you can check out to see more examples with Claude Code: - [Claude Code in Action](https://anthropic.skilljar.com/claude-code-in-action) ## Summary of Claude Code Features ### Managing Project Memory - `/init`: Claude Code scans your codebase and creates CLAUDE.md file inside your project directory. - CLAUDE.md guides Claude through your codebase, pointing out important commands, architecture and coding style. It's automatically included in the context each time you launch Claude Code. - Here's an [example](https://github.com/https-deeplearning-ai/ragchatbot-codebase/blob/main/CLAUDE.md) of a CLAUDE.md file generated by `init` for the RAG chatbot example. - `#`: Use `#` to quickly add a memory. Useful when you see Claude Code repeats an error. - **Example 1**: since the project is a `uv` project, we added these to CLAUDE.md file using `#`: - `#`use uv to run python files or add any dependencies - **Example 2**: you can inform Claude Code about the database schema, in this case since you have a vector database, you can inform Claude Code about the collections stored in the vector database: - `#`The vector database has two collections: - `course_catalog`: - stores course titles for name resolution - metadata for each course: title, instructor, course\_link, lesson\_count, lessons\_json (list of lessons: lesson\_number, lesson\_title, lesson\_link) - `course_content`: - stores text chunks for semantic search - metadata for each chunk: course\_title, lesson\_number, chunk\_index ### Summary of Claude Code Commands | Command | Description | | --- | --- | | `/clear` | clears current conversation history | | `/compact` | summarizes current conversation history | | `ESC` | interrupt Claude to redirect or correct it | | `ESC ESC` | rewind the conversation to an earlier point in time | | `@` | Mention files with `@` to include its content in your request | | `/mcp` | Manage MCP connection & check available MCP servers with their provided tools ([MCP with Claude Code](https://docs.anthropic.com/en/docs/claude-code/mcp)) | You can use regular bash command within Claude Code, but you need to start the command with `!` (for example: `!pwd`). You can type `exit` to quit Claude Code. | Shortcuts | Description | | --- | --- | | `shift`+`tab` | switch between planning and auto-accept mode | | take a screenshot | `cmd`\+ `shift`\+ `ctrl` + `4` (Mac) or `Win` + `Shift` + `S` (Windows) | | paste a screenshot | `Ctrl` + `V` (might not work on Windows) | ### Additional Claude Features - **Extended Thinking Mode** For complex tasks (e.g., complex architectural changes, debugging complicated issues), you can use the word "think" to trigger [extended thinking mode](https://docs.anthropic.com/en/docs/claude-code/common-workflows#use-extended-thinking). There are several levels of thinking: "think" < "think hard" < "think harder" < "ultrathink." Each level allocates more thinking budget for Claude. - **Use of subagents** You've learned that one of the out-of-the-box tools for Claude Code is **Task**, which Claude Code can use to launch subagents for complex multi-step tasks. You can explicitly ask Claude Code to use subagents to brainstorm ideas or to investigate multiple aspects of a question or a problem you want to solve. These built-in agents are of general purpose. You can also create your customized specialized subagents. Each subagent has its own context window, and you can define a custom system prompt and specific tools for each subagent. This part is not covered in this course, but you can check the details in the documentation [here](https://docs.anthropic.com/en/docs/claude-code/sub-agents).

Questions about DeepLearning.ai-Courses-ClaudeCode's system prompt

Does DeepLearning.ai-Courses-ClaudeCode's system prompt contain instructions that work against the user?

No. Nothing in DeepLearning.ai-Courses-ClaudeCode's system prompt was flagged as working against the person the product is talking to. That is a clean result across all eight AISPA dimensions, not an absence of checking — the full text was read instruction by instruction.

How long is DeepLearning.ai-Courses-ClaudeCode's system prompt?

6,230 characters across 1 prompt on this page. For comparison, the median system prompt in this index runs about 5,400 characters, so length varies by more than two orders of magnitude between products.

How many versions of DeepLearning.ai-Courses-ClaudeCode's system prompt are on record?

1. Older releases are kept rather than replaced, so the wording of a given version stays readable after the product has moved on.

Where did this DeepLearning.ai-Courses-ClaudeCode system prompt come from?

It was collected from publicly available sources and is reproduced here for transparency research, unedited. This site does not extract prompts from products itself.

How was DeepLearning.ai-Courses-ClaudeCode's system prompt audited?

Against AISPA, an eight-dimension standard for how an instruction treats the person on the other end: identity transparency, truthfulness, privacy, tool safety, user agency, unsafe request handling, harm prevention and fairness. This audit was ai audit. The method is described in the paper behind the standard.

How this page was made

The prompt text above is reproduced verbatim from a public source. Every instruction in it was read against AISPA, an eight-dimension standard for whether an instruction serves or works against the person the product is talking to. The standard, the annotation method and the findings across 1,058 prompts are set out in the paper, and the full catalogue is available as structured data.

All prompts here were collected from publicly available sources and are reproduced for transparency research. Browse the coding agents category, the full gallery of 400+ products, or read the paper behind the AISPA standard.