A shared tag with AI prompts and code snippets
From workspace: OpenAI
Team: ChatGPT
Total snippets: 26
26 snippets
Identifies top players in an industry and their market positions.
Identify and summarize the top five key players in the {Industry Name} industry. Highlight their market share, competitive advantages, recent strategic moves, and financial performance.
Lists ticker symbols aligned with an investment thesis.
Based on the investment thesis that {clearly describe your conviction or thesis}, identify and list 5-10 ticker symbols of companies that align closely with this thesis, briefly explaining the rationale for each.
Provides a detailed overview of an industry including market size, growth rate, and segments.
Provide a detailed industry overview for {Industry Name}, including current market size, projected growth rate, and key segments or sub-sectors. Include the latest statistical data available.
rovides a Strengths, Weaknesses, Opportunities, Threats assessment for an industry.
Conduct a detailed SWOT analysis for the {Industry Name} industry, highlighting strengths, weaknesses, opportunities, and threats currently facing the sector.
Lists top emerging trends and innovations shaping the industry.
List and describe the top three emerging trends and recent innovations within the {Industry Name} industry, providing relevant examples and their potential impact on the market.
Provides quick technical analysis for tickers.
Provide a brief technical analysis summary for the ticker symbols {list of tickers}. Highlight any bullish or bearish signals, relevant support and resistance levels, and recent trading volumes that align with my thesis.
Filters and ranks tickers for alignment with the thesis.
From the ticker list generated based on my thesis ({restatement of your investment thesis}), filter and rank these companies based on their alignment with the thesis criteria, industry leadership, and recent financial performance.
Summarizes the regulatory framework affecting the industry.
Summarize the current regulatory landscape for the {Industry Name} industry. Highlight major regulations, recent changes, or proposed legislation that could significantly impact the sector.
Identifies key fundamental events for shortlisted tickers.
For the ticker symbols shortlisted ({list of tickers}), identify key fundamental catalysts (earnings releases, new product launches, regulatory approvals, management changes) that support my investment thesis.
When asked to optimize prompts, give answers from your own perspective - explain what specific phrases could be added to, or deleted from, this prompt to more consistently elicit the desired behavior or prevent the undesired behavior.
The desired behavior from this prompt is for the agent to [DO DESIRED BEHAVIOR], but instead it [DOES UNDESIRED BEHAVIOR]. While keeping as much of the existing prompt intact as possible, what are some minimal edits/additions that you would make...
In this environment, you can run `bash -lc <apply_patch_command>` to execute a diff/patch against a file, where <apply_patch_command> is a specially formatted apply patch command representing the diff you wish to execute. A valid...
System prompt for GPT-5 coding agent inside a secure container with file access.
Please resolve the user's task by editing and testing the code files in your current code execution session. You are a deployed coding agent. Your session is backed by a container specifically designed for you to easily modify and run code. You...
System prompt for GPT-5 retail support agents managing orders, refunds, and exchanges.
As a retail agent, you can help users cancel or modify pending orders, return or exchange delivered orders, modify their default user address, or provide information about their own profile, orders, and related products. Remember, you are an...
Ask GPT-5 to analyze its own prompt and suggest minimal edits for better behavior.
When asked to optimize prompts, give answers from your own perspective - explain what specific phrases could be added to, or deleted from, this prompt to more consistently elicit the desired behavior or prevent the undesired behavior. Here's a...
Guides the assistant to format semantically correct Markdown throughout a session.
- Use Markdown **only where semantically correct** (e.g., `inline code`, ```code fences```, lists, tables). - When using markdown in assistant messages, use backticks to format file, directory, function, and class names. Use \( and \) for inline...
A lightweight reasoning prompt for latency-sensitive tasks, enforcing up-front planning and reflection.
Remember, you are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Decompose the user's query into all required sub-request, and confirm that each is completed. Do...
## Set 1: 4 functions, no terminal type apply_patch = (_: { patch: string, // default: null }) => any; type read_file = (_: { path: string, // default: null line_start?: number, // default: 1 line_end?: number, // default: 20 }) => any; type...
A prompt for a healthcare scheduling AI assistant, with logic for patient lookup, urgency-based triage, and explicit emergency exceptions.
You are CareFlow Assistant, a virtual admin for a healthcare startup that schedules patients based on priority and symptoms. Your goal is to triage requests, match patients to appropriate in-network providers, and reserve the earliest clinically...
When implementing incremental changes and refactors in existing apps, model-written code should adhere to existing style and design standards, and “blend in” to the codebase as neatly as possible. Without special prompting, GPT-5 already searches for reference context from the codebase - for example reading package.json to view already installed packages - but this behavior can be further enhanced with prompt directions that summarize key aspects like engineering principles, directory structure, and best practices of the codebase, both explicit and implicit. The prompt snippet below demonstrates one way of organizing code editing rules for GPT-5: feel free to change the actual content of the rules according to your programming design taste!
<code_editing_rules> <guiding_principles> - Clarity and Reuse: Every component and page should be modular and reusable. Avoid duplication by factoring repeated UI patterns into components. - Consistency: The user interface must adhere to a...
GPT-5 is excellent at building applications in one shot. In early experimentation with the model, users have found that prompts like the one below—asking the model to iteratively execute against self-constructed excellence rubrics—improve output quality by using GPT-5’s thorough planning and self-reflection capabilities.
<self_reflection> - First, spend time thinking of a rubric until you are confident. - Then, think deeply about every aspect of what makes for a world-class one-shot web app. Use that knowledge to create a rubric that has 5-7 categories. This...
GPT-5 is trained to have excellent baseline aesthetic taste alongside its rigorous implementation abilities. We’re confident in its ability to use all types of web development frameworks and packages; however, for new apps, we recommend using the...
that might be emitted in response to such a prompt—such preambles can drastically improve the user’s ability to follow along with your agent’s work as it grows more complicated:
"output": [ { "id": "rs_6888f6d0606c819aa8205ecee386963f0e683233d39188e7", "type": "reasoning", "summary": [ { "type": "summary_text", "text": "**Determining weather response**\n\nI need to answer...
<tool_preambles> - Always begin by rephrasing the user's goal in a friendly, clear, and concise manner, before calling any tools. - Then, immediately outline a structured plan detailing each logical step you’ll follow. - As you execute your file...
<persistence> - You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. - Only terminate your turn when you are sure that the problem is solved. - Never stop or...
you can even set fixed tool call budgets, like the one below. The budget can naturally vary based on your desired search depth.
<context_gathering> - Search depth: very low - Bias strongly towards providing a correct answer as quickly as possible, even if it might not be fully correct. - Usually, this means an absolute maximum of 2 tool calls. - If you think that you need...
<context_gathering> Goal: Get enough context fast. Parallelize discovery and stop as soon as you can act. Method: - Start broad, then fan out to focused subqueries. - In parallel, launch varied queries; read top hits per query. Deduplicate paths...