💡 How to use these prompts:
Replace everything in [BRACKETS] with your specific details before sending.
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[OPENAI CODEX — PERSISTENT SESSION]
Assign the following task. Work through it completely and open a PR when done. Do not wait for me.
Task: [DESCRIBE THE CHANGE IN PLAIN ENGLISH]
Repository: [REPO NAME OR CONTEXT]
Branch: create a new branch named [feature/TASK-NAME]
Tests: write or update tests for every change you make
PR description: include what changed, why, and how to test it
When the PR is open, summarise what you did in 3 bullet points.
I have a production error. Help me debug it.
Error message: [PASTE THE FULL ERROR AND STACK TRACE]
What the user was doing: [DESCRIBE THE USER ACTION THAT TRIGGERED IT]
Code involved: [PASTE THE RELEVANT CODE]
When it started: [DATE OR DEPLOY — did it start after a change?]
Frequency: [ALWAYS / INTERMITTENT / AFTER X USES]
Diagnose:
1. What is causing this error? (Be specific about code location)
2. Is this a code bug, a data problem, or an environment issue?
3. The fix
4. A test to verify the fix
5. Whether this error pattern could appear elsewhere in the codebase
Implement a function tool for the OpenAI API that does the following.
Tool purpose: [DESCRIBE WHAT THE TOOL DOES — what the model can call it for]
Function name: [SNAKE_CASE NAME]
Parameters: [LIST WHAT THE FUNCTION TAKES AND THEIR TYPES]
Returns: [WHAT IT GIVES BACK TO THE MODEL]
Side effects: [ANY EXTERNAL CALLS, DATABASE WRITES, ETC.]
Language: [PYTHON / TYPESCRIPT / OTHER]
Output:
1. The tool schema (JSON for the tools parameter)
2. The function implementation
3. Example of calling the API with this tool and handling the function call response
Build the following agent chain. Output complete, working code.
Agent task: [DESCRIBE WHAT THE AGENT SHOULD DO — multi-step, what tools it needs]
Framework: [LANGCHAIN / VANILLA OPENAI / OTHER]
Tools needed: [LIST — e.g. web search, calculator, database lookup, code executor]
Language: [PYTHON / TS]
Error handling: [WHAT TO DO IF A TOOL CALL FAILS]
Output:
1. Complete code with all imports
2. Tool definitions
3. Agent loop
4. Example run with output
5. Where to extend it if I want to add another tool later
I have a prompt that is not performing well. Help me improve it.
Model I am using: [GPT-5.6 SOL / TERRA / LUNA]
What the prompt is for: [DESCRIBE THE TASK]
My current prompt: [PASTE PROMPT]
What is going wrong: [DESCRIBE THE FAILURE MODE — too verbose, wrong format, misses edge cases, etc.]
Diagnose what is wrong with the current prompt, then rewrite it to fix the specific issue. Show me:
1. What was wrong (specific, not generic advice)
2. The improved prompt
3. One example input where the old prompt would fail and the new one would succeed
Extract the following structured data from the text below. Output only the data — no prose, no explanation.
What to extract: [DESCRIBE PRECISELY — e.g. company names, dates, dollar amounts, person names and their roles]
Output format: [JSON / CSV / MARKDOWN TABLE]
If a field is missing from the text: write null / n/a / "not found" — do not infer or guess
If a value is ambiguous: include it with a flag like "(uncertain)"
Text: [PASTE TEXT]
Double-check: read the output against the text to confirm every value matches what is written.