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Datasets:
danielrosehill
/
System-Prompt-Library-030825

Tasks:
Text Generation
Languages:
English
Tags:
system-prompts
ai-agents
prompt-engineering
conversational-ai
llm
chatgpt
DOI:
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Expected object or value
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 306, in _generate_tables
                  batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
                  examples = [ujson_loads(line) for line in original_batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value

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System Prompts Dataset - August 2025

Point-in-time export from Daniel Rosehill's system prompt library as of August 3rd, 2025

Overview

This repository contains a comprehensive collection of 944 system prompts designed for various AI applications, agent workflows, and conversational AI systems. While many of these prompts now serve as the foundation for more complex agent-based workflows, they continue to provide essential building blocks for AI system design and implementation.

Dataset Structure

The dataset is provided as a single JSON file (prompts.json) containing:

  • 944 unique system prompts covering diverse use cases
  • Structured metadata including generation timestamp and source information
  • Detailed prompt descriptions and categorization
  • Links to associated ChatGPT implementations where applicable
  • Agent workflow indicators and configuration flags

JSON Schema

Each prompt entry contains:

{
  "agentname": "Descriptive name for the prompt/agent",
  "description": "Brief description of the prompt's purpose",
  "systemprompt": "The actual system prompt text",
  "chatgptlink": "URL to ChatGPT implementation (if available)",
  "json-schema": "Associated JSON schema (if applicable)",
  "is-agent": "Boolean indicating if this is part of an agent workflow",
  "is-single-turn": "Boolean indicating conversation type"
}

Use Cases

Foundation for Agent Workflows

Many prompts in this collection serve as the base layer for sophisticated agent-based systems, providing:

  • Initial system context and behavior definition
  • Role-specific instructions for specialized agents
  • Consistent interaction patterns across agent networks

Direct Implementation

Prompts can be used directly for:

  • Conversational AI systems
  • Task-specific AI assistants
  • Content generation workflows
  • Analysis and processing pipelines

Research and Development

  • Prompt engineering research
  • AI behavior analysis
  • System prompt effectiveness studies
  • Comparative AI system development

Categories Covered

The collection spans numerous domains including:

  • Task management and productivity
  • Content creation and editing
  • Technical assistance and coding
  • Analysis and research support
  • Creative and artistic applications
  • Business and professional workflows
  • Educational and training scenarios

Usage

Loading the Dataset 

import json

with open('prompts.json', 'r') as f:
    data = json.load(f)
    
prompts = data['prompts']
metadata = data['metadata']

print(f"Total prompts: {metadata['total_prompts']}")

Filtering by Type 

# Get agent-based prompts
agent_prompts = [p for p in prompts if p.get('is-agent', False)]

# Get single-turn prompts
single_turn = [p for p in prompts if p.get('is-single-turn') == 'true']

Dataset Metadata

  • Generated: August 3rd, 2025
  • Total Prompts: 944
  • Format: JSON
  • Source: Daniel Rosehill's system prompt library
  • License: [Please specify license]

Evolution and Context

This dataset represents a snapshot of an actively maintained system prompt library. While the AI landscape continues to evolve toward more sophisticated agent-based architectures, these foundational prompts remain valuable for:

  1. Rapid prototyping of new AI applications
  2. Educational purposes in prompt engineering
  3. Baseline establishment for AI system behavior
  4. Component reuse in larger agent frameworks

Contact

For questions, feedback, or collaboration opportunities:

This dataset reflects the state of system prompt development as of August 3rd, 2025, and serves as both a practical resource and a historical snapshot of AI prompt engineering practices.
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