Dataset Viewer
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:    ArrowInvalid
Message:      JSON parse error: Column() changed from object to string in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 174, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
                  obj = self._get_object_parser(self.data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
                  self._parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1402, in _parse
                  self.obj = DataFrame(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/frame.py", line 778, in __init__
                  mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
                  return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
                  index = _extract_index(arrays)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 680, in _extract_index
                  raise ValueError(
              ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2361, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1905, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 499, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 346, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 177, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0

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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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