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: TypeError
Message: Couldn't cast array of type string to null
Traceback: 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 "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2281, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2233, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1804, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2095, in cast_array_to_feature
return array_cast(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1806, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1957, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
My AI System Prompt Library
This repository contains a comprehensive, up-to-date library of system prompts for AI systems and autonomous agents, started on May 27th, 2025.
Overview
This collection houses 923 system prompts covering a diverse range of AI applications. The prompts include configurations for autonomous agents, simple chatbots, specialized assistants, and various AI-powered tools. This repository serves as a centralized hub for these prompts, maintained through automated workflows that ensure the latest versions are always available.
The library is periodically updated as new system prompts are recorded and saved from ongoing AI projects and experiments.
Repository Contents
Prompt Types
- Autonomous Agents: Complex multi-step reasoning and task execution
- Chatbots: Conversational AI with specific personalities or expertise
- Specialized Assistants: Domain-specific helpers (technical, creative, analytical)
- Tool Integrations: Prompts designed for specific AI platforms and services
Storage Formats
System prompts are organized into two main formats:
- JSON Format: Structured data with metadata (
system-prompts/json/- 923 files) - Markdown Format: Human-readable documentation (
system-prompts/md/- 851 files)
Repository Structure
├── index.md # Main searchable index (markdown table)
├── consolidated_prompts.json # All prompts in single JSON file
├── system-prompts/ # Individual prompt files
│ ├── json/ # JSON format prompts
│ └── md/ # Markdown format prompts
├── scripts/ # Maintenance scripts
│ ├── consolidate_prompts.py # JSON consolidation
│ ├── generate_index.py # Markdown index generation
│ ├── update_all.py # Master update script
│ └── *.sh # Shell wrappers
└── update_library.sh # Main update script (run from root)
How It Works
- Prompts are entered into a structured database with detailed metadata
- Tagged entries marked for export are automatically processed and added to this repository
- Changes are pushed via automation, ensuring the collection stays current with new developments
- Both individual files and consolidated formats are maintained for different use cases
JSON Data Dictionary
Each system prompt is stored as a JSON file with the following standardized fields:
Core Fields
| Field | Type | Description |
|---|---|---|
agentname |
String | Display name of the AI agent or assistant |
description |
String | Brief description of the agent's purpose and capabilities |
systemprompt |
String | The complete system prompt text used to configure the AI |
creation_date |
String | ISO timestamp of when the prompt was created |
Integration & Links
| Field | Type | Description |
|---|---|---|
chatgptlink |
String/null | URL to ChatGPT custom GPT implementation (if available) |
Capability Flags
| Field | Type | Description |
|---|---|---|
is-agent |
Boolean | Whether this is a complex autonomous agent (vs simple assistant) |
is-single-turn |
String | "true"/"false" - Whether designed for single interactions only |
structured-output-generation |
String | "true"/"false" - Can generate structured data formats |
image-generation |
String | "true"/"false" - Includes image generation capabilities |
data-utility |
String | "true"/"false" - Designed for data processing/analysis tasks |
personalised-system-prompt |
String | "true"/"false" - Contains user-specific personalization |
Advanced Configuration
| Field | Type | Description |
|---|---|---|
json-schema |
Object/null | JSON schema definition for structured outputs (if applicable) |
json-example |
String/null | Example JSON output format (if applicable) |
depersonalised-system-prompt |
String/null | Generic version without personal references |
chatgpt-privacy |
String/null | Privacy settings for ChatGPT implementations |
Notes
- Boolean values are stored as strings ("true"/"false") for consistency
nullvalues indicate the field is not applicable to that particular prompt- All prompts include the core fields; advanced fields are optional based on functionality
Accessing Prompts
- Browse: Use
index.mdfor a searchable table of all prompts - Bulk access: Use
consolidated_prompts.jsonfor programmatic access - Individual files: Browse
system-prompts/json/for specific prompts
Purpose
This collection is intended for:
- Reuse across AI projects and experiments
- Open source distribution and community sharing
- Benchmarking and prompt engineering research
- Reference library for AI system development
Status
This repository is autogenerated and updated periodically. The collection represents an evolving library of proven system prompts from real-world AI applications.
Authorship
All system prmopt by me (Daniel Rosehill) with some drafting assistance with AI.
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