What can chatgpt_system_prompt do?

automated-toc-generation-for-prompt-collections, prompt-metadata-parsing-and-standardization, custom-gpt-and-development-ide-assistant-patterns, multi-source-prompt-aggregation-and-curation, prompt-injection-and-jailbreak-technique-documentation, custom-gpt-discovery-and-browsing, system-prompt-comparison-across-vendors, contribution-workflow-and-validation-guidelines, prompt-file-enumeration-and-lookup, official-ai-product-prompt-documentation, open-source-ai-project-prompt-collection

chatgpt_system_prompt

PromptFree

A collection of GPT system prompts and various prompt injection/leaking knowledge.

Open Source

/ 100

11 capabilities

Capabilities11 decomposed

automated-toc-generation-for-prompt-collections

Medium confidence

Automatically generates and maintains table of contents (TOC) files across the repository using a GitHub Actions workflow that triggers on main branch pushes and PR merges. The system uses Python scripts (idxtool.py, gptparser.py) to enumerate prompt files, parse their metadata, and rebuild TOC.md files in the root and all subdirectories under /prompts/, ensuring navigation links remain current as new prompts are added or modified without manual intervention.

Solves for

I need to keep my prompt collection indexed and navigable as I add hundreds of new promptsI want contributors to submit new prompts without manually updating table of contentsI need to generate hierarchical navigation for 1,100+ custom GPTs organized by category

Best for

open-source prompt repositories with frequent community contributions

teams managing large collections of AI system prompts across multiple categories

documentation maintainers who want to avoid manual TOC updates

Requires

GitHub repository with Actions enabled

Python 3.6+ for idxtool.py and gptparser.py scripts

.github/workflows/build-toc.yaml configuration file

Limitations

Requires GitHub Actions CI/CD integration — not portable to other version control systems without modification

TOC generation latency increases linearly with repository size (1,100+ GPTs may take 10-30 seconds per rebuild)

No incremental indexing — rebuilds entire TOC on every trigger rather than delta updates

What makes it unique

Uses a dual-script approach (idxtool.py for orchestration, gptparser.py for metadata extraction) with GitHub Actions automation to maintain consistency across 1,100+ prompts organized in three separate collections (gpts, official-product, opensource-prj), each with its own TOC hierarchy. The rebuild_toc() and generate_toc_for_prompts_dirs() functions ensure both root-level and subdirectory TOCs stay synchronized.

vs alternatives

More automated than manual TOC maintenance and more scalable than static documentation, but less sophisticated than full-text search indices or semantic navigation systems that some larger documentation projects use.

prompt-metadata-parsing-and-standardization

Medium confidence

Parses markdown prompt files using gptparser.py to extract and standardize metadata fields (name, description, author, tags, etc.) from YAML frontmatter and markdown headers. The parser maintains a dictionary of supported fields with display names and processing order, enabling consistent formatting across heterogeneous prompt sources (official OpenAI/Anthropic products, community GPTs, open-source projects) and enabling downstream indexing and search capabilities.

Solves for

I need to extract structured metadata from unstructured prompt markdown files to build searchable indicesI want to normalize prompt metadata across different sources (ChatGPT, Claude, Grok) that use different documentation conventionsI need to validate that contributed prompts follow the repository's metadata schema before merging

Best for

prompt repository maintainers aggregating prompts from multiple vendors with inconsistent formats

teams building searchable prompt databases or discovery tools

developers creating prompt validation pipelines for CI/CD workflows

Requires

Python 3.6+ with standard library (no external dependencies mentioned)

Markdown files with YAML frontmatter or standardized header format

Consistent field naming conventions across all prompt files

Limitations

Markdown-only parsing — cannot extract metadata from JSON, YAML, or other structured formats without additional parsers

Fragile to formatting variations — prompts with non-standard YAML frontmatter or missing headers may fail to parse completely

No semantic understanding of prompt content — extracts only syntactic metadata fields, not capability descriptions or use-case inference

What makes it unique

Implements a field-mapping dictionary that defines both display names and processing order for metadata fields, allowing flexible extraction from heterogeneous prompt sources (ChatGPT system prompts, Claude Code system, Grok jailbreak prompts, custom GPTs) without requiring source-specific parsers. The gptparser.py module handles both YAML frontmatter and markdown-embedded metadata.

vs alternatives

More flexible than regex-based extraction because it uses structured YAML parsing, but less robust than full AST-based markdown parsing (e.g., tree-sitter) which would handle edge cases like nested code blocks or escaped characters.

custom-gpt-and-development-ide-assistant-patterns

Medium confidence

Documents patterns and system prompts for custom GPTs and development IDE assistants (including Grimoire Coding Assistant and other specialized tools) organized in /prompts/gpts/. The collection includes 1,100+ examples of how developers structure prompts for specific domains (coding, finance, education, etc.), providing a comprehensive reference for understanding custom GPT design patterns and specialized assistant architectures.

Solves for

I want to study how successful custom GPTs structure their system prompts for specific domainsI need examples of coding assistant prompts to understand best practices for developer toolsI want to learn from the Grimoire Coding Assistant and other specialized IDE assistants

Best for

developers building custom GPTs for specialized domains

teams designing AI-powered IDE assistants and developer tools

prompt engineers studying domain-specific prompt patterns

Requires

GitHub repository access to /prompts/gpts/ directory

Understanding of custom GPT architecture and system prompt design

Ability to browse and compare multiple prompt examples

Limitations

No quality filtering — all 1,100+ GPTs are included regardless of effectiveness or adoption

No usage metrics — cannot identify which patterns are most successful or widely used

No domain-specific organization beyond directory structure — no semantic tagging or capability-based search

What makes it unique

Aggregates 1,100+ custom GPT prompts organized by domain (coding, finance, education, etc.) with specific examples like Grimoire Coding Assistant, providing a comprehensive reference for understanding how developers structure prompts for specialized tasks. The scale (1,100+ examples) enables pattern analysis across diverse use cases.

vs alternatives

More comprehensive than individual GPT examples because it provides 1,100+ patterns in one place, but less curated than specialized prompt engineering courses or frameworks that provide guided learning paths.

multi-source-prompt-aggregation-and-curation

Medium confidence

Aggregates and organizes system prompts from three distinct sources (official-product: ChatGPT/Claude/Grok, gpts: 1,100+ community-created custom GPTs, opensource-prj: open-source AI projects) into a unified repository structure with separate TOC hierarchies. The architecture uses directory-based organization (/prompts/gpts/, /prompts/official-product/, /prompts/opensource-prj/) to maintain source separation while enabling cross-source discovery and comparison through unified indexing.

Solves for

I want to study how different AI vendors (OpenAI, Anthropic, xAI) structure their system prompts to understand best practicesI need to find and compare community-created GPTs across different domains (coding, finance, education) in one placeI want to contribute my own custom GPT prompt to a curated collection without forking the entire repository

Best for

prompt engineers and researchers studying AI system prompt design patterns

developers building custom GPTs who want to learn from existing examples

teams evaluating different AI vendors' instruction-following approaches

Requires

GitHub repository with write access for contributions

Understanding of repository structure and contribution guidelines (CONTRIBUTING.md)

Markdown files following the standardized prompt format

Limitations

No deduplication across sources — identical or near-identical prompts from different vendors are stored separately, increasing repository size

No version control for prompt evolution — historical changes to official product prompts are not tracked

Limited metadata about prompt provenance — no timestamps, source URLs, or licensing information for community GPTs

What makes it unique

Maintains three parallel prompt collections (official-product with 141+ entries, gpts with 1,100+ entries, opensource-prj with 20+ entries) in separate directory hierarchies, each with its own TOC, enabling both source-specific browsing and cross-source comparison. The architecture preserves source identity while enabling unified discovery through the root-level TOC.md.

vs alternatives

More comprehensive than vendor-specific prompt collections (e.g., OpenAI's official docs alone) because it includes community contributions and competing vendors, but less curated than specialized prompt marketplaces that apply quality filters or user ratings.

prompt-injection-and-jailbreak-technique-documentation

Medium confidence

Documents and catalogs prompt injection techniques, jailbreak methods, and prompt leaking knowledge as a research and educational resource. The repository includes specific files like GrokJailbreakPrompt.md and security-focused documentation (SECURITY.md) that explain how system prompts can be extracted, bypassed, or manipulated, serving as both a learning resource and a reference for understanding AI safety vulnerabilities.

Solves for

I need to understand common prompt injection and jailbreak techniques to build safer AI systemsI want to study real-world examples of prompt leaking to improve my system prompt designI need to document known vulnerabilities in AI systems for security research and red-teaming

Best for

AI safety researchers and red-teamers evaluating system prompt robustness

security engineers building guardrails against prompt injection attacks

developers designing custom GPTs who want to understand attack vectors

Requires

Understanding of AI system prompts and instruction-following mechanisms

Familiarity with prompt engineering concepts

Ethical commitment to responsible disclosure and security research

Limitations

Educational content only — does not provide automated detection or mitigation of prompt injection attacks

Techniques may become outdated as AI vendors patch vulnerabilities or improve instruction-following

No systematic taxonomy of injection techniques — documentation is ad-hoc rather than comprehensively categorized

What makes it unique

Explicitly documents prompt injection and jailbreak techniques (e.g., GrokJailbreakPrompt.md) as part of the repository's educational mission, treating security vulnerabilities as learning opportunities rather than hiding them. The SECURITY.md file provides contribution guidelines for responsibly documenting vulnerabilities.

vs alternatives

More transparent and educational than vendor security advisories that often withhold technical details, but less systematic than academic security research papers that provide formal vulnerability taxonomies and impact assessments.

custom-gpt-discovery-and-browsing

Medium confidence

Enables discovery and browsing of 1,100+ community-created custom GPTs through hierarchical organization by category (coding, finance, education, etc.) with automated TOC generation and file enumeration. The enum_gpts() and find_gptfile() functions in idxtool.py support both directory-based browsing and ID/URL-based lookup, allowing users to search for GPTs by name, category, or functionality without requiring a database backend.

Solves for

I want to find existing custom GPTs in a specific domain (e.g., quantitative finance) to understand how others structure themI need to browse all available GPTs organized by category to discover new tools and use casesI want to look up a specific GPT by its ID or URL to view its system prompt and configuration

Best for

developers and prompt engineers exploring custom GPT design patterns

non-technical users discovering useful GPTs for their workflows

teams evaluating community-created solutions before building in-house alternatives

Requires

GitHub repository access (read-only for browsing)

Understanding of directory structure (/prompts/gpts/)

Markdown viewer or text editor to read GPT files

Limitations

No full-text search — discovery is limited to directory browsing and file enumeration, not semantic search across prompt content

No user ratings or popularity metrics — all GPTs are presented equally regardless of quality or adoption

No filtering by capability or use case — users must manually browse categories to find relevant GPTs

What makes it unique

Implements enum_gpts() and find_gptfile() functions that enable both directory-based enumeration and ID/URL-based lookup of 1,100+ custom GPTs without requiring a database or search index. The file naming convention (e.g., tveXvXU5g_QuantFinance.md) embeds the GPT ID, enabling reverse lookup from URL to local file.

vs alternatives

More accessible than the official OpenAI GPT Store because it provides source-level access to system prompts and configuration, but less discoverable than the GPT Store's UI-based search and recommendation system.

system-prompt-comparison-across-vendors

Medium confidence

Enables side-by-side comparison of system prompts from different AI vendors (OpenAI ChatGPT, Anthropic Claude, xAI Grok, Google AI tools) by organizing official product prompts in /prompts/official-product/ with vendor-specific subdirectories. Users can examine how different vendors structure instructions, handle edge cases, and implement safety guidelines by reading and comparing prompts like ChatGPT system.md, Claude Code System, and Grok2.md/Grok3.md files.

Solves for

I want to understand how OpenAI, Anthropic, and xAI differ in their system prompt design and instruction-following approachesI need to study vendor-specific safety guidelines and constraint handling to inform my own system prompt designI want to analyze how different vendors structure prompts for specialized tasks (code generation, research, deep search)

Best for

prompt engineers studying best practices across different AI vendors

researchers analyzing AI instruction-following and alignment approaches

teams evaluating which vendor's approach best fits their use case

Requires

GitHub repository access to /prompts/official-product/ directory

Understanding of different vendors' product lines (ChatGPT, Claude, Grok, etc.)

Ability to read and interpret system prompts

Limitations

Static snapshots only — prompts are not automatically updated when vendors release new versions, requiring manual updates

No version history — cannot track how vendor prompts evolve over time or compare across versions

Incomplete coverage — not all vendor products are documented (e.g., missing some Google AI tools)

What makes it unique

Maintains official product prompts from multiple competing vendors (OpenAI, Anthropic, xAI, Google) in a single repository, enabling direct comparison of instruction-following approaches. The /prompts/official-product/ directory includes vendor-specific subdirectories (chatwise, manus, xai) with multiple versions (e.g., Grok2.md, Grok3.md, Grok3WithDeepSearch.md) showing how vendors iterate on their system prompts.

vs alternatives

More comprehensive than individual vendor documentation because it aggregates multiple vendors in one place, but less authoritative than official vendor documentation and may lag behind actual deployed prompts.

contribution-workflow-and-validation-guidelines

Medium confidence

Provides structured contribution guidelines (CONTRIBUTING.md) and security policies (SECURITY.md) that define how community members can submit new prompts, validate metadata, and ensure quality standards. The workflow integrates with GitHub's pull request system and automated TOC generation, enabling contributors to add new prompts without manually updating indices while maintaining repository integrity through validation checks.

Solves for

I want to contribute my custom GPT prompt to the repository without manually updating table of contentsI need to understand what metadata and format are required for my prompt submission to be acceptedI want to report a security vulnerability or jailbreak technique responsibly without enabling malicious use

Best for

open-source contributors wanting to share prompt innovations with the community

prompt engineers building custom GPTs who want their work to be discoverable

security researchers responsibly disclosing AI vulnerabilities

Requires

GitHub account with repository fork/PR permissions

Understanding of markdown format and YAML frontmatter

Familiarity with CONTRIBUTING.md and SECURITY.md guidelines

Limitations

Manual review required — no automated validation of prompt quality, safety, or effectiveness before merge

No contributor reputation system — all contributors are treated equally regardless of past contributions

Limited guidance on prompt quality — CONTRIBUTING.md specifies format but not content quality standards

What makes it unique

Integrates contribution guidelines with automated TOC generation, allowing contributors to submit new prompts via pull requests without manually updating indices. The SECURITY.md file provides specific guidance for responsibly disclosing prompt injection and jailbreak techniques, treating security vulnerabilities as educational opportunities rather than suppressing them.

vs alternatives

More community-friendly than closed prompt collections because it enables open contributions, but less structured than platforms with automated quality checks, duplicate detection, or contributor reputation systems.

prompt-file-enumeration-and-lookup

Medium confidence

Implements find_gptfile() function in idxtool.py that enables both ID-based and URL-based lookup of prompt files across the repository. The function supports searching by GPT ID (embedded in filenames like tveXvXU5g_QuantFinance.md) or by OpenAI GPT URL, returning the corresponding markdown file path and enabling programmatic access to prompt content without requiring a database backend.

Solves for

I have a GPT ID or URL and need to find the corresponding system prompt in the repositoryI want to programmatically access prompt files by ID for integration with other tools or scriptsI need to verify that a specific GPT's prompt is documented in the repository

Best for

developers building tools that integrate with the prompt repository

researchers programmatically analyzing prompt collections

automation scripts that need to fetch specific prompts by ID

Requires

Python 3.6+ with file system access

Knowledge of GPT ID format or OpenAI GPT URL structure

idxtool.py script available in repository

Limitations

Requires exact ID or URL match — no fuzzy matching or partial search capability

File-system based lookup — scales linearly with repository size, no indexing for O(1) lookup

No caching — repeated lookups require re-scanning the file system

What makes it unique

Implements find_gptfile() that supports both GPT ID-based lookup (parsing IDs from filenames) and URL-based lookup (extracting IDs from OpenAI GPT URLs), enabling multiple access patterns without requiring a database. The function integrates with enum_gpts() to build an in-memory index of available GPTs.

vs alternatives

More flexible than static file paths because it supports multiple lookup methods (ID and URL), but slower than database-backed lookup and requires file system access rather than working over HTTP.

official-ai-product-prompt-documentation

Medium confidence

Documents system prompts and configuration from official AI products including OpenAI ChatGPT, Anthropic Claude (with Code System and agent loop documentation), xAI Grok (versions 2, 3, and 3 with deep search), and Google AI tools. The /prompts/official-product/ directory includes not just system prompts but also capability descriptions, tool configurations (tools.json), and agent loop documentation, providing comprehensive insight into how commercial AI products are structured.

Solves for

I want to understand how OpenAI structures ChatGPT's system prompt and instruction hierarchyI need to study Anthropic's Claude Code System to understand code generation best practicesI want to compare Grok's jailbreak-resistant prompts across versions 2 and 3

Best for

prompt engineers studying commercial AI product design

teams building AI products who want to learn from vendor approaches

researchers analyzing how different vendors implement instruction-following

Requires

GitHub repository access to /prompts/official-product/ directory

Understanding of different vendor product lines and versions

Ability to interpret system prompts and configuration files

Limitations

Prompts may be outdated — vendors update system prompts frequently, and repository updates are manual

Incomplete product coverage — not all vendor products or versions are documented

No official endorsement — prompts are reverse-engineered or leaked, not officially published by vendors

What makes it unique

Aggregates official product prompts from multiple vendors (OpenAI, Anthropic, xAI, Google) with vendor-specific subdirectories that include not just system prompts but also capability descriptions (capabilities.md), tool configurations (tools.json), and agent loop documentation (agentloop.md). This provides a more complete picture of how commercial products are structured than system prompts alone.

vs alternatives

More comprehensive than individual vendor documentation because it includes multiple vendors and versions in one place, but less authoritative than official vendor sources and may lag behind actual deployed systems.

open-source-ai-project-prompt-collection

Medium confidence

Catalogs system prompts and prompt engineering patterns from open-source AI projects (20+ entries in /prompts/opensource-prj/) including projects like II Agent, GAIA systems, and others. This collection documents how open-source developers structure prompts for specialized tasks, enabling knowledge sharing and pattern reuse across the open-source AI community.

Solves for

I want to study how open-source AI projects structure their system prompts and agent loopsI need to find examples of prompt engineering patterns used in production open-source systemsI want to contribute my open-source AI project's prompts to a community collection

Best for

open-source AI developers learning from peer implementations

researchers studying prompt engineering patterns in production systems

teams building open-source AI projects who want to share their approaches

Requires

GitHub repository access to /prompts/opensource-prj/ directory

Understanding of open-source AI project landscape

Ability to read and interpret system prompts

Limitations

Limited coverage — only 20+ open-source projects documented compared to 1,100+ custom GPTs

Inconsistent documentation quality — open-source projects may have varying levels of prompt documentation

No maintenance guarantees — open-source projects may become inactive, making their prompts outdated

What makes it unique

Maintains a dedicated collection of prompts from open-source AI projects (II Agent, GAIA systems, etc.) separate from commercial products and community GPTs, enabling developers to learn from production open-source implementations. The /prompts/opensource-prj/ directory treats open-source projects as a distinct knowledge source with its own TOC and organization.

vs alternatives

More accessible than reading source code directly because prompts are extracted and documented, but less comprehensive than full project documentation and may not reflect the latest project versions.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Best For

✓open-source prompt repositories with frequent community contributions
✓teams managing large collections of AI system prompts across multiple categories
✓documentation maintainers who want to avoid manual TOC updates
✓prompt repository maintainers aggregating prompts from multiple vendors with inconsistent formats
✓teams building searchable prompt databases or discovery tools
✓developers creating prompt validation pipelines for CI/CD workflows
✓developers building custom GPTs for specialized domains
✓teams designing AI-powered IDE assistants and developer tools

Known Limitations

⚠Requires GitHub Actions CI/CD integration — not portable to other version control systems without modification
⚠TOC generation latency increases linearly with repository size (1,100+ GPTs may take 10-30 seconds per rebuild)
⚠No incremental indexing — rebuilds entire TOC on every trigger rather than delta updates
⚠Markdown-only output format — cannot generate alternative documentation formats (HTML, JSON indices)
⚠Markdown-only parsing — cannot extract metadata from JSON, YAML, or other structured formats without additional parsers
⚠Fragile to formatting variations — prompts with non-standard YAML frontmatter or missing headers may fail to parse completely

Requirements

GitHub repository with Actions enabledPython 3.6+ for idxtool.py and gptparser.py scripts.github/workflows/build-toc.yaml configuration fileStandardized markdown format for prompt files with parseable metadata headersPython 3.6+ with standard library (no external dependencies mentioned)Markdown files with YAML frontmatter or standardized header formatConsistent field naming conventions across all prompt filesGitHub repository access to /prompts/gpts/ directory

Input / Output

Accepts: markdown files with YAML/frontmatter metadata, directory structure under /prompts/ with subdirectories (gpts, official-product, opensource-prj), markdown files (.md) with YAML frontmatter, markdown headers and structured text sections, custom GPT system prompts, domain-specific prompt patterns (coding, finance, education, etc.), IDE assistant and specialized tool prompts, system prompts from OpenAI ChatGPT, Anthropic Claude, xAI Grok, custom GPT definitions with metadata, open-source AI project prompts, markdown documentation of jailbreak techniques, example prompts demonstrating injection vulnerabilities, security guidelines and best practices, GPT file names and directory structure, GPT metadata (ID, name, description), official system prompts from OpenAI, Anthropic, xAI, Google, vendor-specific prompt variations (e.g., Grok2 vs Grok3, Grok3WithDeepSearch), new prompt markdown files with metadata, pull request descriptions explaining contributions, security vulnerability reports, GPT ID string (e.g., 'tveXvXU5g'), OpenAI GPT URL (e.g., 'https://chatgpt.com/g/g-tveXvXU5g'), official system prompts from ChatGPT, Claude, Grok, Google AI, capability descriptions and tool configurations, agent loop documentation, system prompts from open-source AI projects, agent loop and capability documentation, prompt engineering patterns and best practices

Produces: markdown TOC files (TOC.md), hierarchical navigation links with file paths, parsed metadata dictionary with standardized field names, validation results indicating missing or malformed fields, markdown files containing custom GPT prompts, organized examples by domain and use case, pattern references for building similar GPTs, organized markdown files in source-specific directories, hierarchical TOC files enabling navigation by source and category, educational markdown files explaining attack vectors, example prompts showing vulnerability demonstrations, security recommendations and mitigation strategies, list of available GPTs organized by category, markdown files containing GPT system prompts and configuration, file paths and URLs for direct access, markdown files containing vendor system prompts, organized directory structure enabling vendor-by-vendor comparison, merged prompt files in appropriate directory, updated TOC files (auto-generated), contributor acknowledgment in repository, file path to corresponding markdown prompt file, boolean indicating whether GPT is found in repository, markdown files containing system prompts, JSON configuration files (tools.json), capability and agent loop documentation, markdown files containing project prompts, documentation of prompt patterns and techniques, links to original open-source projects

UnfragileRank

Adoption35%(20% weight)

Quality42%(30% weight)

Ecosystem49%(15% weight)

Match Graph10%(30% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Prompt

11 capabilities

Visit chatgpt_system_prompt→

Repository Details

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License

Topics

gptpromptprompt-engineering

Last commit: Apr 20, 2026

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A collection of GPT system prompts and various prompt injection/leaking knowledge.

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Capabilities11 decomposed

automated-toc-generation-for-prompt-collections

Medium confidence

Solves for

Best for

open-source prompt repositories with frequent community contributions

teams managing large collections of AI system prompts across multiple categories

documentation maintainers who want to avoid manual TOC updates

Requires

GitHub repository with Actions enabled

Python 3.6+ for idxtool.py and gptparser.py scripts

.github/workflows/build-toc.yaml configuration file

Limitations

Requires GitHub Actions CI/CD integration — not portable to other version control systems without modification

TOC generation latency increases linearly with repository size (1,100+ GPTs may take 10-30 seconds per rebuild)

No incremental indexing — rebuilds entire TOC on every trigger rather than delta updates

What makes it unique

vs alternatives

prompt-metadata-parsing-and-standardization

Medium confidence

Solves for

Best for

prompt repository maintainers aggregating prompts from multiple vendors with inconsistent formats

teams building searchable prompt databases or discovery tools

developers creating prompt validation pipelines for CI/CD workflows

Requires

Python 3.6+ with standard library (no external dependencies mentioned)

Markdown files with YAML frontmatter or standardized header format

Consistent field naming conventions across all prompt files

Limitations

Markdown-only parsing — cannot extract metadata from JSON, YAML, or other structured formats without additional parsers

Fragile to formatting variations — prompts with non-standard YAML frontmatter or missing headers may fail to parse completely

No semantic understanding of prompt content — extracts only syntactic metadata fields, not capability descriptions or use-case inference

What makes it unique

vs alternatives

custom-gpt-and-development-ide-assistant-patterns

Medium confidence

Solves for

Best for

developers building custom GPTs for specialized domains

teams designing AI-powered IDE assistants and developer tools

prompt engineers studying domain-specific prompt patterns

Requires

GitHub repository access to /prompts/gpts/ directory

Understanding of custom GPT architecture and system prompt design

Ability to browse and compare multiple prompt examples

Limitations

No quality filtering — all 1,100+ GPTs are included regardless of effectiveness or adoption

No usage metrics — cannot identify which patterns are most successful or widely used

No domain-specific organization beyond directory structure — no semantic tagging or capability-based search

What makes it unique

vs alternatives

multi-source-prompt-aggregation-and-curation

Medium confidence

Solves for

Best for

prompt engineers and researchers studying AI system prompt design patterns

developers building custom GPTs who want to learn from existing examples

teams evaluating different AI vendors' instruction-following approaches

Requires

GitHub repository with write access for contributions

Understanding of repository structure and contribution guidelines (CONTRIBUTING.md)

Markdown files following the standardized prompt format

Limitations

No deduplication across sources — identical or near-identical prompts from different vendors are stored separately, increasing repository size

No version control for prompt evolution — historical changes to official product prompts are not tracked

Limited metadata about prompt provenance — no timestamps, source URLs, or licensing information for community GPTs

What makes it unique

vs alternatives

prompt-injection-and-jailbreak-technique-documentation

Medium confidence

Solves for

Best for

AI safety researchers and red-teamers evaluating system prompt robustness

security engineers building guardrails against prompt injection attacks

developers designing custom GPTs who want to understand attack vectors

Requires

Understanding of AI system prompts and instruction-following mechanisms

Familiarity with prompt engineering concepts

Ethical commitment to responsible disclosure and security research

Limitations

Educational content only — does not provide automated detection or mitigation of prompt injection attacks

Techniques may become outdated as AI vendors patch vulnerabilities or improve instruction-following

No systematic taxonomy of injection techniques — documentation is ad-hoc rather than comprehensively categorized

What makes it unique

vs alternatives

custom-gpt-discovery-and-browsing

Medium confidence

Solves for

Best for

developers and prompt engineers exploring custom GPT design patterns

non-technical users discovering useful GPTs for their workflows

teams evaluating community-created solutions before building in-house alternatives

Requires

GitHub repository access (read-only for browsing)

Understanding of directory structure (/prompts/gpts/)

Markdown viewer or text editor to read GPT files

Limitations

No full-text search — discovery is limited to directory browsing and file enumeration, not semantic search across prompt content

No user ratings or popularity metrics — all GPTs are presented equally regardless of quality or adoption

No filtering by capability or use case — users must manually browse categories to find relevant GPTs

What makes it unique

vs alternatives

system-prompt-comparison-across-vendors

Medium confidence

Solves for

Best for

prompt engineers studying best practices across different AI vendors

researchers analyzing AI instruction-following and alignment approaches

teams evaluating which vendor's approach best fits their use case

Requires

GitHub repository access to /prompts/official-product/ directory

Understanding of different vendors' product lines (ChatGPT, Claude, Grok, etc.)

Ability to read and interpret system prompts

Limitations

Static snapshots only — prompts are not automatically updated when vendors release new versions, requiring manual updates

No version history — cannot track how vendor prompts evolve over time or compare across versions

Incomplete coverage — not all vendor products are documented (e.g., missing some Google AI tools)

What makes it unique

vs alternatives

contribution-workflow-and-validation-guidelines

Medium confidence

Solves for

Best for

open-source contributors wanting to share prompt innovations with the community

prompt engineers building custom GPTs who want their work to be discoverable

security researchers responsibly disclosing AI vulnerabilities

Requires

GitHub account with repository fork/PR permissions

Understanding of markdown format and YAML frontmatter

Familiarity with CONTRIBUTING.md and SECURITY.md guidelines

Limitations

Manual review required — no automated validation of prompt quality, safety, or effectiveness before merge

No contributor reputation system — all contributors are treated equally regardless of past contributions

Limited guidance on prompt quality — CONTRIBUTING.md specifies format but not content quality standards

What makes it unique

vs alternatives

prompt-file-enumeration-and-lookup

Medium confidence

Solves for

Best for

developers building tools that integrate with the prompt repository

researchers programmatically analyzing prompt collections

automation scripts that need to fetch specific prompts by ID

Requires

Python 3.6+ with file system access

Knowledge of GPT ID format or OpenAI GPT URL structure

idxtool.py script available in repository

Limitations

Requires exact ID or URL match — no fuzzy matching or partial search capability

File-system based lookup — scales linearly with repository size, no indexing for O(1) lookup

No caching — repeated lookups require re-scanning the file system

What makes it unique

vs alternatives

More flexible than static file paths because it supports multiple lookup methods (ID and URL), but slower than database-backed lookup and requires file system access rather than working over HTTP.

official-ai-product-prompt-documentation

Medium confidence

Solves for

Best for

prompt engineers studying commercial AI product design

teams building AI products who want to learn from vendor approaches

researchers analyzing how different vendors implement instruction-following

Requires

GitHub repository access to /prompts/official-product/ directory

Understanding of different vendor product lines and versions

Ability to interpret system prompts and configuration files

Limitations

Prompts may be outdated — vendors update system prompts frequently, and repository updates are manual

Incomplete product coverage — not all vendor products or versions are documented

No official endorsement — prompts are reverse-engineered or leaked, not officially published by vendors

What makes it unique

vs alternatives

open-source-ai-project-prompt-collection

Medium confidence

Solves for

Best for

open-source AI developers learning from peer implementations

researchers studying prompt engineering patterns in production systems

teams building open-source AI projects who want to share their approaches

Requires

GitHub repository access to /prompts/opensource-prj/ directory

Understanding of open-source AI project landscape

Ability to read and interpret system prompts

Limitations

Limited coverage — only 20+ open-source projects documented compared to 1,100+ custom GPTs

Inconsistent documentation quality — open-source projects may have varying levels of prompt documentation

No maintenance guarantees — open-source projects may become inactive, making their prompts outdated

What makes it unique

vs alternatives

More accessible than reading source code directly because prompts are extracted and documented, but less comprehensive than full project documentation and may not reflect the latest project versions.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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chatgpt_system_prompt

Capabilities11 decomposed

automated-toc-generation-for-prompt-collections

prompt-metadata-parsing-and-standardization

custom-gpt-and-development-ide-assistant-patterns

multi-source-prompt-aggregation-and-curation

prompt-injection-and-jailbreak-technique-documentation

custom-gpt-discovery-and-browsing

system-prompt-comparison-across-vendors

contribution-workflow-and-validation-guidelines

prompt-file-enumeration-and-lookup

official-ai-product-prompt-documentation

open-source-ai-project-prompt-collection

Related Artifactssharing capabilities

GPT Prompt Tuner

ChatGPT prompt engineering for developers

ChatGPT prompt engineering for developers

Prompt Engineering for ChatGPT - Vanderbilt University

TypingMind

StylerGPT

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to chatgpt_system_prompt

Are you the builder of chatgpt_system_prompt?

Get the weekly brief

Data Sources

chatgpt_system_prompt

Capabilities11 decomposed

automated-toc-generation-for-prompt-collections

prompt-metadata-parsing-and-standardization

custom-gpt-and-development-ide-assistant-patterns

multi-source-prompt-aggregation-and-curation

prompt-injection-and-jailbreak-technique-documentation

custom-gpt-discovery-and-browsing

system-prompt-comparison-across-vendors

contribution-workflow-and-validation-guidelines

prompt-file-enumeration-and-lookup

official-ai-product-prompt-documentation

open-source-ai-project-prompt-collection

Related Artifactssharing capabilities

GPT Prompt Tuner

ChatGPT prompt engineering for developers

ChatGPT prompt engineering for developers

Prompt Engineering for ChatGPT - Vanderbilt University

TypingMind

StylerGPT

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to chatgpt_system_prompt

Are you the builder of chatgpt_system_prompt?

Get the weekly brief

Data Sources