{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-asgeirtj--system_prompts_leaks","slug":"asgeirtj--system_prompts_leaks","name":"system_prompts_leaks","type":"repo","url":"https://github.com/asgeirtj/system_prompts_leaks","page_url":"https://unfragile.ai/asgeirtj--system_prompts_leaks","categories":["prompt-engineering"],"tags":["ai","ai-transparency","anthropic","chatgpt","claude","claude-code","gemini","generative-ai","gpt-5","grok","large-language-models","llm","openai","perplexity","prompt-engineering","system-prompt","system-prompts","xai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-asgeirtj--system_prompts_leaks__cap_0","uri":"capability://memory.knowledge.multi.provider.system.prompt.extraction.and.archival","name":"multi-provider system prompt extraction and archival","description":"Maintains a comprehensive, version-controlled repository of system prompts extracted from 8+ major AI providers (OpenAI, Anthropic, Google, xAI, Perplexity, Mistral, Microsoft, Notion) across 30+ model variants. Uses a hierarchical directory structure organized by provider and model version, with both raw prompt documents and human-readable markdown variants. Implements automated collection workflows to detect and capture prompt updates across provider releases, enabling longitudinal analysis of how system instructions evolve across model generations.","intents":["Understand how different AI providers architect their system prompts and behavioral guidelines","Compare safety constraints, tool integration patterns, and memory systems across competing models","Track changes in system prompt design across model versions to identify architectural shifts","Build prompt injection tests and security research against known system prompt structures","Reverse-engineer tool calling conventions and API integration patterns from extracted prompts"],"best_for":["AI security researchers studying prompt injection vulnerabilities","LLM engineers building competing models or fine-tuning approaches","Prompt engineers optimizing interactions with multiple AI providers","Transparency advocates analyzing AI provider design decisions","Teams building multi-model orchestration layers"],"limitations":["Prompts are static snapshots — may lag behind live model behavior by weeks or months","No guarantee of completeness — some providers actively hide or obfuscate system prompts","Extracted prompts may be incomplete or partially redacted by providers","Does not capture runtime behavior divergence from documented system prompts","No versioning metadata for when each prompt was extracted or which model version it applies to"],"requires":["Git client to clone repository","Text editor or markdown viewer to read prompt documents","No API keys or authentication required — all content is static documentation"],"input_types":["none — this is a reference archive, not an interactive tool"],"output_types":["markdown documents","raw text system prompts","structured prompt documentation with annotations"],"categories":["memory-knowledge","ai-transparency"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_1","uri":"capability://tool.use.integration.tool.integration.pattern.documentation.and.comparison","name":"tool integration pattern documentation and comparison","description":"Extracts and documents how different AI providers implement tool calling, function invocation, and API integration within their system prompts. Captures provider-specific patterns including OpenAI's channel-based tool namespace organization, Anthropic's MCP (Model Context Protocol) integration with browser automation and external services, Google's Gemini API search/browse tool architecture, and xAI's API policy layers. Enables analysis of how tool schemas, error handling, and capability constraints are communicated to models through system-level instructions.","intents":["Understand how each provider structures tool calling to prevent model hallucination of unavailable functions","Compare tool integration approaches to design multi-provider orchestration layers","Identify which providers support which external integrations (Slack, Gmail, Google Workspace, etc.)","Build tool calling compatibility layers that work across multiple model providers","Reverse-engineer tool schema requirements and validation logic from system prompts"],"best_for":["Teams building multi-model agent frameworks","Developers creating tool-calling abstraction layers","Researchers studying how LLMs learn to use external APIs","Security teams analyzing tool calling attack surfaces"],"limitations":["System prompts document intended behavior, not actual runtime tool calling implementation","Tool integration patterns may differ between API and web interface versions of same model","No information about tool calling latency, retry logic, or failure handling at runtime","Extracted prompts may omit sensitive tool credentials or authentication mechanisms"],"requires":["Understanding of function calling APIs (OpenAI, Anthropic, Google formats)","Familiarity with MCP protocol (for Claude integration analysis)"],"input_types":["none — reference documentation only"],"output_types":["markdown documentation of tool integration patterns","extracted tool schema definitions","comparative analysis of provider approaches"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_10","uri":"capability://tool.use.integration.specialized.deployment.and.workspace.architecture.documentation","name":"specialized deployment and workspace architecture documentation","description":"Extracts and documents system prompts for specialized AI deployments including workspace integrations, API variants, and specialized tools. Captures Claude Desktop Code CLI architecture, Gemini Workspace and AI Studio deployments, Grok Team Collaboration mode, and how providers adapt system prompts for different deployment contexts. Documents how system-level instructions vary between web interface, API, and specialized workspace deployments.","intents":["Understand how providers adapt system prompts for different deployment contexts","Design workspace-aware AI systems that leverage provider-specific deployments","Build deployment-specific prompt engineering strategies","Analyze how providers handle team collaboration and shared context","Compare system prompt variations across deployment types"],"best_for":["Teams building workspace-integrated AI tools","Developers creating deployment-specific prompt strategies","Researchers studying how deployment context affects model behavior","Product managers designing multi-deployment AI products"],"limitations":["Specialized deployment prompts may be less documented or harder to extract","Deployment-specific behavior may not be fully captured in system prompts","Access to specialized deployments may require specific account types or permissions","System prompts may differ between public and private workspace deployments"],"requires":["Access to specialized deployment environments","Understanding of workspace and team collaboration patterns"],"input_types":["none — reference documentation only"],"output_types":["markdown documentation of deployment variants","extracted deployment-specific prompts","comparative analysis of deployment approaches"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_2","uri":"capability://memory.knowledge.memory.and.context.management.architecture.analysis","name":"memory and context management architecture analysis","description":"Documents how different AI providers implement conversation memory, user preference persistence, and context window management through system-level instructions. Captures Claude's past conversation and memory system with search/fetch capabilities, GPT-5.4's memory and bio systems with user update cadence, Gemini's workspace-level context persistence, and Grok's team collaboration memory architecture. Enables understanding of how models are instructed to retrieve, prioritize, and forget information across conversation turns.","intents":["Design conversation memory systems that match provider capabilities","Understand how providers handle context window overflow and memory prioritization","Build user preference systems that integrate with multiple AI providers","Analyze how providers implement copyright compliance in memory retrieval","Compare stateful vs stateless conversation architectures across providers"],"best_for":["Teams building multi-turn conversation systems","Developers implementing user preference persistence","Researchers studying how LLMs manage long-term context","Product managers designing conversation UX across multiple models"],"limitations":["System prompts describe memory architecture but not actual storage implementation or retrieval latency","No information about memory persistence across sessions or data retention policies","Extracted prompts may omit details about memory encryption or privacy controls","Memory behavior may differ between web interface and API deployments"],"requires":["Understanding of conversation state management patterns","Familiarity with vector embeddings for semantic memory retrieval"],"input_types":["none — reference documentation only"],"output_types":["markdown documentation of memory architectures","extracted memory system specifications","comparative analysis of context management strategies"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_3","uri":"capability://safety.moderation.safety.constraint.and.alignment.framework.extraction","name":"safety constraint and alignment framework extraction","description":"Extracts and documents safety guardrails, content filtering policies, and alignment constraints embedded in system prompts across providers. Captures Claude's security architecture and prompt injection defense mechanisms, GPT-5.4's safety constraints and personality-based behavior modulation, Gemini's chain-of-thought protection and security policies, and Grok's policy layer architecture. Enables analysis of how providers encode safety rules, handle adversarial inputs, and balance capability with constraint.","intents":["Understand how different providers implement content filtering and safety constraints","Identify gaps or inconsistencies in safety implementations across models","Design red-teaming tests based on documented safety boundaries","Build safety-aware prompt engineering strategies","Compare alignment approaches across competing AI providers"],"best_for":["AI safety researchers studying alignment mechanisms","Red-team operators testing model robustness","Compliance teams evaluating provider safety claims","Developers building safety-critical applications"],"limitations":["System prompts document intended safety behavior, not actual runtime enforcement","Safety constraints may be bypassed through prompt injection or jailbreaking techniques","Extracted prompts may omit sensitive safety mechanisms or detection thresholds","Safety behavior may differ between web interface and API deployments","No information about safety constraint enforcement latency or false positive rates"],"requires":["Understanding of adversarial prompt techniques","Familiarity with content policy frameworks"],"input_types":["none — reference documentation only"],"output_types":["markdown documentation of safety constraints","extracted safety policy specifications","comparative analysis of alignment approaches"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_4","uri":"capability://text.generation.language.personality.and.behavioral.framework.documentation","name":"personality and behavioral framework documentation","description":"Extracts and documents how AI providers implement personality systems, behavioral variation, and tone modulation through system prompts. Captures GPT-5's personality framework with Listener (warm, reflective), Nerdy (playful, scientific), and Cynic (sarcastic with hidden warmth) variants, Grok's persona and companion system, and how personality constraints affect artifact handling and response style. Enables understanding of how models are instructed to vary behavior based on user context or explicit personality selection.","intents":["Design personality systems for multi-variant AI products","Understand how personality affects model behavior and output quality","Build personality-aware prompt engineering strategies","Compare personality implementation approaches across providers","Analyze how personality constraints interact with safety and capability systems"],"best_for":["Product teams designing conversational AI with personality","Prompt engineers optimizing for specific behavioral styles","Researchers studying how personality affects model outputs","Teams building multi-personality model variants"],"limitations":["System prompts document personality instructions but not actual behavioral consistency","Personality may not be consistently applied across all response types","No metrics for personality adherence or user perception of personality","Personality constraints may conflict with safety or capability requirements"],"requires":["Understanding of behavioral psychology and personality frameworks","Familiarity with prompt engineering for style control"],"input_types":["none — reference documentation only"],"output_types":["markdown documentation of personality frameworks","extracted personality specifications","comparative analysis of personality approaches"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_5","uri":"capability://code.generation.editing.artifact.generation.and.code.output.architecture.analysis","name":"artifact generation and code output architecture analysis","description":"Extracts and documents how AI providers implement artifact generation, code block handling, and structured output formatting through system prompts. Captures how Claude handles artifacts with Anthropic API integration, how GPT-5.4 manages artifact generation and skills integration, and how different providers constrain code output formatting. Documents system-level instructions for when to generate artifacts, how to structure them, and how to handle multi-file or complex code generation.","intents":["Understand how providers decide when to generate artifacts vs inline responses","Design artifact systems for multi-provider code generation tools","Build code output formatting that matches provider conventions","Analyze how providers handle large code generation and file management","Compare artifact handling across different model versions"],"best_for":["Teams building code generation tools with artifact support","Developers creating multi-provider code output formatters","Researchers studying how LLMs structure code generation","Product managers designing code-centric AI interfaces"],"limitations":["System prompts document artifact generation rules but not actual output consistency","Artifact formatting may vary based on context or user preferences","No information about artifact generation latency or token efficiency","Artifact handling may differ between web interface and API deployments"],"requires":["Understanding of code formatting and syntax highlighting","Familiarity with artifact storage and retrieval systems"],"input_types":["none — reference documentation only"],"output_types":["markdown documentation of artifact systems","extracted artifact generation specifications","comparative analysis of code output approaches"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_6","uri":"capability://tool.use.integration.external.integration.and.api.orchestration.pattern.documentation","name":"external integration and api orchestration pattern documentation","description":"Extracts and documents how AI providers integrate with external services and APIs through system prompts. Captures Claude's integrations with Slack, Gmail, and Google Workspace, Gemini's search and browse tool architecture, Perplexity's browser and voice assistant integrations, and how providers handle API authentication, error handling, and capability constraints. Documents system-level instructions for API orchestration, rate limiting awareness, and multi-service coordination.","intents":["Understand which external services each provider integrates with natively","Design multi-provider orchestration layers that leverage provider-specific integrations","Build API error handling strategies based on provider constraints","Analyze how providers handle authentication and credential management","Compare external integration capabilities across competing models"],"best_for":["Teams building multi-provider agent frameworks","Developers creating API orchestration layers","Researchers studying how LLMs coordinate multiple external services","Product managers evaluating provider integration capabilities"],"limitations":["System prompts document intended integrations but not actual API reliability","Integration availability may vary by region, account tier, or deployment method","No information about integration latency, rate limits, or failure handling","Extracted prompts may omit sensitive integration details or authentication mechanisms"],"requires":["Understanding of OAuth and API authentication patterns","Familiarity with multi-service orchestration"],"input_types":["none — reference documentation only"],"output_types":["markdown documentation of integrations","extracted integration specifications","comparative analysis of provider capabilities"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_7","uri":"capability://memory.knowledge.model.version.evolution.and.capability.tracking","name":"model version evolution and capability tracking","description":"Maintains version-controlled documentation of system prompts across model generations and variants, enabling longitudinal analysis of how AI providers evolve their architectures. Tracks changes from GPT-4 through GPT-5.4, Claude Sonnet through Claude Opus 4.6, Gemini 2.5 through 3.1 Pro, and Grok 3 through 4.2. Documents how capabilities are added, deprecated, or modified across versions, and how system-level instructions change to support new features or address discovered issues.","intents":["Track how AI providers evolve their system prompt architectures across versions","Understand which capabilities are stable vs experimental across model generations","Plan migration strategies when upgrading between model versions","Analyze how providers address safety or capability issues through prompt updates","Predict future capability directions based on version evolution patterns"],"best_for":["Teams managing multi-version model deployments","Researchers studying LLM evolution and capability emergence","Product managers planning feature rollouts across model versions","Developers building version-aware prompt engineering strategies"],"limitations":["Version tracking depends on community contributions and may lag behind actual releases","No information about why specific changes were made or their impact on behavior","Version boundaries may not align with actual capability changes","Extracted prompts may be incomplete or partially redacted for newer versions"],"requires":["Git knowledge to track version history","Understanding of semantic versioning and model release cycles"],"input_types":["none — reference documentation only"],"output_types":["version-controlled markdown documents","git diff output showing prompt changes","version comparison analysis"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_8","uri":"capability://memory.knowledge.cross.provider.architectural.pattern.analysis.and.comparison","name":"cross-provider architectural pattern analysis and comparison","description":"Enables comparative analysis of architectural patterns across different AI providers by aggregating system prompts in a structured format. Supports identification of common patterns (tool integration, memory systems, safety constraints, personality frameworks) and provider-specific innovations. Facilitates analysis of how different providers solve similar problems (e.g., context window management, tool calling, artifact generation) using different architectural approaches.","intents":["Identify common architectural patterns across AI providers","Understand provider-specific innovations and differentiation strategies","Design multi-provider abstraction layers that handle architectural differences","Analyze how different providers balance capability, safety, and performance","Build research insights about LLM architecture design tradeoffs"],"best_for":["Researchers studying LLM architecture design patterns","Teams building multi-provider abstraction frameworks","Architects designing next-generation AI systems","Analysts evaluating competitive positioning of AI providers"],"limitations":["Comparison is limited to documented system prompts, not actual runtime behavior","Architectural differences may not be visible in system prompts alone","No information about performance tradeoffs or implementation complexity","Patterns may be outdated as providers iterate on their systems"],"requires":["Deep understanding of LLM architecture and system design","Familiarity with multiple AI provider APIs and capabilities"],"input_types":["none — reference documentation only"],"output_types":["comparative analysis documents","architectural pattern summaries","design tradeoff analysis"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-asgeirtj--system_prompts_leaks__cap_9","uri":"capability://data.processing.analysis.raw.and.human.readable.prompt.variant.documentation","name":"raw and human-readable prompt variant documentation","description":"Maintains both raw extracted system prompts and human-readable markdown variants with annotations, enabling different use cases for prompt analysis. Raw variants preserve exact formatting and structure as extracted from providers, while markdown variants add section headers, formatting, and explanatory notes for easier reading and analysis. Supports both programmatic analysis of raw prompts and human-readable study of architectural decisions.","intents":["Access raw system prompts for programmatic analysis and parsing","Read annotated markdown versions for understanding architectural decisions","Build prompt parsing tools that work with raw extracted formats","Create educational materials explaining how system prompts work","Compare raw vs annotated versions to understand provider obfuscation techniques"],"best_for":["Researchers building prompt analysis tools","Educators teaching LLM architecture and system design","Developers building prompt parsing and analysis pipelines","Teams creating documentation about AI provider architectures"],"limitations":["Raw prompts may contain formatting artifacts or encoding issues","Markdown annotations are subjective and may introduce interpretation bias","No automated validation that raw and markdown versions are consistent","Variant maintenance requires manual effort and may lag behind updates"],"requires":["Text processing tools for raw prompt analysis","Markdown viewer for annotated versions"],"input_types":["none — reference documentation only"],"output_types":["raw text system prompts","markdown-formatted annotated prompts","structured prompt metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Git client to clone repository","Text editor or markdown viewer to read prompt documents","No API keys or authentication required — all content is static documentation","Understanding of function calling APIs (OpenAI, Anthropic, Google formats)","Familiarity with MCP protocol (for Claude integration analysis)","Access to specialized deployment environments","Understanding of workspace and team collaboration patterns","Understanding of conversation state management patterns","Familiarity with vector embeddings for semantic memory retrieval","Understanding of adversarial prompt techniques"],"failure_modes":["Prompts are static snapshots — may lag behind live model behavior by weeks or months","No guarantee of completeness — some providers actively hide or obfuscate system prompts","Extracted prompts may be incomplete or partially redacted by providers","Does not capture runtime behavior divergence from documented system prompts","No versioning metadata for when each prompt was extracted or which model version it applies to","System prompts document intended behavior, not actual runtime tool calling implementation","Tool integration patterns may differ between API and web interface versions of same model","No information about tool calling latency, retry logic, or failure handling at runtime","Extracted prompts may omit sensitive tool credentials or authentication mechanisms","Specialized deployment prompts may be less documented or harder to extract","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8187568966194729,"quality":0.47,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:21.997Z","last_commit":"2026-05-01T17:46:38Z"},"community":{"stars":39410,"forks":6504,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=asgeirtj--system_prompts_leaks","compare_url":"https://unfragile.ai/compare?artifact=asgeirtj--system_prompts_leaks"}},"signature":"xVFlSmzf7kJaKmc7tY1jrjL9DBze4b2DnW5MkCWhzQQPJ8Z36mQAYBO1JivOD2tDuAyVWqOkYytsnB5y9hZwDA==","signedAt":"2026-06-20T07:10:21.508Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/asgeirtj--system_prompts_leaks","artifact":"https://unfragile.ai/asgeirtj--system_prompts_leaks","verify":"https://unfragile.ai/api/v1/verify?slug=asgeirtj--system_prompts_leaks","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}