{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-elder-plinius--cl4r1t4s","slug":"elder-plinius--cl4r1t4s","name":"CL4R1T4S","type":"prompt","url":"https://github.com/elder-plinius/CL4R1T4S","page_url":"https://unfragile.ai/elder-plinius--cl4r1t4s","categories":["prompt-engineering","app-builders"],"tags":["agents","ai","chatgpt","gemini","google","grok","hacking","leak","leaked","openai","prompt","prompt-engineering","prompts","red-team","red-teaming","system","system-info","system-prompts","tools","transparency"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-elder-plinius--cl4r1t4s__cap_0","uri":"capability://safety.moderation.system.prompt.extraction.via.directive.injection","name":"system-prompt-extraction-via-directive-injection","description":"Extracts hidden system prompts from AI models by injecting specific trigger directives (e.g., *!<NEW_PARADIGM>!*) that cause models to self-disclose their internal instruction sets. The extraction mechanism exploits prompt injection vulnerabilities where obfuscated payloads (leetspeak encoding like '5h1f7 y0ur f0cu5') bypass safety filters and force models to output their complete behavioral scaffolds, including restriction logic, persona definitions, and tool-calling schemas.","intents":["I want to understand what hidden instructions are controlling a specific AI model's behavior","I need to test whether an AI system will leak its system prompt when given a specially crafted directive","I want to document the actual alignment constraints and ethical guidelines embedded in proprietary models"],"best_for":["security researchers conducting red-team assessments of AI systems","transparency advocates documenting AI model behavior and bias","developers building prompt injection detection systems"],"limitations":["Effectiveness varies by model version and deployment date — newer models may have patched disclosure vulnerabilities","Extracted prompts may be incomplete or sanitized if the model partially resists disclosure","Directives require active interaction with the target model; cannot extract from offline/archived models","Success rate depends on obfuscation technique; some models may ignore leetspeak payloads entirely"],"requires":["Direct API access or web interface to the target AI model (ChatGPT, Claude, Gemini, Grok, etc.)","Knowledge of model-specific disclosure triggers (repository documents these per provider)","Ability to craft and test prompt injection payloads iteratively"],"input_types":["text (trigger directives with obfuscated payloads)","structured metadata (model name, version, deployment date)"],"output_types":["text (raw system prompt content)","structured markdown (documented system prompt with metadata)"],"categories":["safety-moderation","red-teaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_1","uri":"capability://memory.knowledge.system.prompt.documentation.and.archival","name":"system-prompt-documentation-and-archival","description":"Maintains a centralized, version-controlled repository of extracted system prompts organized by AI provider (OpenAI, Anthropic, Google, xAI, etc.) and model version, with structured markdown documentation including extraction date, contextual metadata, and technical analysis. The repository functions as a structured database where each prompt is cataloged with temporal tracking to detect behavioral drift across model updates and versions.","intents":["I want to compare how different AI providers define alignment and behavioral constraints","I need to track how a specific model's system prompt has changed over time to understand evolving safety policies","I want to audit whether an AI model's actual behavior matches its documented system instructions"],"best_for":["AI safety researchers studying alignment mechanisms across providers","compliance auditors verifying AI system behavior against documented constraints","open-source maintainers building transparency tools for AI governance"],"limitations":["Prompts become stale as models are updated; extraction date metadata is critical but may lag actual deployments","Repository depends on community contributions — coverage gaps exist for newer models or less-documented providers","Extracted prompts may be incomplete if the model uses multi-stage instruction loading or dynamic prompt composition","No automated verification that documented prompts match current model behavior; manual testing required"],"requires":["Git repository access (GitHub) to clone and browse prompt files","Markdown reader or text editor to parse documented prompts","Understanding of AI system prompt structure and terminology"],"input_types":["extracted system prompts (text)","metadata (extraction date, model version, provider name, extraction method)"],"output_types":["markdown files (structured prompt documentation)","version history (Git commits tracking prompt changes over time)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_10","uri":"capability://safety.moderation.ai.system.alignment.framework.analysis","name":"ai-system-alignment-framework-analysis","description":"Analyzes and categorizes how different AI labs implement alignment through system prompts, organizing findings into four technical domains: Restriction Logic (hard-coded refusals and topic bans), Persona Scaffolding (forced identities and roles), Deception/Redirection (instructions to pivot away from sensitive queries), and Ideological Framing (embedded ethical or political biases). This enables researchers to understand the mechanisms through which alignment is implemented and compare approaches across providers.","intents":["I want to understand how an AI lab implements restriction logic and content filtering through system prompts","I need to analyze whether different providers use different ideological framing in their system prompts","I want to study how AI systems are instructed to handle sensitive topics and redirect queries"],"best_for":["AI alignment researchers studying implementation mechanisms","policy analysts evaluating AI governance approaches","bias researchers analyzing embedded values in AI systems"],"limitations":["Categorization into four domains is a simplification; actual alignment mechanisms may be more complex","Extracted prompts may not reveal all alignment mechanisms if some are implemented in model architecture or training","Interpretation of 'Ideological Framing' is subjective and may reflect researcher bias","No automated tools provided for categorization; analysis requires manual review"],"requires":["Extracted system prompts from multiple providers","Domain expertise in AI alignment and safety","Understanding of the four alignment framework categories"],"input_types":["system prompts (text/markdown)","provider metadata"],"output_types":["alignment analysis (text/markdown)","categorized findings (structured data mapping prompts to framework categories)","comparative reports (how different providers implement each category)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_2","uri":"capability://data.processing.analysis.multi.provider.system.prompt.comparison.and.analysis","name":"multi-provider-system-prompt-comparison-and-analysis","description":"Enables systematic comparison of system prompts across 10+ AI providers (OpenAI, Anthropic, Google, xAI, Cognition, Replit, etc.) to identify patterns in restriction logic, persona scaffolding, deception/redirection strategies, and ideological framing. The repository's organizational structure groups prompts by provider and model, allowing researchers to analyze how different labs implement alignment constraints, ethical guidelines, and behavioral boundaries.","intents":["I want to understand how OpenAI's alignment approach differs from Anthropic's or Google's in concrete terms","I need to identify common patterns in how AI labs implement content restrictions and safety guardrails","I want to audit whether different providers apply different ethical standards to the same types of queries"],"best_for":["AI governance researchers studying alignment diversity across the industry","policy makers evaluating regulatory approaches to AI safety","journalists investigating bias and inconsistency in AI system design"],"limitations":["Comparison validity depends on prompt extraction completeness — missing or partial prompts skew analysis","Prompts may be intentionally obfuscated or use provider-specific instruction formats that resist direct comparison","Behavioral differences may stem from model architecture, training data, or fine-tuning rather than system prompts alone","No automated analysis tools provided; comparison requires manual reading and synthesis"],"requires":["Access to the CL4R1T4S repository with prompts from multiple providers","Domain expertise in AI alignment, safety, and prompt engineering to interpret differences","Ability to read and analyze unstructured or semi-structured prompt text"],"input_types":["system prompts from multiple providers (text/markdown)","provider metadata (company name, model name, version)"],"output_types":["comparative analysis (text/markdown)","structured findings (restriction patterns, persona types, ethical frameworks)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_3","uri":"capability://safety.moderation.prompt.injection.vulnerability.testing.and.documentation","name":"prompt-injection-vulnerability-testing-and-documentation","description":"Documents and catalogs prompt injection techniques that successfully trigger system prompt disclosure across different AI models, including obfuscation strategies (leetspeak encoding, special character sequences), timing-based attacks, and context manipulation. The repository serves as a reference for security researchers to understand which injection patterns work against specific models and versions, enabling systematic red-teaming of AI systems.","intents":["I want to test whether a specific AI model is vulnerable to prompt injection attacks that leak system prompts","I need to understand which obfuscation techniques (leetspeak, special characters, etc.) are most effective against different models","I want to document and share successful injection payloads to help the security community improve AI defenses"],"best_for":["security researchers conducting vulnerability assessments of AI systems","AI safety engineers building prompt injection detection and mitigation systems","red-team operators testing AI model robustness"],"limitations":["Documented injection techniques may become ineffective as models are patched or updated","Effectiveness varies significantly by model version, deployment environment, and fine-tuning","Some injection payloads may be model-specific and not generalize across providers","No automated testing framework provided; researchers must manually craft and test payloads"],"requires":["Direct API access or web interface to target AI models","Understanding of prompt injection mechanics and obfuscation techniques","Ability to iterate on payload design based on model responses"],"input_types":["prompt injection payloads (text with obfuscation)","model metadata (name, version, provider)"],"output_types":["vulnerability reports (text/markdown)","successful injection payloads (text)","effectiveness metrics (success rate, extraction completeness)"],"categories":["safety-moderation","red-teaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_4","uri":"capability://safety.moderation.ai.model.behavioral.alignment.auditing","name":"ai-model-behavioral-alignment-auditing","description":"Enables auditing of AI model behavior against documented system prompts by comparing extracted instructions with observed model outputs. Researchers can verify whether a model's actual responses align with its stated restrictions, personas, and ethical guidelines, or identify cases where models deviate from, contradict, or selectively ignore their system prompts. This capability supports compliance verification and bias detection.","intents":["I want to verify that a model's actual behavior matches its documented system prompt constraints","I need to identify cases where a model violates or ignores its stated restrictions","I want to audit whether a model applies its ethical guidelines consistently across different query types"],"best_for":["compliance auditors verifying AI system behavior against documented policies","AI safety researchers studying the gap between stated and actual model behavior","organizations deploying AI systems that need to verify alignment with internal policies"],"limitations":["Requires both extracted system prompts and extensive behavioral testing data — labor-intensive to conduct at scale","Model behavior may vary based on context, user history, or deployment environment, making direct comparison difficult","Extracted prompts may be incomplete or sanitized, limiting audit accuracy","No automated framework provided for systematic behavioral testing and comparison"],"requires":["Extracted system prompts from CL4R1T4S repository","Behavioral test cases and expected outputs for the target model","API access to the model for testing","Expertise in AI safety and behavioral analysis"],"input_types":["system prompts (text/markdown)","test queries (text)","model responses (text)"],"output_types":["audit reports (text/markdown)","compliance findings (structured data)","deviation analysis (cases where behavior contradicts system prompt)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_5","uri":"capability://memory.knowledge.ai.transparency.and.interpretability.research.support","name":"ai-transparency-and-interpretability-research-support","description":"Serves as a primary data source for AI transparency research by exposing the 'hidden instructions' that define model behavior, personas, and constraints. The repository enables researchers to study how AI labs implement alignment, what ethical frameworks are embedded in models, and how system prompts shape outputs. This supports interpretability research, bias detection, and understanding of AI system design decisions.","intents":["I want to understand what ethical guidelines and biases are embedded in a specific AI model's system prompt","I need to study how different AI labs approach alignment and safety to inform my research","I want to analyze the relationship between system prompt content and observed model behavior"],"best_for":["academic researchers studying AI alignment and interpretability","transparency advocates documenting AI system design and bias","policy researchers analyzing AI governance and safety approaches"],"limitations":["Extracted prompts may not represent the complete instruction set if models use dynamic or multi-stage prompt loading","Prompts are snapshots in time and may not reflect current model behavior","Interpretation of prompts requires domain expertise; raw prompts alone do not provide complete understanding of model behavior","No analysis tools provided; researchers must manually synthesize findings"],"requires":["Access to CL4R1T4S repository","Domain expertise in AI safety, alignment, and prompt engineering","Ability to analyze and interpret unstructured prompt text"],"input_types":["system prompts (text/markdown)","provider metadata (company, model, version)"],"output_types":["research papers (text)","analysis reports (text/markdown)","datasets (structured prompt data for quantitative analysis)"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_6","uri":"capability://automation.workflow.community.contributed.prompt.extraction.and.validation","name":"community-contributed-prompt-extraction-and-validation","description":"Implements an open-source contribution model where security researchers and developers can submit newly extracted system prompts with structured metadata (model name, version, extraction date, extraction method, contextual logs). The repository includes submission guidelines and validation requirements to ensure extracted prompts are technically accurate and reproducible. Contributors provide evidence of successful extraction and document the techniques used.","intents":["I want to contribute a newly extracted system prompt from a model not yet in the repository","I need to document the extraction method and metadata so other researchers can verify and reproduce my findings","I want to help maintain the repository by validating and updating prompts as models are released"],"best_for":["security researchers discovering new prompt injection vulnerabilities","open-source contributors maintaining transparency databases","developers building tools that depend on current system prompt data"],"limitations":["Contribution quality depends on submitter expertise — incomplete or inaccurate prompts may be accepted without sufficient validation","No automated verification that submitted prompts are authentic or complete","Maintenance burden grows with repository size; older prompts may become stale without active updates","Pull request review process may be slow, delaying availability of newly extracted prompts"],"requires":["GitHub account and familiarity with Git/Pull Request workflow","Extracted system prompt content (text)","Metadata: model name, version, extraction date, extraction method, contextual logs","Evidence of successful extraction (screenshots, logs, or reproducible steps)"],"input_types":["system prompt text (markdown or plain text)","structured metadata (JSON or markdown frontmatter)","extraction evidence (screenshots, logs, reproducible steps)"],"output_types":["pull request (GitHub)","merged prompt documentation (markdown file in repository)","version history (Git commit)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_7","uri":"capability://data.processing.analysis.model.version.drift.tracking.and.temporal.analysis","name":"model-version-drift-tracking-and-temporal-analysis","description":"Tracks system prompt changes across model versions and deployment dates, enabling researchers to analyze how AI labs evolve their alignment strategies over time. By maintaining version-controlled prompts with extraction timestamps, the repository enables temporal analysis of behavioral drift, policy changes, and safety mechanism updates. Researchers can correlate prompt changes with model release dates and identify when and how alignment constraints were modified.","intents":["I want to understand how a model's system prompt has changed between version updates","I need to identify when and how an AI lab modified its alignment constraints or safety policies","I want to analyze whether prompt changes correlate with reported model behavior improvements or regressions"],"best_for":["AI safety researchers studying alignment evolution across model versions","policy analysts tracking how AI labs respond to safety concerns or regulatory pressure","developers monitoring whether model updates affect their applications' behavior"],"limitations":["Temporal analysis requires comprehensive coverage of multiple model versions — gaps in extraction history limit analysis","Extraction dates may not align exactly with model release dates, introducing temporal uncertainty","Prompt changes alone do not explain behavioral changes; model architecture and training data also evolve","No automated tools provided for temporal analysis; researchers must manually compare versions"],"requires":["Multiple extracted prompts from the same model across different versions","Accurate extraction dates for each prompt version","Git history access to view prompt changes over time","Model release date information for correlation analysis"],"input_types":["versioned system prompts (text/markdown)","extraction timestamps (ISO 8601 dates)","model version identifiers (e.g., GPT-4o, Claude 3.5)"],"output_types":["version comparison reports (text/markdown)","temporal analysis (changes over time)","correlation analysis (prompt changes vs. model release dates)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_8","uri":"capability://tool.use.integration.agentic.ai.system.instruction.documentation","name":"agentic-ai-system-instruction-documentation","description":"Documents system prompts and instruction sets for agentic AI systems (Cursor, Windsurf, Devin, Replit Agent, Cline, etc.) that operate with tool-calling capabilities, function schemas, and autonomous decision-making. The repository captures how these systems are instructed to use tools, manage state, handle errors, and make decisions — information critical for understanding agent behavior and potential failure modes. Includes documentation of tool-calling schemas (e.g., <x41:function_call>) and agent-specific constraints.","intents":["I want to understand what instructions control an agentic AI system's tool usage and decision-making","I need to audit whether a coding agent (Cursor, Devin) is following its documented constraints when using tools","I want to analyze how different agent systems are instructed to handle errors, refusals, and edge cases"],"best_for":["developers integrating agentic AI systems into production workflows","security researchers auditing agent behavior and tool access","AI safety researchers studying autonomous decision-making in agents"],"limitations":["Agent system prompts may be more complex and dynamic than standard chat models, making extraction and documentation harder","Tool-calling schemas and function definitions may be partially obfuscated or dynamically generated","Agent behavior depends on tool availability and environment state, not just system prompts","Extracted prompts may not capture the full agent instruction set if tools or state management are handled separately"],"requires":["Access to agentic AI systems (Cursor IDE, Devin API, Replit Agent, etc.)","Understanding of tool-calling schemas and agent architecture","Ability to extract and document function definitions and tool constraints"],"input_types":["agent system prompts (text/markdown)","tool-calling schemas (JSON or structured text)","function definitions (text)"],"output_types":["agent instruction documentation (markdown)","tool-calling schema documentation (JSON/structured text)","agent behavior analysis (text/markdown)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-elder-plinius--cl4r1t4s__cap_9","uri":"capability://safety.moderation.prompt.obfuscation.and.evasion.technique.catalog","name":"prompt-obfuscation-and-evasion-technique-catalog","description":"Catalogs obfuscation and evasion techniques used in prompt injection attacks, including leetspeak encoding, special character sequences, context manipulation, and other methods that bypass safety filters. The repository documents which techniques are effective against specific models and versions, serving as both a reference for security researchers and a resource for understanding how models can be manipulated to disclose hidden instructions.","intents":["I want to understand what obfuscation techniques are most effective for bypassing a model's safety filters","I need to design prompt injection payloads that work against a specific model version","I want to study how models respond to obfuscated directives to improve my defenses"],"best_for":["security researchers designing prompt injection detection systems","AI safety engineers building robustness improvements","red-team operators testing model vulnerabilities"],"limitations":["Documented obfuscation techniques may become ineffective as models are patched","Effectiveness varies significantly by model, version, and deployment environment","Obfuscation techniques may not generalize across different model architectures","No systematic evaluation of which techniques are most robust or likely to persist"],"requires":["Understanding of prompt injection mechanics","Access to target models for testing","Ability to iterate on obfuscation payloads"],"input_types":["obfuscation techniques (text descriptions)","example payloads (text with obfuscation)","model metadata (name, version)"],"output_types":["technique documentation (text/markdown)","effectiveness reports (success rates by model)","payload examples (text)"],"categories":["safety-moderation","red-teaming"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Direct API access or web interface to the target AI model (ChatGPT, Claude, Gemini, Grok, etc.)","Knowledge of model-specific disclosure triggers (repository documents these per provider)","Ability to craft and test prompt injection payloads iteratively","Git repository access (GitHub) to clone and browse prompt files","Markdown reader or text editor to parse documented prompts","Understanding of AI system prompt structure and terminology","Extracted system prompts from multiple providers","Domain expertise in AI alignment and safety","Understanding of the four alignment framework categories","Access to the CL4R1T4S repository with prompts from multiple providers"],"failure_modes":["Effectiveness varies by model version and deployment date — newer models may have patched disclosure vulnerabilities","Extracted prompts may be incomplete or sanitized if the model partially resists disclosure","Directives require active interaction with the target model; cannot extract from offline/archived models","Success rate depends on obfuscation technique; some models may ignore leetspeak payloads entirely","Prompts become stale as models are updated; extraction date metadata is critical but may lag actual deployments","Repository depends on community contributions — coverage gaps exist for newer models or less-documented providers","Extracted prompts may be incomplete if the model uses multi-stage instruction loading or dynamic prompt composition","No automated verification that documented prompts match current model behavior; manual testing required","Categorization into four domains is a simplification; actual alignment mechanisms may be more complex","Extracted prompts may not reveal all alignment mechanisms if some are implemented in model architecture or training","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39680027429735654,"quality":0.47,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:50.673Z","last_commit":"2026-04-17T19:56:21Z"},"community":{"stars":25900,"forks":4680,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=elder-plinius--cl4r1t4s","compare_url":"https://unfragile.ai/compare?artifact=elder-plinius--cl4r1t4s"}},"signature":"QmcIo+eHYKld7dk6esWCVu55GUosNDE8jS+LmG/Po3W7sbW2dpqpLHtY4rapFQUN4uEZH9uhbirMQGd7t+alDw==","signedAt":"2026-06-21T01:48:02.134Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/elder-plinius--cl4r1t4s","artifact":"https://unfragile.ai/elder-plinius--cl4r1t4s","verify":"https://unfragile.ai/api/v1/verify?slug=elder-plinius--cl4r1t4s","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"}}