{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-prompts-md","slug":"prompts-md","name":"PROMPTS.md","type":"dataset","url":"https://raw.githubusercontent.com/f/prompts.chat/main/PROMPTS.md","page_url":"https://unfragile.ai/prompts-md","categories":["prompt-engineering","model-training"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-prompts-md__cap_0","uri":"capability://memory.knowledge.markdown.based.prompt.template.library.with.contributor.attribution","name":"markdown-based prompt template library with contributor attribution","description":"Provides a curated collection of LLM prompts stored as static markdown with hierarchical structure (## headings for titles), inline code blocks for prompt text, and GitHub username attribution for each contribution. The dataset is distributed via raw GitHub file access and mirrored on Hugging Face, enabling both direct HTTP retrieval and programmatic access through the Hugging Face datasets library without requiring authentication or API keys.","intents":["I need a library of pre-written prompts to use with ChatGPT or Claude without writing them from scratch","I want to discover prompt engineering patterns and techniques used by other practitioners","I need to build a training dataset of prompt-response pairs for fine-tuning or evaluation","I want to understand how to structure prompts for specific domains like coding, interviews, or language tasks"],"best_for":["LLM practitioners and prompt engineers building personal or organizational prompt libraries","Developers training or fine-tuning language models who need diverse prompt examples","Non-technical users seeking copy-paste prompts for consumer LLM interfaces","Researchers studying prompt engineering patterns and effectiveness"],"limitations":["Static snapshot with no versioning or update tracking — cannot detect when prompts are added, modified, or deprecated","No structured metadata beyond contributor name — lacks creation date, quality metrics, success rates, or performance benchmarks","Incomplete documentation in public excerpt — full dataset scope unknown, content cuts off mid-prompt","No built-in search or filtering mechanism — users must manually parse markdown to find relevant prompts","No validation of prompt effectiveness — no quality assurance process visible for contributed prompts"],"requires":["HTTP client or curl for GitHub raw content access","Python 3.6+ with datasets library (pip install datasets) for Hugging Face integration","No authentication required for public access"],"input_types":["markdown text file","GitHub repository URL","Hugging Face dataset identifier"],"output_types":["plain text prompts","markdown-formatted prompt blocks","structured dataset rows (via Hugging Face)"],"categories":["memory-knowledge","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prompts-md__cap_1","uri":"capability://text.generation.language.template.variable.substitution.with.default.value.syntax","name":"template variable substitution with default value syntax","description":"Supports parameterized prompts using `${VariableName:DefaultValue}` syntax embedded in prompt text, allowing users to inject dynamic values (job titles, names, domains) before passing prompts to LLMs. This enables a single prompt template to be reused across multiple contexts without manual editing, though the syntax is ad-hoc and lacks formal specification or validation tooling.","intents":["I want to create a reusable prompt template that works for different job positions or interview scenarios","I need to parameterize prompts so they can be used in batch processing or automation workflows","I want to generate multiple prompt variants from a single template by substituting different values"],"best_for":["Developers building prompt management systems or LLM applications that need template reuse","Teams automating prompt generation for bulk use cases (batch interviews, content generation)","Prompt engineers creating libraries of reusable templates for organizational use"],"limitations":["No formal specification for template syntax — implementation details and parsing rules are not documented","No built-in validation or type checking — invalid or missing variables will be passed literally to the LLM","No escaping mechanism for literal curly braces — prompts containing `${...}` patterns unrelated to variables will be incorrectly parsed","Default values are optional but not enforced — no guarantee that all variables have sensible defaults","No tooling provided for template validation or testing — users must manually verify substitution works correctly"],"requires":["Custom regex or string parsing logic to extract and substitute variables","No built-in library or SDK provided — implementation is user's responsibility","Understanding of the `${VariableName:DefaultValue}` syntax (inferred from examples, not formally documented)"],"input_types":["markdown prompt text with embedded template variables"],"output_types":["substituted prompt text ready for LLM injection"],"categories":["text-generation-language","template-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prompts-md__cap_2","uri":"capability://text.generation.language.role.playing.and.behavioral.constraint.prompt.patterns","name":"role-playing and behavioral constraint prompt patterns","description":"Provides a collection of prompts that establish LLM behavior through role definition (e.g., 'act as a Linux terminal', 'act as a job interviewer') combined with explicit output format constraints ('only reply with terminal output', 'do not write explanations'). These prompts demonstrate techniques for constraining LLM responses through system-level instructions and behavioral guardrails, serving as reference implementations for prompt engineering patterns.","intents":["I want to understand how to structure prompts that make LLMs adopt specific roles or personas","I need examples of how to constrain LLM output format (code blocks, JSON, plain text only)","I want to learn prompt patterns for interactive simulations (terminal emulation, spreadsheet emulation, interview scenarios)","I need to see how to combine role definition with behavioral constraints for reliable LLM behavior"],"best_for":["Prompt engineers learning best practices for role-based and constraint-based prompting","Developers building LLM applications that require consistent output formats or behavioral patterns","Researchers studying prompt engineering techniques and their effectiveness","Teams training internal LLM applications on domain-specific behaviors"],"limitations":["No performance metrics or success rates provided — unclear which prompts work reliably across different LLM models","No model-specific variants — prompts may work differently on GPT-4, Claude, Llama, etc., but no guidance provided","No explanation of why certain constraints work — patterns are shown but not analyzed or justified","Limited scope of domains covered — only partial dataset visible, so coverage of different use cases is unknown","No testing or validation framework — users must manually test prompts against their target LLM"],"requires":["Access to an LLM API or interface (ChatGPT, Claude, Ollama, etc.)","Understanding of LLM prompt structure and behavior","Manual testing to validate prompt effectiveness for specific use cases"],"input_types":["markdown prompt text with role definitions and constraints"],"output_types":["LLM responses constrained by role and behavioral instructions"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prompts-md__cap_3","uri":"capability://code.generation.editing.domain.specific.prompt.collection.for.coding.and.technical.domains","name":"domain-specific prompt collection for coding and technical domains","description":"Includes specialized prompts for technical domains such as Ethereum/Solidity development, Linux terminal emulation, JavaScript execution simulation, and code-related tasks. These prompts demonstrate how to structure instructions for domain-specific LLM behavior, including handling of technical syntax, code output formatting, and domain-specific constraints that differ from general-purpose prompts.","intents":["I want prompts that make LLMs behave like a Linux terminal or JavaScript runtime for interactive coding","I need examples of how to prompt LLMs for blockchain development (Solidity, Ethereum) tasks","I want to understand how to structure prompts for code generation with specific output format requirements","I need domain-specific prompt patterns that handle technical syntax and code blocks correctly"],"best_for":["Developers building LLM-powered coding assistants or interactive development environments","Blockchain developers seeking prompt patterns for smart contract development","Teams building educational tools that use LLMs to simulate development environments","Prompt engineers specializing in technical and domain-specific applications"],"limitations":["No validation of technical accuracy — prompts may contain outdated or incorrect technical information","Limited to observed domains (Solidity, Linux, JavaScript) — coverage of other technical domains unknown","No guidance on model-specific behavior — different LLMs may handle technical syntax differently","No error handling or fallback patterns — prompts don't address how LLMs should handle invalid input or edge cases","No performance benchmarks — unclear how reliably these prompts work for actual technical tasks"],"requires":["LLM with strong technical knowledge in the target domain","Understanding of the technical domain (Solidity, Linux, JavaScript, etc.)","Manual validation that LLM outputs are technically correct"],"input_types":["markdown prompts with domain-specific instructions"],"output_types":["code snippets","terminal output simulation","technical explanations"],"categories":["code-generation-editing","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prompts-md__cap_4","uri":"capability://text.generation.language.interactive.simulation.prompts.for.terminal.spreadsheet.and.interview.scenarios","name":"interactive simulation prompts for terminal, spreadsheet, and interview scenarios","description":"Provides prompts designed to make LLMs simulate interactive environments (Linux terminal, spreadsheet application, job interview) by establishing role-based behavior combined with strict output format constraints and meta-instruction handling. These prompts use curly bracket syntax to embed English instructions within simulated environments, enabling multi-turn interactions where the LLM maintains context and responds as the simulated system rather than as a general assistant.","intents":["I want to use an LLM as an interactive Linux terminal for learning or testing shell commands","I need to simulate a spreadsheet application or other interactive tool using an LLM","I want to conduct mock job interviews using an LLM that maintains interviewer behavior across multiple turns","I need to understand how to structure prompts for stateful, multi-turn interactions with consistent role behavior"],"best_for":["Educators building interactive learning tools powered by LLMs","Developers creating LLM-based simulators for technical training or practice","Recruiters or hiring teams using LLMs for initial screening or interview practice","Prompt engineers designing complex, multi-turn interaction patterns"],"limitations":["Requires careful prompt engineering to maintain state across turns — LLM may break character or lose context","No built-in session management or state persistence — each turn is independent unless explicitly managed","Meta-instruction syntax (curly brackets) is ad-hoc and not formally specified — unclear how to handle edge cases","Limited to single-LLM interactions — no support for multi-agent or multi-turn conversation management","No error recovery or fallback behavior — if LLM breaks character, no mechanism to re-establish context"],"requires":["LLM with strong instruction-following and role-playing capabilities","Understanding of multi-turn conversation management","Manual testing to ensure LLM maintains character and context across turns","Custom logic to parse and handle meta-instructions within simulated environments"],"input_types":["markdown prompts with role definition and output format constraints","user input within simulated environment"],"output_types":["simulated terminal output","simulated spreadsheet responses","interview questions and feedback"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prompts-md__cap_5","uri":"capability://text.generation.language.language.processing.and.translation.prompt.templates","name":"language processing and translation prompt templates","description":"Includes prompts for language-related tasks such as translation, spelling correction, and language analysis. These prompts demonstrate how to structure instructions for linguistic tasks, including handling of multiple languages, output format specifications (e.g., 'only provide the corrected text'), and domain-specific constraints that ensure LLM outputs are suitable for downstream language processing applications.","intents":["I want a prompt template for translating text between languages with consistent output format","I need a prompt that corrects spelling and grammar without adding explanations","I want to understand how to structure prompts for language analysis or linguistic tasks","I need prompts that handle multiple languages or language variants correctly"],"best_for":["Developers building translation or language processing applications","Teams automating content localization or language correction workflows","Linguists or language researchers studying LLM capabilities for language tasks","Prompt engineers specializing in language-specific applications"],"limitations":["No language-specific variants — unclear if prompts work equally well for all language pairs","No quality metrics or accuracy benchmarks — no indication of translation or correction quality","Limited scope of language tasks — only partial dataset visible, coverage unknown","No handling of context-dependent language features — prompts may not preserve tone, formality, or cultural nuance","No validation of linguistic correctness — users must manually verify outputs are linguistically sound"],"requires":["LLM with multilingual capabilities","Understanding of target language and linguistic requirements","Manual validation of translation or correction quality"],"input_types":["markdown prompts with language task specifications","text to be translated or corrected"],"output_types":["translated text","corrected text","linguistic analysis"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prompts-md__cap_6","uri":"capability://data.processing.analysis.hugging.face.dataset.mirroring.and.programmatic.access","name":"hugging-face-dataset-mirroring and programmatic access","description":"The prompt collection is mirrored on Hugging Face as the `fka/prompts.chat` dataset, enabling programmatic access through the Hugging Face datasets library without requiring direct GitHub access or manual markdown parsing. This integration allows users to load prompts as structured dataset rows using standard Python code, supporting batch processing, filtering, and integration with ML workflows.","intents":["I want to load prompts programmatically in Python for use in ML pipelines or batch processing","I need to integrate prompts into a Hugging Face-based workflow or training pipeline","I want to access prompts without parsing markdown manually or using GitHub raw content URLs","I need to work with prompts as structured dataset rows rather than raw text files"],"best_for":["Python developers building ML pipelines or LLM applications","Teams using Hugging Face ecosystem tools (transformers, datasets, etc.)","Researchers working with prompt datasets in Jupyter notebooks or ML frameworks","Developers automating prompt loading and processing in batch workflows"],"limitations":["Requires Python 3.6+ and datasets library installation — adds dependency management overhead","No guarantee of synchronization between GitHub and Hugging Face versions — may be out of sync","Hugging Face dataset structure not documented — unclear how prompts are represented as rows/columns","No filtering or search API provided by Hugging Face dataset — users must load full dataset and filter locally","No authentication required but Hugging Face API rate limits may apply for large-scale access"],"requires":["Python 3.6+","Hugging Face datasets library (pip install datasets)","Internet access to Hugging Face Hub","No authentication required for public dataset"],"input_types":["Hugging Face dataset identifier (fka/prompts.chat)"],"output_types":["Python Dataset object","structured rows with prompt text and metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-prompts-md__cap_7","uri":"capability://memory.knowledge.contributor.attribution.and.community.driven.prompt.curation","name":"contributor attribution and community-driven prompt curation","description":"Prompts in the collection include GitHub username attribution for each contributor, enabling transparent tracking of who created or contributed each prompt. This design supports community-driven curation where contributions are visible and attributable, though the dataset lacks formal governance, quality assurance processes, or mechanisms for feedback on prompt effectiveness.","intents":["I want to understand who contributed specific prompts and see their other contributions","I need to credit contributors when using or sharing prompts from this collection","I want to discover prompt engineers or practitioners whose work I respect","I need transparency about the source and provenance of prompts I'm using"],"best_for":["Community members contributing to or curating the prompt collection","Researchers studying prompt engineering practices and contributor patterns","Teams building internal prompt libraries who want to track prompt ownership","Users who value attribution and transparency in open-source collections"],"limitations":["No quality assurance or vetting process — any contributor can add prompts without validation","No feedback mechanism for rating or reviewing prompts — no way to assess contributor expertise","No contributor guidelines or standards — inconsistent prompt quality and format","No dispute resolution or moderation process — no mechanism to remove or correct problematic prompts","Attribution is GitHub username only — no way to contact contributors or provide feedback"],"requires":["GitHub account to contribute (if adding new prompts)","Understanding of markdown and GitHub pull request workflow","No formal contributor agreement or license specified"],"input_types":["GitHub username (for attribution)"],"output_types":["contributor attribution in markdown"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["HTTP client or curl for GitHub raw content access","Python 3.6+ with datasets library (pip install datasets) for Hugging Face integration","No authentication required for public access","Custom regex or string parsing logic to extract and substitute variables","No built-in library or SDK provided — implementation is user's responsibility","Understanding of the `${VariableName:DefaultValue}` syntax (inferred from examples, not formally documented)","Access to an LLM API or interface (ChatGPT, Claude, Ollama, etc.)","Understanding of LLM prompt structure and behavior","Manual testing to validate prompt effectiveness for specific use cases","LLM with strong technical knowledge in the target domain"],"failure_modes":["Static snapshot with no versioning or update tracking — cannot detect when prompts are added, modified, or deprecated","No structured metadata beyond contributor name — lacks creation date, quality metrics, success rates, or performance benchmarks","Incomplete documentation in public excerpt — full dataset scope unknown, content cuts off mid-prompt","No built-in search or filtering mechanism — users must manually parse markdown to find relevant prompts","No validation of prompt effectiveness — no quality assurance process visible for contributed prompts","No formal specification for template syntax — implementation details and parsing rules are not documented","No built-in validation or type checking — invalid or missing variables will be passed literally to the LLM","No escaping mechanism for literal curly braces — prompts containing `${...}` patterns unrelated to variables will be incorrectly parsed","Default values are optional but not enforced — no guarantee that all variables have sensible defaults","No tooling provided for template validation or testing — users must manually verify substitution works correctly","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.25,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"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":"2026-05-24T12:16:21.011Z","last_scraped_at":"2026-05-03T14:00:02.893Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=prompts-md","compare_url":"https://unfragile.ai/compare?artifact=prompts-md"}},"signature":"MVeqmvJVRLmv6siZeu1L8Vma2Qt9QxCg/+2CNQqROu0zEpMjZMJvTctDGpPt29q7XcYsKkWPcXmEHS7jRqqpCA==","signedAt":"2026-06-16T01:08:19.718Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/prompts-md","artifact":"https://unfragile.ai/prompts-md","verify":"https://unfragile.ai/api/v1/verify?slug=prompts-md","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"}}