{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ai-boost--awesome-prompts","slug":"ai-boost--awesome-prompts","name":"awesome-prompts","type":"prompt","url":"https://awesomegpt.vip","page_url":"https://unfragile.ai/ai-boost--awesome-prompts","categories":["prompt-engineering"],"tags":["awesome","awesome-list","chatgpt","gpt4","gpts","gptstore","papers","prompt","prompt-engineering"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-ai-boost--awesome-prompts__cap_0","uri":"capability://memory.knowledge.curated.prompt.retrieval.from.gpt.store.rankings","name":"curated-prompt-retrieval-from-gpt-store-rankings","description":"Provides access to a manually curated collection of prompts extracted from top-ranked GPTs in OpenAI's official GPT Store, organized by popularity ranking (1st, 2nd, 3rd, etc.) and functional category. 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This enables the repository to function as a community knowledge base where prompt engineering patterns are shared, iterated on, and attributed to contributors.","intents":["I want to share a prompt I've engineered with the community and get feedback","I'm looking for innovative or niche prompts that aren't in the official GPT Store","I want to contribute improvements to existing prompts and see them adopted by others"],"best_for":["prompt engineering communities and open-source contributors","developers building specialized GPTs for niche use cases","teams crowdsourcing prompt optimization across organizations"],"limitations":["No formal quality assurance or validation process for community contributions","Contributions lack performance metrics or user feedback ratings","No mechanism to track prompt evolution or version history within the repository","Attribution and licensing of community prompts may be ambiguous","Requires GitHub account and familiarity with pull request workflow to contribute"],"requires":["GitHub account with push access (for contributors)","Understanding of markdown formatting and repository structure","Compliance with repository contribution guidelines (if defined)"],"input_types":["markdown-formatted prompt text","prompt metadata (name, category, description, author)","optional: use case documentation, performance notes"],"output_types":["merged prompt file in repository","git commit history with contributor attribution","visibility in community-contributed section"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-boost--awesome-prompts__cap_3","uri":"capability://planning.reasoning.advanced.prompt.engineering.technique.documentation","name":"advanced-prompt-engineering-technique-documentation","description":"Aggregates academic research papers and technical documentation on advanced prompting methodologies including Chain-of-Thought (CoT), Tree-of-Thoughts (ToT), Graph-of-Thoughts (GoT), Skeleton-of-Thought (SoT), Algorithm-of-Thoughts (AoT), and Self-Consistency Improvement techniques. The papers/ directory serves as a curated research index bridging academic literature and practical prompt engineering, enabling developers to understand the theoretical foundations and implementation patterns for sophisticated reasoning prompts.","intents":["I want to understand the research behind advanced prompting techniques like chain-of-thought","I need to implement a reasoning-heavy prompt and want to study the academic approaches","I'm evaluating which prompting technique (CoT vs ToT vs GoT) is best for my use case"],"best_for":["prompt engineers building reasoning-intensive applications (planning, multi-step problem solving)","researchers studying LLM prompting techniques and their effectiveness","teams implementing advanced reasoning patterns and wanting to understand theoretical foundations"],"limitations":["Papers are static PDFs with no interactive explanations or code examples","No implementation guides or code samples showing how to apply techniques in practice","Papers may be outdated relative to latest LLM capabilities and prompting research","No performance benchmarks or comparative analysis of techniques across different models","Requires academic background to fully understand research papers"],"requires":["PDF reader or browser with PDF support","Understanding of academic paper structure and notation","Familiarity with LLM concepts (tokens, embeddings, reasoning)"],"input_types":["technique name (e.g., 'Chain-of-Thought', 'Tree-of-Thoughts')","research topic keyword"],"output_types":["PDF research papers","paper metadata (title, authors, publication venue, abstract)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-boost--awesome-prompts__cap_4","uri":"capability://safety.moderation.prompt.attack.and.defense.resource.collection","name":"prompt-attack-and-defense-resource-collection","description":"Maintains documentation and resources on prompt injection attacks, adversarial prompting, and prompt protection techniques, enabling developers to understand vulnerabilities in GPT-based systems and implement defensive measures. This capability addresses the security dimension of prompt engineering by collecting attack patterns, defense strategies, and mitigation approaches in a centralized, discoverable format.","intents":["I need to understand how prompts can be attacked or manipulated to prevent vulnerabilities in my GPT application","I want to test my GPT's robustness against adversarial prompts and prompt injection attacks","I'm building a production GPT system and need to implement prompt protection mechanisms"],"best_for":["security-conscious developers building GPT-based applications","teams conducting red-teaming or adversarial testing of LLM systems","prompt engineers responsible for production GPT deployments"],"limitations":["Attack and defense techniques are rapidly evolving; documentation may lag behind new attack vectors","No interactive testing environment or automated vulnerability scanning tools","Defense strategies are often heuristic-based and not guaranteed to prevent all attacks","Limited guidance on how to implement defenses in specific frameworks or platforms","No quantitative metrics on effectiveness of different defense approaches"],"requires":["Understanding of prompt injection and adversarial attack concepts","Access to GPT models for testing (API key or local deployment)","Security mindset and threat modeling experience"],"input_types":["attack pattern description or example","defense technique name","prompt vulnerability report"],"output_types":["attack pattern documentation","defense strategy explanation","mitigation technique examples","best practices guide"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-boost--awesome-prompts__cap_5","uri":"capability://automation.workflow.markdown.based.prompt.storage.and.versioning","name":"markdown-based-prompt-storage-and-versioning","description":"Implements a lightweight, git-based storage system where prompts are maintained as markdown files in a GitHub repository, enabling version control, change tracking, collaborative editing, and attribution through native git workflows. Each prompt is stored as a standalone markdown file with metadata (rank, category, description) embedded or inferred from filename and directory structure, making prompts both human-readable and machine-parseable.","intents":["I want to track changes to prompts over time and see who contributed each version","I need to integrate prompt retrieval into my CI/CD pipeline or development workflow","I want to maintain a version-controlled library of prompts for my team"],"best_for":["development teams using git-based workflows who want to version control prompts","open-source projects needing collaborative prompt management","organizations building internal prompt libraries with audit trails"],"limitations":["Markdown format is unstructured; no schema validation for prompt metadata","No built-in search or indexing; requires external tools or manual parsing for discovery","Scaling to thousands of prompts may make repository navigation cumbersome","No built-in versioning semantics (e.g., major/minor versions); relies on git commit messages","Requires git knowledge to contribute; higher barrier to entry than web-based interfaces"],"requires":["Git client and GitHub account","Markdown editor or IDE with markdown support","Understanding of git workflows (clone, commit, push, pull request)"],"input_types":["markdown-formatted prompt text","filename following naming convention","directory path indicating category"],"output_types":["markdown file in repository","git commit with change history","raw markdown URL for programmatic access","structured metadata inferred from filename/path"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-boost--awesome-prompts__cap_6","uri":"capability://search.retrieval.ranked.prompt.discovery.by.gpt.store.popularity","name":"ranked-prompt-discovery-by-gpt-store-popularity","description":"Exposes prompts ranked by their corresponding GPT's position in the OpenAI GPT Store (1st, 2nd, 3rd, etc.), providing a popularity-based ranking signal that correlates with real-world user adoption and perceived effectiveness. Developers can browse prompts ordered by rank to identify which prompt patterns are most successful in the market, using ranking as a proxy for prompt quality and effectiveness.","intents":["I want to see which prompts are most popular in the GPT Store to understand market trends","I'm looking for the highest-ranked prompt in a category to use as a baseline","I want to compare my prompt against the top-ranked prompts in the same domain"],"best_for":["prompt engineers benchmarking their work against market leaders","product managers analyzing GPT Store trends and user preferences","developers seeking proven, high-adoption prompt patterns"],"limitations":["Rankings are static snapshots and may not reflect current GPT Store rankings","Ranking algorithm is opaque; unclear whether rank correlates with quality, marketing, or user engagement","No access to actual ranking metrics (downloads, ratings, reviews) from GPT Store","Ranking may be biased toward popular categories or well-funded GPT creators","No time-series data showing how rankings have evolved"],"requires":["Access to repository with ranking metadata embedded in filenames or metadata","Understanding that ranking is a proxy for popularity, not necessarily quality"],"input_types":["category filter","rank range (e.g., top 10, top 100)"],"output_types":["ranked list of prompts with rank metadata","prompt files sorted by rank","category-level ranking summaries"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-boost--awesome-prompts__cap_7","uri":"capability://memory.knowledge.multi.domain.prompt.template.library","name":"multi-domain-prompt-template-library","description":"Maintains a comprehensive library of prompt templates spanning diverse domains (Academic, Programming, Design, Productivity, Lifestyle/Entertainment, Education) with specialized subcategories (literature review, code automation, logo design, task automation, adventure games, homework help). This enables developers to find domain-specific prompt patterns without building from scratch, with templates covering both common use cases and specialized applications.","intents":["I need a prompt template for academic writing but don't want to engineer from scratch","I'm building a multi-domain application and need reference templates across several areas","I want to understand how prompts differ across domains (e.g., academic vs. creative writing)"],"best_for":["developers building domain-specific GPT applications","non-technical users seeking ready-to-use prompts for common tasks","teams prototyping multi-domain LLM systems"],"limitations":["Templates are generic and may require significant customization for specific use cases","No guidance on how to adapt templates across domains or combine templates","Limited documentation on template parameters, variables, or customization points","Templates may not reflect latest best practices or research in prompt engineering","No performance benchmarks or effectiveness metrics for templates"],"requires":["Understanding of the target domain (e.g., academic writing conventions)","Ability to customize templates for specific use cases"],"input_types":["domain name (Academic, Programming, Design, etc.)","use case or subcategory","optional: customization parameters"],"output_types":["prompt template text","template metadata (domain, subcategory, use case)","customization guidance"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ai-boost--awesome-prompts__cap_8","uri":"capability://automation.workflow.open.source.prompt.knowledge.base.with.community.governance","name":"open-source-prompt-knowledge-base-with-community-governance","description":"Implements an open-source knowledge base model where prompts are maintained under a permissive license (typically MIT or similar), enabling free access, modification, and redistribution by the community. The repository structure and contribution workflow enable community governance, where improvements and new prompts are contributed via pull requests, reviewed by maintainers, and merged into the main collection, creating a self-sustaining ecosystem.","intents":["I want to use and modify prompts freely without licensing restrictions","I want to contribute my prompt improvements back to the community","I need a prompt library that's maintained by the community rather than a single vendor"],"best_for":["open-source projects and communities","organizations wanting to avoid vendor lock-in on prompt libraries","developers contributing to collective prompt engineering knowledge"],"limitations":["No formal governance structure or decision-making process for major changes","Maintenance burden falls on volunteer contributors; may lead to stale or outdated content","No service-level agreements or guaranteed support","Quality control depends on maintainer review capacity","No commercial support or professional services available"],"requires":["Acceptance of open-source license terms","GitHub account for contributing","Understanding of community contribution norms"],"input_types":["prompt contributions via pull request","issue reports and feature requests"],"output_types":["merged contributions","community-maintained prompt library","attribution and credit to contributors"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Access to GitHub repository (no authentication required for public access)","GPT model API access (OpenAI API key) to use retrieved prompts","Understanding of markdown format for prompt files","Ability to navigate GitHub directory structure or markdown file listings","Basic understanding of domain terminology (e.g., 'literature review', 'code automation')","GitHub account with push access (for contributors)","Understanding of markdown formatting and repository structure","Compliance with repository contribution guidelines (if defined)","PDF reader or browser with PDF support","Understanding of academic paper structure and notation"],"failure_modes":["Prompts are static snapshots and may not reflect real-time GPT Store rankings or updates","No guarantee that extracted prompts are complete or unmodified from original GPT implementations","Prompts are OpenAI GPT-specific; transferability to other LLM providers (Claude, Llama) requires manual adaptation","Community contributions lack formal validation or performance metrics","Categories are manually maintained and may not reflect emerging use cases or new GPT Store categories","No cross-category search or tag-based filtering beyond directory structure","Subcategory organization is inconsistent across domains (some have detailed breakdowns, others are flat)","No quantitative metrics (usage, ratings, performance) associated with category-level aggregations","No formal quality assurance or validation process for community contributions","Contributions lack performance metrics or user feedback ratings","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.33607991572997475,"quality":0.43,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:59:50.673Z","last_commit":"2026-05-03T10:07:26Z"},"community":{"stars":7780,"forks":713,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ai-boost--awesome-prompts","compare_url":"https://unfragile.ai/compare?artifact=ai-boost--awesome-prompts"}},"signature":"a4oaFkbb98dQbLgrdyW+vOSM7bjb0/2w7m8Iy7j+xwYk1CMiT3/EaBMOF4codiujCcZFcbsiVFk6C0FXqhKcCA==","signedAt":"2026-06-21T10:18:02.687Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai-boost--awesome-prompts","artifact":"https://unfragile.ai/ai-boost--awesome-prompts","verify":"https://unfragile.ai/api/v1/verify?slug=ai-boost--awesome-prompts","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"}}