PROMPTS.md
Dataset| [Hugging Face Dataset](https://huggingface.co/datasets/fka/prompts.chat) |
Capabilities8 decomposed
markdown-based prompt template library with contributor attribution
Medium confidenceProvides 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.
Combines GitHub raw file hosting with Hugging Face dataset mirroring, enabling both direct markdown parsing and programmatic access through the datasets library without requiring a custom API layer. Uses simple markdown structure with contributor attribution via GitHub usernames, making contributions transparent and discoverable.
Simpler and more transparent than proprietary prompt marketplaces because it's version-controlled on GitHub with visible contributor history, and more accessible than academic prompt datasets because it requires no authentication or complex tooling.
template variable substitution with default value syntax
Medium confidenceSupports parameterized prompts using `${VariableName:DefaultValue}` syntax embedded in prompt text, allowing users to inject dynamic values (job titles, names, domains) before passing prompts to LLMs. This enables a single prompt template to be reused across multiple contexts without manual editing, though the syntax is ad-hoc and lacks formal specification or validation tooling.
Uses a simple `${VariableName:DefaultValue}` syntax for inline variable substitution within markdown prompts, allowing templates to be self-contained with fallback defaults. This approach prioritizes human readability over formal specification, making templates easy to read and edit in any text editor without special tooling.
More readable and portable than Jinja2 or Handlebars templating because it uses a minimal, domain-specific syntax that doesn't require learning a full template language, but less robust because it lacks validation and error handling.
role-playing and behavioral constraint prompt patterns
Medium confidenceProvides 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.
Demonstrates practical prompt patterns combining role definition with explicit output constraints (e.g., 'act as X' + 'only reply with Y format'), showing how to layer multiple instruction types to achieve reliable LLM behavior. Includes domain-specific examples like terminal emulation and interview simulation that require both role adoption and strict output formatting.
More practical than academic prompt engineering papers because it provides ready-to-use examples with real-world patterns, but less rigorous than formal prompt optimization frameworks because it lacks systematic evaluation or theoretical grounding.
domain-specific prompt collection for coding and technical domains
Medium confidenceIncludes 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.
Provides specialized prompts for technical domains that require LLMs to understand and output domain-specific syntax (Solidity, shell commands, JavaScript), including prompts that simulate interactive environments (terminal, runtime) rather than just generating code. This demonstrates how to structure prompts for stateful, interactive technical simulations.
More specialized than general-purpose prompt libraries because it includes domain-specific examples and patterns, but less comprehensive than dedicated technical prompt frameworks because it lacks systematic coverage of all technical domains and no validation of technical correctness.
interactive simulation prompts for terminal, spreadsheet, and interview scenarios
Medium confidenceProvides 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.
Combines role definition with strict output format constraints and meta-instruction handling (curly bracket syntax) to enable stateful, multi-turn simulations where LLMs maintain consistent behavior across interactions. This approach allows a single prompt to establish both the simulation environment and the mechanism for users to embed instructions within that environment.
More sophisticated than simple role-playing prompts because it handles multi-turn interactions and meta-instructions, but less robust than dedicated simulation frameworks because it relies entirely on LLM instruction-following without explicit state management or error recovery.
language processing and translation prompt templates
Medium confidenceIncludes 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.
Provides language-specific prompt templates that combine task definition (translate, correct) with output format constraints ('only provide corrected text') to ensure LLM outputs are suitable for downstream processing without additional parsing or cleanup. Demonstrates how to handle multilingual tasks within a single prompt framework.
More accessible than specialized NLP libraries because it uses simple prompts that work with any LLM, but less accurate than dedicated translation or language processing models because it relies on general-purpose LLM capabilities rather than specialized training.
hugging-face-dataset-mirroring and programmatic access
Medium confidenceThe 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.
Provides dual-channel access to prompts via both GitHub raw files and Hugging Face datasets library, enabling both direct markdown parsing and programmatic Python access without custom API infrastructure. This approach leverages Hugging Face's dataset distribution and caching mechanisms while maintaining GitHub as the source of truth.
More convenient than GitHub-only distribution because it integrates with Hugging Face ecosystem tools and provides caching/offline access, but less feature-rich than a dedicated prompt management API because it lacks search, filtering, versioning, and metadata query capabilities.
contributor attribution and community-driven prompt curation
Medium confidencePrompts 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.
Uses GitHub username attribution to make prompt contributions transparent and discoverable, enabling community members to identify and follow prompt engineers whose work they value. This approach leverages GitHub's social features (user profiles, contribution history) to support community curation without requiring a dedicated platform.
More transparent than proprietary prompt marketplaces because contributions are publicly visible and attributable, but less structured than formal open-source projects because it lacks contribution guidelines, code review processes, or quality assurance mechanisms.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with PROMPTS.md, ranked by overlap. Discovered automatically through the match graph.
LangGPT
LangGPT: Empowering everyone to become a prompt expert! 🚀 📌 结构化提示词（Structured Prompt）提出者 📌 元提示词（Meta-Prompt）发起者 📌 最流行的提示词落地范式 | Language of GPT The pioneering framework for structured & meta-prompt design 10,000+ ⭐ | Battle-tested by thousands of users worldwide Created by 云中江树
Awesome ChatGPT Prompts
Curated collection of 150+ ChatGPT prompt templates.
Awesome ChatGPT prompts
... just follow [@goodside](https://twitter.com/goodside)
llamaindex
<p align="center"> <img height="100" width="100" alt="LlamaIndex logo" src="https://ts.llamaindex.ai/square.svg" /> </p> <h1 align="center">LlamaIndex.TS</h1> <h3 align="center"> Data framework for your LLM application. </h3>
WeChatAI
All in One AI Chat Tool( GPT-4 / GPT-3.5 /OpenAI API/Azure OpenAI/Prompt Template Engine)
Pezzo
Accelerate AI development with streamlined collaboration and deployment...
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
- ✓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
- ✓Prompt engineers learning best practices for role-based and constraint-based prompting
Known 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
- ⚠No formal specification for template syntax — implementation details and parsing rules are not documented
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
| [Hugging Face Dataset](https://huggingface.co/datasets/fka/prompts.chat) |
Categories
Alternatives to PROMPTS.md
Are you the builder of PROMPTS.md?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →