Some notes on AI Agent Rule / Instruction / Context files / etc Based on the article, there are several emerging standards for guiding AI coding agents, including **AGENTS.md** (an open format for guiding coding agents) and **DESIGN.md** (a specification for describing visual identity to agents). The article notes that the **AGENT.md** standard appears to have been deprecated or updated to align with **AGENTS.md**, as evidenced by redirects from agent.md to agents.md. Additionally, the **/llms.txt** proposal is mentioned as a standard for providing LLM-friendly content on websites at inference time. AI Agent Rule / Instruction / Context files / etc Some notes on AI Agent Rule / Instruction / Context files / etc. Table of Contents < -- TOC start generated with https://bitdowntoc.derlin.ch/ -- - AGENTS.md agentsmd - AGENT.md Legacy agentmd-legacy - DESIGN.md designmd - llms.txt llmstxt - Agents / Tools agents--tools - Better Touch Tool BTT / h@llo.ai better-touch-tool-btt--hlloai - Claude Code claude-code - Claude Desktop claude-desktop - Continue continue - Cursor cursor - Gemini CLI gemini-cli - GitHub Copilot github-copilot - Humanloop humanloop - JetBrains AI Assistant jetbrains-ai-assistant - JetBrains Junie jetbrains-junie - OpenAI Codex openai-codex - OpenAI Codex CLI openai-codex-cli - Sourcegraph Amp sourcegraph-amp - Prompts prompts - Unsorted unsorted - See Also see-also - My Other Related Deepdive Gist's and Projects my-other-related-deepdive-gists-and-projects < -- TOC end -- AGENTS.md - https://agents.md/ - https://github.com/openai/agents.md - AGENTS.md — a simple, open format for guiding coding agents - AGENTS.md https://agents.md is a simple, open format for guiding coding agents. AGENT.md Legacy - See: - AGENTS.md agentsmd - https://github.com/agentmd/agent.md - AGENT.md: The Universal Agent Configuration File - This repository defines AGENT.md, a standardized format that lets your codebase speak directly to any agentic coding tool. - Note: I believe this standard or at least the non-plural filename pre-dated AGENTS.md , and has since sort of been deprecated or updated to align more with AGENTS.md - https://github.com/agentmd/agent.md/issues/2 - Issue 2: Redirect to agents.md? - It seems at least the main website redirects now: - http://agent.md/ is a 307 Temporary Redirect to https://agent.md/ - https://agent.md/ is a 301 Moved Permanently to https://ampcode.com/agent.md - https://ampcode.com/agent.md is a 302 Found to https://agents.md Though it might also be useful to add a banner to the README.md or similar directing people to the appropriate site/repo too: - https://agents.md/ - https://github.com/openai/agents.md - AGENTS.md — a simple, open format for guiding coding agents - AGENTS.md https://agents.md is a simple, open format for guiding coding agents. Originally posted by @0xdevalias in 2 https://github.com/agentmd/agent.md/issues/2 issuecomment-3403846700 https://github.com/agentmd/agent.md/issues/2 DESIGN.md - https://github.com/google-labs-code/design.md - DESIGN.md - A format specification for describing a visual identity to coding agents. DESIGN.md gives agents a persistent, structured understanding of a design system. - https://stitch.withgoogle.com/docs/design-md/specification - The DESIGN.md specification - The formal specification for the DESIGN.md format — token schema, section structure, and type system. - A DESIGN.md file has two layers. The YAML front matter contains machine-readable design tokens — the precise values agents use to enforce consistency. The markdown body provides human-readable design rationale organized into sections. Prose may use descriptive color names e.g., “Midnight Forest Green” that correspond to systematic token names e.g., primary . The tokens are the normative values; the prose provides context for how to apply them. The spec is a foundation, not a prescription. It provides common ground that agents, tools, and teams can rely on, while preserving the freedom to extend the format for domain-specific needs. - https://blog.google/innovation-and-ai/models-and-research/google-labs/stitch-design-md/ - Stitch’s DESIGN.md format is now open-source so you can use it across platforms Apr 21, 2026 - https://www.youtube.com/watch?v=W1gWIQp9k1Y - YouTube: Meet DESIGN.md: A new open standard for AI-generated UI 10:15 llms.txt - https://llmstxt.org/ - The /llms.txt file - A proposal to standardise on using an /llms.txt file to provide information to help LLMs use a website at inference time. - https://llmstxt.org/ proposal - Proposal We propose adding a /llms.txt markdown file to websites to provide LLM-friendly content. This file offers brief background information, guidance, and links to detailed markdown files. llms.txt markdown is human and LLM readable, but is also in a precise format allowing fixed processing methods i.e. classical programming techniques such as parsers and regex . We furthermore propose that pages on websites that have information that might be useful for LLMs to read provide a clean markdown version of those pages at the same URL as the original page, but with .md appended. URLs without file names should append index.html.md instead. - This proposal does not include any particular recommendation for how to process the llms.txt file, since it will depend on the application. For example, the FastHTML project opted to automatically expand the llms.txt to two markdown files with the contents of the linked URLs, using an XML-based structure suitable for use in LLMs such as Claude. The two files are: llms-ctx.txt https://answerdotai.github.io/fasthtml/llms-ctx.txt , which does not include the optional URLs, and llms-ctx-full.txt https://answerdotai.github.io/fasthtml/llms-ctx-full.txt , which does include them. They are created using the llms txt2ctx https://llmstxt.org/intro.html cli command line application, and the FastHTML documentation includes information for users about how to use them. - https://llmstxt.org/ format - Format - https://llmstxt.org/ existing-standards - Existing Standards - https://github.com/AnswerDotAI/llms-txt - The /llms.txt file, helping language models use your website - https://llmstxt.site/ - llms.txt directory - A list of all llms.txt file locations across the web with stats. The llms.txt is derived from the llmstxt.org standard. - https://github.com/krish-adi/llmstxt-site - llmstxt-site - This is a centralized directory of all /llms.txt files available online. The /llms.txt file is a proposed standard for websites to provide concise and structured information to help large language models LLMs efficiently use website content during inference time. Contributions are the backbone of this repository’s success. Let’s work together to build a comprehensive resource for /llms.txt files and advance the adoption of this standard for LLM-friendly content - https://directory.llmstxt.cloud/ - /llms.txt directory - A curated directory of products and companies leading the adoption of the llms.txt standard. Agents / Tools Better Touch Tool BTT / h@llo.ai - https://docs.folivora.ai/docs/3000 hallo ai.html - https://docs.folivora.ai/docs/3000 hallo ai.html projects - Projects - When calling the AI Assistant while in some sort of project, BTT checks whether there is an AGENT.md or BTT.md file. If so it will read the content of that file and use it to adapt the assistant to the project. - https://docs.folivora.ai/docs/3012 halloai mcp.html - h@llo.ai MCP - BetterTouchTool's MCP server configuration by default is stored in this file: ~/Library/Application Support/BetterTouchTool/AI/btt-mcp-config.json - Alternative Configuration Locations It can also be stored in these files - however you should pick one and stick with that. - ~/.config/btt/mcp/.mcp.json - ~/.btt/mcp/.mcp.json - ~/Library/Application Support/BetterTouchTool/AI/ - /Library/Application Support/BetterTouchTool/AI/ Claude Code - See Also ? : - Claude Desktop claude-desktop - https://www.anthropic.com/engineering/claude-code-best-practices - Claude Code: Best practices for agentic coding - Claude Code is a command line tool for agentic coding. This post covers tips and tricks that have proven effective for using Claude Code across various codebases, languages, and environments. - https://www.anthropic.com/engineering/claude-code-best-practices 1-customize-your-setup - Customize your setup Claude Code is an agentic coding assistant that automatically pulls context into prompts. This context gathering consumes time and tokens, but you can optimize it through environment tuning. - Create CLAUDE.md files CLAUDE.md is a special file that Claude automatically pulls into context when starting a conversation. This makes it an ideal place for documenting: - Common bash commands - Core files and utility functions - Code style guidelines - Testing instructions - Repository etiquette e.g., branch naming, merge vs. rebase, etc. - Developer environment setup e.g., pyenv use, which compilers work - Any unexpected behaviors or warnings particular to the project - Other information you want Claude to remember There’s no required format for CLAUDE.md files. We recommend keeping them concise and human-readable. - You can place CLAUDE.md files in several locations: - The root of your repo , or wherever you run claude from the most common usage . Name it CLAUDE.md and check it into git so that you can share it across sessions and with your team recommended , or name it CLAUDE.local.md and .gitignore it - Any parent of the directory where you run claude . This is most useful for monorepos, where you might run claude from root/foo , and have CLAUDE.md files in both root/CLAUDE.md and root/foo/CLAUDE.md . Both of these will be pulled into context automatically - Any child of the directory where you run claude . This is the inverse of the above, and in this case, Claude will pull in CLAUDE.md files on demand when you work with files in child directories - Your home folder ~/.claude/CLAUDE.md , which applies it to all your claude sessions When you run the /init command, Claude will automatically generate a CLAUDE.md for you. - Tune your CLAUDE.md files Your CLAUDE.md files become part of Claude’s prompts, so they should be refined like any frequently used prompt. A common mistake is adding extensive content without iterating on its effectiveness. Take time to experiment and determine what produces the best instruction following from the model. You can add content to your CLAUDE.md manually or press the key to give Claude an instruction that it will automatically incorporate into the relevant CLAUDE.md . Many engineers use frequently to document commands, files, and style guidelines while coding, then include CLAUDE.md changes in commits so team members benefit as well. At Anthropic, we occasionally run CLAUDE.md files through the prompt improver https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/prompt-improver and often tune instructions e.g. adding emphasis with "IMPORTANT" or "YOU MUST" to improve adherence. - Curate Claude's list of allowed tools - There are four ways to manage allowed tools - ..snip.. - Manually edit your .claude/settings.json or ~/.claude.json we recommend checking the former into source control to share with your team . - ..snip.. - https://www.anthropic.com/engineering/claude-code-best-practices 2-give-claude-more-tools - Give Claude more tools - Use Claude with MCP Claude Code functions as both an MCP server and client. As a client, it can connect to any number of MCP servers to access their tools in three ways: - In project config available when running Claude Code in that directory - In global config available in all projects - In a checked-in .mcp.json file available to anyone working in your codebase . For example, you can add Puppeteer and Sentry servers to your .mcp.json , so that every engineer working on your repo can use these out of the box. - Use custom slash commands For repeated workflows—debugging loops, log analysis, etc.—store prompt templates in Markdown files within the .claude/commands folder. These become available through the slash commands menu when you type / . You can check these commands into git to make them available for the rest of your team. Custom slash commands can include the special keyword $ARGUMENTS to pass parameters from command invocation. - Putting the above content into .claude/commands/fix-github-issue.md makes it available as the /project:fix-github-issue command in Claude Code. You could then for example use /project:fix-github-issue 1234 to have Claude fix issue \ 1234. Similarly, you can add your own personal commands to the ~/.claude/commands folder for commands you want available in all of your sessions. Claude Desktop - See Also ? : - Claude Code claude-code - https://modelcontextprotocol.io/quickstart/user - For Claude Desktop Users Get started using pre-built servers in Claude for Desktop. - https://modelcontextprotocol.io/quickstart/user 2-add-the-filesystem-mcp-server - This will create a configuration file at: - macOS: ~/Library/Application Support/Claude/claude desktop config.json - Windows: %APPDATA%\Claude\claude desktop config.json Continue - https://hub.continue.dev/ - https://docs.continue.dev/ - Continue enables developers to create, share, and use custom AI code assistants with our open-source VS Code and JetBrains extensions and hub of models, rules, prompts, docs, and other building blocks - https://docs.continue.dev/reference - config.yaml Reference - Continue hub assistants are defined using the config.yaml specification. Assistants can be loaded from the Hub https://hub.continue.dev/explore/assistants or locally - Continue Hub https://hub.continue.dev/explore/assistants - YAML is stored on the hub and automatically synced to the extension - Locally - in your global .continue folder ~/.continue on Mac, %USERPROFILE%\.continue within .continue/assistants . The name of the file will be used as the display name of the assistant, e.g. My Assistant.yaml - in your workspace in a /.continue/assistants folder, with the same naming convention Config YAML replaces config.json https://docs.continue.dev/json-reference , which is deprecated. View the Migration Guide https://docs.continue.dev/yaml-migration . An assistant is made up of: \1. Top level properties , which specify the name , version , and config.yaml schema for the assistant \2. Block lists , which are composable arrays of coding assistant building blocks available to the assistant, such as models, docs, and context providers. A block is a single standalone building block of a coding assistants, e.g., one model or one documentation source. In config.yaml syntax, a block consists of the same top-level properties as assistants name , version , and schema , but only has ONE item under whichever block type it is. Examples of blocks and assistants can be found on the Continue hub https://hub.continue.dev/explore/assistants . Assistants can either explicitly define blocks - see Properties https://docs.continue.dev/reference properties below - or import and configure existing hub blocks. - https://docs.continue.dev/reference local-blocks - Local Blocks It is also possible to define blocks locally in a .continue folder. This folder can be located at either the root of your workspace these will automatically be applied to all assistants when you are in that workspace or in your home directory at ~/.continue these will automatically be applied globally . Place your YAML files in the following folders: Assistants: - .continue/assistants - for assistants Blocks: - .continue/rules - for rules - .continue/models - for models - .continue/prompts - for prompts - .continue/context - for context providers - .continue/docs - for docs - .continue/data - for data - .continue/mcpServers - for MCP Servers You can find many examples of each of these block types on the Continue Explore Page https://hub.continue.dev/explore/models - https://docs.continue.dev/reference complete-yaml-config-example - Complete YAML Config Example Putting it all together, here's a complete example of a config.yaml configuration file - https://docs.continue.dev/blocks/models - Model Blocks These blocks form the foundation of the entire assistant experience, offering different specialized capabilities: - Chat https://docs.continue.dev/customize/model-roles/chat : Power conversational interactions about code and provide detailed guidance - Edit https://docs.continue.dev/customize/model-roles/edit : Handle complex code transformations and refactoring tasks - Apply https://docs.continue.dev/customize/model-roles/apply : Execute targeted code modifications with high accuracy - Autocomplete https://docs.continue.dev/customize/model-roles/autocomplete : Provide real-time suggestions as developers type - Embedding https://docs.continue.dev/customize/model-roles/embeddings : Transform code into vector representations for semantic search - Reranker https://docs.continue.dev/customize/model-roles/reranking : Improve search relevance by ordering results based on semantic meaning - https://docs.continue.dev/blocks/context-providers - Context Blocks These blocks determine what internal information your AI assistant can access - https://docs.continue.dev/blocks/rules - Rules Blocks Think of these as the guardrails for your AI coding assistants: - Enforce company-specific coding standards and security practices - Implement quality checks that match your engineering culture - Create paved paths for developers to follow organizational best practices - https://docs.continue.dev/customize/deep-dives/rules continuerules - .continuerules You can create project-specific rules by adding a .continuerules file to the root of your project. This file is raw text and its full contents will be used as rules. - https://docs.continue.dev/blocks/prompts - Prompt Blocks These are the specialized instructions that shape how models respond: - Define interaction patterns for specific tasks or frameworks - Encode domain expertise for particular technologies - Ensure consistent guidance aligned with organizational practices - Can be shared and reused across multiple assistants - Act as automated code reviewers that ensure consistency across teams - https://docs.continue.dev/customize/deep-dives/prompts local-prompt-files - Local .prompt files In addition to Prompt blocks on the Hub, you can also define prompts in local .prompt files, located in the .continue/prompts folder at the top level of your workspace. This is useful for quick iteration on prompts to test them out before pushing up to the Hub. - Below is a quick example of setting up a prompt file: - Create a folder called .continue/prompts at the top level of your workspace - Add a file called test.prompt to this folder. - Write the following contents to test.prompt and save. - https://docs.continue.dev/customize/deep-dives/prompts format - Format The format is inspired by HumanLoops's .prompt file https://docs.humanloop.com/docs/prompt-file-format , with additional templating to reference files, URLs, and context providers. - https://docs.continue.dev/blocks/mcp - MCP Blocks Model Context Protocol servers provide specialized functionality: - Enable integration with external tools and systems - Create extensible interfaces for custom capabilities - Support more complex interactions with your development environment - Allow partners to contribute specialized functionality - Database Connectors: Understand schema and data models during development Cursor - https://docs.cursor.com/context/rules - Rules - Control how the Agent model behaves with reusable, scoped instructions. Rules allow you to provide system-level guidance to the Agent and Cmd-K AI. Think of them as a persistent way to encode context, preferences, or workflows for your projects or for yourself. - We support three types of rules: - Project Rules: Stored in .cursor/rules , version-controlled and scoped to your codebase. - User Rules: Global to your Cursor environment. Defined in settings and always applied. - .cursorrules Legacy : Still supported, but deprecated. Use Project Rules instead. - https://docs.cursor.com/context/rules project-rules - Project rules - Project rules live in .cursor/rules . Each rule is stored as a file and version-controlled. They can be scoped using path patterns, invoked manually, or included based on relevance. Use project rules to: - Encode domain-specific knowledge about your codebase - Automate project-specific workflows or templates - Standardize style or architecture decisions - https://docs.cursor.com/context/rules cursorrules-legacy - .cursorrules Legacy - The .cursorrules file in the root of your project is still supported, but will be deprecated. We recommend migrating to the Project Rules format for more control, flexibility, and visibility. - https://docs.cursor.com/context/ignore-files - Ignore Files - Control which files Cursor’s AI features and indexing can access using .cursorignore and .cursorindexingignore - Cursor reads and indexes your project’s codebase to power its features. You can control which directories and files Cursor can access by adding a .cursorignore file to your root directory. - https://docs.cursor.com/context/model-context-protocol - Model Context Protocol Connect external tools and data sources to Cursor using the Model Context Protocol MCP plugin system - https://docs.cursor.com/context/model-context-protocol configuring-mcp-servers - The MCP configuration file uses a JSON format - https://docs.cursor.com/context/model-context-protocol configuration-locations - Configuration Locations You can place this configuration in two locations, depending on your use case: - Project Configuration - For tools specific to a project, create a .cursor/mcp.json file in your project directory. This allows you to define MCP servers that are only available within that specific project. - Global Configuration - For tools that you want to use across all projects, create a ~/.cursor/mcp.json file in your home directory. This makes MCP servers available in all your Cursor workspaces. - https://github.com/PatrickJS/awesome-cursorrules - Awesome CursorRules - A curated list of awesome .cursorrules files for enhancing your Cursor AI experience. Gemini CLI - https://github.com/google-gemini/gemini-cli - Gemini CLI - An open-source AI agent that brings the power of Gemini directly into your terminal. - This repository contains the Gemini CLI, a command-line AI workflow tool that connects to your tools, understands your code and accelerates your workflows. - https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/configuration.md available-settings-in-settingsjson - Available settings in settings.json : - contextFileName string or array of strings : - Description: Specifies the filename for context files e.g., GEMINI.md , AGENTS.md . Can be a single filename or a list of accepted filenames. - Default: GEMINI.md - Example: "contextFileName": "AGENTS.md" - https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/configuration.md context-files-hierarchical-instructional-context - Context Files Hierarchical Instructional Context While not strictly configuration for the CLI's behavior , context files defaulting to GEMINI.md but configurable via the contextFileName setting are crucial for configuring the instructional context also referred to as "memory" provided to the Gemini model. This powerful feature allows you to give project-specific instructions, coding style guides, or any relevant background information to the AI, making its responses more tailored and accurate to your needs. The CLI includes UI elements, such as an indicator in the footer showing the number of loaded context files, to keep you informed about the active context. - Purpose: These Markdown files contain instructions, guidelines, or context that you want the Gemini model to be aware of during your interactions. The system is designed to manage this instructional context hierarchically. - https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/configuration.md example-context-file-content-eg-geminimd - Example Context File Content e.g., GEMINI.md GitHub Copilot - https://docs.github.com/en/copilot/tutorials/coding-agent/get-the-best-results adding-custom-instructions-to-your-repository - By adding custom instructions to your repository, you can guide Copilot on how to understand your project and how to build, test and validate its changes. - You can add instructions in a single .github/copilot-instructions.md file in the repository, or create one or more .github/instructions/ / .instructions.md files applying to different files or directories in your repository. - For more information, see Adding repository custom instructions for GitHub Copilot https://docs.github.com/en/copilot/customizing-copilot/adding-repository-custom-instructions-for-github-copilot?tool=webui . - https://docs.github.com/en/copilot/tutorials/coding-agent/get-the-best-results pre-installing-dependencies-in-github-copilots-environment - Pre-installing dependencies in GitHub Copilot's environment While working on a task, Copilot has access to its own ephemeral development environment, powered by GitHub Actions, where it can explore your code, make changes, execute automated tests and linters and more. If Copilot is able to build, test and validate its changes in its own development environment, it is more likely to produce good pull requests which can be merged quickly. To do that, it will need your project's dependencies. Copilot can discover and install these dependencies itself via a process of trial and error - but this can be slow and unreliable, given the non-deterministic nature of large language models LLMs . You can configure a copilot-setup-steps.yml file to pre-install these dependencies before the agent starts working so it can hit the ground running. For more information, see Customizing the development environment for GitHub Copilot coding agent https://docs.github.com/en/copilot/customizing-copilot/customizing-the-development-environment-for-copilot-coding-agent preinstalling-tools-or-dependencies-in-copilots-environment . - https://docs.github.com/en/copilot/how-tos/use-copilot-agents/coding-agent/customize-the-agent-environment preinstalling-tools-or-dependencies-in-copilots-environment - Preinstalling tools or dependencies in Copilot's environment - you can preconfigure Copilot's environment before the agent starts by creating a special GitHub Actions workflow file, located at .github/workflows/copilot-setup-steps.yml within your repository. A copilot-setup-steps.yml file looks like a normal GitHub Actions workflow file, but must contain a single copilot-setup-steps job. This job will be executed in GitHub Actions before Copilot starts working. - https://docs.github.com/en/copilot/how-tos/configure-custom-instructions/add-repository-instructions - Adding repository custom instructions for GitHub Copilot - Create a file in a repository that automatically adds information to questions you ask Copilot Chat. - https://docs.github.com/en/copilot/how-tos/configure-custom-instructions/add-repository-instructions creating-a-repository-custom-instructions-file - Creating a repository custom instructions file - Copilot Chat on the GitHub website, Copilot coding agent and Copilot code review support a single .github/copilot-instructions.md custom instructions file stored in the repository. In addition, Copilot coding agent supports one or more .instructions.md files stored within .github/instructions in the repository. Each file can specify applyTo frontmatter to define what files or directories its instructions apply to. - https://docs.github.com/en/copilot/how-tos/configure-custom-instructions/add-repository-instructions writing-effective-repository-custom-instructions - Writing effective repository custom instructions - The instructions you add to your custom instruction file s should be short, self-contained statements that provide Copilot with relevant information to help it work in this repository. Because the instructions are sent with every chat message, they should be broadly applicable to most requests you will make in the context of the repository. - https://docs.github.com/en/copilot/customizing-copilot/extending-copilot-chat-with-mcp - Extending Copilot Chat with the Model Context Protocol MCP - Learn how to use the Model Context Protocol MCP to extend Copilot Chat. - https://docs.github.com/en/copilot/customizing-copilot/extending-copilot-chat-with-mcp configuring-mcp-servers-in-visual-studio-code - To configure MCP servers in Visual Studio Code, you need to set up a configuration script that specifies the details of the MCP servers you want to use. You can configure MCP servers for either: - A specific repository. This will share MCP servers with anyone who opens the project in Visual Studio Code. To do this, create a .vscode/mcp.json file in the root of your repository. - Your personal instance of Visual Studio Code. You will be the only person who has access to configured MCP servers. To do this, add the configuration to your settings.json file in Visual Studio Code. - https://github.blog/changelog/2025-06-13-copilot-code-review-customization-for-all/ - Copilot code review now supports the same custom instructions used by Copilot Chat and coding agent—unlocking personalized, consistent AI reviews across your workflow. - You can now tailor Copilot code review using .github/copilot-instructions.md — the same customization file already used by Copilot Chat and Copilot coding agent. This brings a consistent way to shape how Copilot responds across your entire workflow. - https://github.blog/changelog/2025-07-23-github-copilot-coding-agent-now-supports-instructions-md-custom-instructions/ - GitHub Copilot coding agent now supports .instructions.md custom instructions - You can add custom instructions https://docs.github.com/enterprise-cloud@latest/copilot/how-tos/custom-instructions/adding-repository-custom-instructions-for-github-copilot to your repository to teach Copilot how the repository works as well as how to run any build steps, automated tests, or linters. With these instructions, Copilot can produce higher quality pull requests. Now, along with .github/copilot-instructions.md , Copilot coding agent supports .instructions.md files stored under .github/instructions . You can create many .instructions.md files, and each one can use YAML frontmatter to specify which files or directories it applies to. This means that you can give Copilot different instructions for different parts of your codebase. - https://plugins.jetbrains.com/plugin/17718-github-copilot/versions/stable/722432 - GitHub Copilot 1.5.42-241 - Added: Custom instructions for generating Chat and Git commit messages. Specify these in the .github/copilot-instructions.md or .github/git-commit-instructions.md files. - https://github.com/microsoft/copilot-intellij-feedback/issues/38 issuecomment-2763249941 - Support for repository custom instructions - You can create .github/copilot-instructions.md for custom instructions for inline chat and panel chat. Additionally, you can create custom instructions for Git commit message generation in: .github/git-commit-instructions.md - This is available in the latest release, 1.5.41. - https://copilot-instructions.md/ - Adding custom instructions for GitHub Copilot - https://copilot-instructions.md/prompts.html - Godlike Prompts Humanloop - https://humanloop.com/ - Your AI product needs evals The LLM evals platform for enterprises. Humanloop gives you the tools that top teams use to ship and scale AI with confidence. - https://humanloop.com/docs/reference/prompt-file-format - Prompt file format - Our file format for serializing Prompts to store alongside your source code. - Our .prompt file format is a serialized representation of a Prompt, designed to be human-readable and suitable for checking into your version control systems alongside your code. This allows technical teams to maintain the source of truth for their prompts within their existing version control workflow. - https://humanloop.com/docs/reference/prompt-file-format format - Format The format is heavily inspired by MDX