This guide helps new contributors get started with the OpenAI Agents JS monorepo. It covers repo structure, how to test your work, available utilities, file locations, and guidelines for commits and PRs. Location: AGENTS.md at the repository root. Follow SECURITY.md for repository scope and private vulnerability reporting, and CONTRIBUTING.md for contributor security practices. These requirements guide work; their presence does not prove that GitHub or registry controls are configured or that a security scan has completed.
Java SDK for the Model Context Protocol, enabling Java applications to implement MCP clients and servers (sync and async) over stdio, SSE, and Streamable HTTP transports. Formatting (spring-javaformat) is validated automatically as part of every build (bound to the validate phase), so a formatting violation fails ./mvnw test before any tests run. Fix violations with: Records in McpSchema are serialized directly to the MCP JSON wire format, so changing one is a wire-format change, not a routine refactor. Whether a…
The repository builds with the standard .NET SDK CLI; the SDK version is pinned in global.json. - Build the client: dotnet build src/Elastic.Clients.Elasticsearch/Elastic.Clients.Elasticsearch.csproj - Build and test the request converter: dotnet test RequestConverter.sln (requires the wasm-tools workload: dotnet workload install wasm-tools) A legacy Bullseye build system (./build.sh, .\build.bat, driven by the F# project build/scripts/scripts.fsproj) still backs a few CI workflows. It is planned to be modernized or removed; do not use it for local development. There is no separate lint…
This file provides authoritative context, architectural invariants, branching policies, and verification requirements for AI coding agents and maintainers working on google/recaptcha.
This file provides repository-specific guidance for working on Transformer Engine. README.rst, CONTRIBUTING.rst, docs/, qa/ remain the authoritative sources for user documentation, contribution policy, and executable CI behavior. Transformer Engine provides optimized building blocks for Transformer models on NVIDIA GPUs. It is not a complete model-training system. Higher-level frameworks and toolkits compose its operations and modules into models and own model-level orchestration. Most functionality is at or below the level of an individual Transformer layer. Tensor, sequence, expert, and context parallel…
All apps and examples with dev servers use portless to avoid hardcoded ports. Portless assigns random ports and exposes each app via .localhost URLs. Naming convention: - Docs app: docs.wterm → docs.wterm.localhost - Examples: <name>.wterm → <name>.wterm.localhost When adding a new app or example that runs a dev server, wrap its dev script with portless <name>: Do not add --port flags — portless handles port assignment automatically. Do not add portless as a project dependency; it must be installed globally.
This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in node_modules/next/dist/docs/ (resolved from this file's directory; in monorepos the next package may not be visible from the repo root) before writing any code. Heed deprecation notices. This block is written and re-added by next dev — verify at node_modules/next/dist/server/lib/generate-agent-files.js. Removing it from a diff only re-creates the uncommitted change; committing it with your work keeps the tree…
Most SDK source is generated by Castiron. Follow CONTRIBUTING.md before changing generated files. Handwritten policy, automation, tests, and examples should remain small and should not alter exported SDK APIs unless the change explicitly requires it. Follow the custom-code guidance. Budget changes belong in a separate PR containing only .castiron-ratchet.json, with an explicit justification in the PR description. Increases require a human approving review before merging. Agents may investigate and draft proposals, but must not approve budget increases (including through a…
You are a coding agent running on AWS Bedrock AgentCore. You fix bugs in GitHub repositories by reading issues, applying fixes, and submitting PRs. You have a gateway MCP server connected that provides GitHub tools. Use them directly — they are available as native tools. When given a prompt, act immediately: 1. Extract the repository owner, repository name, and issue number from the user's message. 2. Use the MCP tools to read the issue, fix the code, and submit a…
You are a coding agent running on AWS Bedrock AgentCore. You fix bugs in GitHub repositories by reading issues, applying fixes, and submitting PRs. You have a gateway MCP server connected that provides GitHub tools. Use them directly — they are available as native tools. When given a prompt, act immediately: 1. Extract the repository owner, repository name, and issue number from the user's message. 2. Use the MCP tools to read the issue, fix the code, and submit a…
This project contains configuration and infrastructure for an Amazon Bedrock AgentCore application. The agentcore/ directory is a declarative model of the project. The agentcore/cdk/ subdirectory uses the @aws/agentcore-cdk L3 constructs to deploy the configuration to AWS. The project uses a flat resource model. Agents, memories, credentials, gateways, evaluators, and policies are independent top-level arrays in agentcore.json. There is no binding between resources in the schema — each resource is provisioned independently. Agents discover memories and credentials at runtime via environment…
This project contains configuration and infrastructure for an Amazon Bedrock AgentCore application. The agentcore/ directory is a declarative model of the project. The agentcore/cdk/ subdirectory uses the @aws/agentcore-cdk L3 constructs to deploy the configuration to AWS. The project uses a flat resource model. Agents, memories, credentials, gateways, evaluators, and policies are independent top-level arrays in agentcore.json. There is no binding between resources in the schema — each resource is provisioned independently. Agents discover memories and credentials at runtime via environment…
This file is for the coding assistant (Claude Code, Codex, Cursor, Kiro). It is a thin orchestration layer that loads the aws-agents payments skill and hands off to it. Do not reinvent the skill's content -- load it and follow it. This runbook supports exactly two scenarios: A short request such as "use this folder to add AgentCore payments" is sufficient. If the scenario (existing vs new) cannot be determined from the prompt or workspace, ask one routing question before…
For humans: This file provides context for AI coding assistants (Kiro, Cursor, Claude Code, GitHub Copilot). For the human-readable documentation, see docs/, README.md, or docs/tutorial.md. This project is an event-driven insurance claims processor built on Amazon Bedrock AgentCore. It uses a dual-agent architecture (Claims Processor + Validation Agent) with cost-based model routing (Sonnet for reasoning, Haiku for validation) and a deterministic execution phase. Deploys as a single CloudFormation stack (AgentCore-ClaimsAgent-dev) via the AgentCore CLI. Important: AgentCore resources (Runtime, Gateway, Memory,…
MicrosoftAGENTS.md
