Builders for the $search and $vectorSearch aggregation stages. Atlas-only — there is no local MongoDB equivalent. All tests gated by ATLAS_SEARCH_TESTS_ENABLED and ATLAS_SEARCH_URI. Two builder hubs reachable from Builders<TDocument>: SearchScoreFunction name collision. The name is shared by two unrelated types. Builders<T>.SearchScoreFunction (a property) returns a SearchScoreFunctionBuilder<TDocument>, which produces the generic SearchScoreFunction<TDocument> expression class for Function(...) score modifiers. Separately, at the driver root, SearchScoreFunction is also a plain enum (src/MongoDB.Driver/SearchScoreFunction.cs) used by VectorSearchOptions.EmbeddedScoreMode. They share a name but aren't overloads. Don't…
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Shared test infrastructure for the driver test projects. Hosts integration-test base classes, fixtures, mock implementations, fail-point helpers, event capture, and xUnit extensions. Sibling helper projects: tests/MongoDB.Bson.TestHelpers/ (BSON-specific) and tests/MongoDB.TestHelpers/ (cross-cutting xUnit). Under Core/JsonDrivenTests/: a small set of event asserters and field-setter helpers (CommandStartedEventAsserter, EventAsserterFactory, RecursiveFieldSetter) used by the spec runners in tests/MongoDB.Driver.Tests/Specifications/. The broader JSON-driven test base classes (test factories, document/value comparers, JSON resource loading) live in the sibling project at tests/MongoDB.Bson.TestHelpers/JsonDrivenTests/, not here.
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JSON-driven runners for the cross-driver test suite. The JSON test files mirror those at https://github.com/mongodb/specifications and are embedded as project resources; the C# code in this directory wires them up to xUnit. Many spec areas have both legacy and unified subdirectories during the migration window; new tests should land in unified.
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A repository of skills, prompts, and MCP configurations for AI coding agents working with Azure SDKs and Microsoft AI Foundry services. Azure SDKs and Foundry APIs change constantly. Never work with stale knowledge. Before implementing anything with Azure/Foundry SDKs: If you skip this step and use outdated patterns, you will produce broken code. These principles reduce common LLM coding mistakes. Apply them to every task. Don't assume. Don't hide confusion. Surface tradeoffs. Minimum code that solves the problem. Nothing speculative.
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Generate AGENTS.md files for pertinent repository folders — only where missing. Provides coding agents with build commands, testing instructions, code style, project structure, and boundaries.
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This folder contains a test harness for evaluating AI-generated code against acceptance criteria for skills. What we're testing: Skills in .github/skills/ that provide domain knowledge for Azure SDKs. How it works: Run pnpm harness --list from the tests/ directory to see all skills with criteria. Location: tests/scenarios/<skill-name>/acceptance-criteria.md Source materials (in order of priority): Critical: Document import distinctions carefully. Many Azure SDKs have models in different locations (e.g., azure.ai.agents.models vs azure.ai.projects.models). Location: tests/scenarios/<skill-name>/scenarios.yaml Scenario design principles: Success criteria:
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Read and follow .github/copilot-instructions.md before writing or reviewing code. It contains MXC's architecture, compatibility, security, build, and SDK rules. The expectations below apply in addition. - Understand the existing flow before editing: callers, shared helpers, backend ownership, errors, cleanup, and affected SDK entry points. A working happy path is not enough; check how the change composes with existing behavior. - Put behavior where it belongs. Parsers decode contracts and preserve field presence; backend-specific defaults, policy checks, and enforcement belong in…
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This file provides guidance for AI coding agents working on the osv.dev repository. It outlines the project structure, setup commands, testing instructions, and coding standards. [!IMPORTANT] Keeping this file up to date: If you (an AI agent) make any major architectural changes, introduce new services, or modify core workflows (like testing or linting), you MUST update this AGENTS.md file to reflect those changes. These models are defined in Python (osv/models.py) and mirrored in Go (go/internal/database/datastore/models.go).
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Tau is a Python implementation of Pi's minimalist coding-agent harness architecture. The goal is to develop it incrementally, with each phase clearly documented and tested. The implementation roadmap is tracked in GitHub issue #1: Use that issue as the primary reference for phase ordering and architectural intent. Preserve Pi's core separation of concerns: Tau should be organized around these layers: Keep the core agent package independent of CLI, Textual, Rich rendering, session file locations, and application-specific resource loading. Use Textual…
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These standards apply to AI coding agents working in this repository (Cursor, Claude Code, and compatible hosts). NVIDIA NeMo Retriever Library (NRL) is the multimodal extraction and retrieval library formerly known in docs as NV-Ingest. Published customer documentation lives primarily under docs/docs/extraction/ and builds with MkDocs (docs/mkdocs.yml). Library and Helm sources live under nemo_retriever/. Select the documenta
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You are a documentation engineer and writer for NeMo Retriever Library (NRL) user-facing docs. Treat docs/docs/extraction/ as the primary source of truth for published extraction content. MkDocs config and redirects live in docs/mkdocs.yml. Apply these rules to documentation, examples, headings, UI text, and release notes that you create or edit. - Write in a professional, active, conversational voice. - Use active voice whenever possible. Use present tense for product behavior. Address the reader in second person as "you." - Keep…
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Instructions for AI coding agents working with this codebase. This is a Turborepo monorepo with pnpm workspaces: The CLI is a Rust binary with an npm shim. The Rust crate lives in packages/opensrc/cli/. Or from root: turbo dev, turbo build Releases are manual, single-PR affairs. The maintainer controls the changelog voice and format. To prepare a release: When the PR merges, CI compares packages/opensrc/package.json version to what's on npm. If it differs, it builds all 7 platform binaries, publishes to…
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The website content tree docsy.dev/content/en/project/ ([project][]) is the authoritative source for project structure, branching model, CI/CD, and release process; note in particular [git-repo.md][]. [project]: docsy.dev/content/en/project/_index.md [git-repo.md]: docsy.dev/content/en/project/build/git-repo.md - Assume the maintainers and readers of the code you write are senior web developers and designers who know the site's tools, including Hugo and Bootstrap. - Write lean and DRY, in code, comments, and commit messages. - npm package scripts run under Bash on every platform (script-shell is pinned in the root…
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Guidance for AI coding agents working in this repository. Human contributors should also read CONTRIBUTING.md and the documentation site which are the source of truth. The NVIDIA GPU Operator is a Kubernetes operator (built with Kubebuilder v3) that automates the management of all NVIDIA software components needed to provision GPU nodes in a Kubernetes cluster. These components include the NVIDIA drivers (to enable CUDA), Kubernetes Device Plugin for NVIDIA GPUs, the NVIDIA Container Toolkit, GFD for node labeling, DCGM based…
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MongoDBAGENTS.md
