Add AGENTS.md for MINGW-packages#194
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This is adapted from the MSYS2-packages version. The Perl-specific sections (upgrading Perl, patchprov, DLL/XS version mismatches, Windows Defender false positives, rebuild lists) were removed since they are not relevant to MINGW packages. The build instructions were updated to use makepkg-mingw with MSYSTEM=MINGW64 and the mingw64 toolchain on PATH, and to warn against login shells which hang in Git for Windows SDK environments. The playground workflow, updpkgsums/autoCRLF guidance, and general worktree workflow are retained unchanged. Assisted-by: Claude Opus 4.6 Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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AI-assisted contributions are a reality of open source, and that includes the maintainers themselves. Over recent months, AI has proven increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: chasing down CI failures, tracing cross-repository automation flows, and adapting workflows to upstream changes. This AGENTS.md provides enough context about the repository's architecture, conventions, and contracts that AI tools can produce reasonable results even when a human contributor fails to steer carefully. Similar files have been added to other repositories in the Git for Windows project: [`git`](git-for-windows/git#6198) and [`MINGW-packages`](git-for-windows/MINGW-packages#194). So let's add an `AGENTS.md` file to provide guidance for AI agents (and developers) working with this repository. It documents: - The repository's purpose and architecture (slash commands, GitForWindowsHelper dispatch, check-run mirroring) - Critical contracts with `gfw-helper-github-app` (workflow filenames, check-run names, artifact names) - Key workflows, JavaScript modules, and shell scripts - The MSYS2 runtime stack and how it relates to Git Bash - Coding conventions (JS, shell, YAML) including the YAML block scalar indentation pitfall - Known pitfalls (sparse checkout commits, `git describe` in merging-rebase topologies, version update script environments) - GitHub workflow secrets' names and purposes, cross-repo relationships, and validation guidance - The maintainer development environment layout
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Note: This backports https://github.com/git-for-windows/pull/6198 into `vfs-2.54.0`. AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37). Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
dscho
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Note: This backports https://github.com/git-for-windows/pull/6198 into `vfs-2.54.0`. AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37). Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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May 18, 2026
…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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May 19, 2026
…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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May 19, 2026
…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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May 21, 2026
…6198) AI-assisted contributions are a reality of open source in 2025 and beyond. Contributors will use AI tools, and that includes the maintainers themselves. Over recent months, I have found AI increasingly useful for the kind of menial, tedious work that does not require much creativity but is highly boring when done by hand: resolving merge conflicts during merging-rebases, chasing down CI failures across platforms, adapting downstream patches to upstream API changes. To that end, I would like to have an `AGENTS.md` file in the code base that helps any LLM to understand the context of the project. A secondary goal of this is to preemptively help outside contributors. The risk is not AI usage per se, but low-quality AI slop: contributions where the human hits "accept" without sufficient context being available to the model (and without proper review by the human, we've all been there), resulting in changes that miss conventions, break patterns, or misunderstand the project's architecture. Git's source code is about as legacy as they come, having grown organically over two decades with no design that AI coding models would readily grasp from a narrow code sample alone. This `AGENTS.md` is designed to raise the floor on AI-assisted contributions by providing enough context that even when a human contributor fails to steer carefully, the model has the information it needs to produce something reasonable. It documents the repository structure, build process, test conventions, the object model and ODB internals, debugging techniques (Trace2, instrumenting tests, bisecting failures), the merging-rebase workflow, conflict resolution patterns, coding conventions (ASCII only, 80 columns, tabs), commit message expectations, and the GitGitGadget contribution workflow. This is information that a human might take for granted, but no coding model will have been trained on specifically. Similar `AGENTS.md` files have recently been added to other repositories in the Git for Windows project: [MINGW-packages](git-for-windows/MINGW-packages#194), [git-for-windows.github.io](git-for-windows/git-for-windows.github.io#88) and [msys2-runtime](git-for-windows/msys2-runtime@1e0ff37).
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This adds an AGENTS.md file to help AI tools work effectively with
this repository.
The file serves two purposes. First, it raises the floor for flyby
contributions that use AI. Without guidance, AI-assisted contributions
tend to produce low-quality patches that ignore the project's
conventions and workflows. The AGENTS.md file cannot guarantee good
contributions, but it gives AI tools enough context to produce
something that is at least a reasonable starting point for a human
to refine.
Second, and more importantly, it helps the maintainers of Git for
Windows use AI more effectively for the kind of menial work that does
not require much creativity but is tedious to do by hand. A good
example is the
src/playground/workflow for managing patches: importingtarballs, applying patches as commits, ensuring stable OIDs so that
format-patch output is reproducible, and re-exporting after amendments.
This is exactly the sort of mechanical, non-trivial work where AI
assistance is valuable, but only if the AI knows the workflow.
The file covers:
package classes (MSYS vs MINGW)
makepkg-mingwfrom PowerShell (including the loginshell hang trap)
$^Osituation (cygwin vs msys) and its implications fortest skip guards and Windows Firewall hangs
commit OIDs
amend!/autosquash rather thanlayering new patches