AGENTS: document learnings from split-index + fsmonitor investigation
While investigating a CI failure in the `linux-TEST-vars` job caused by
the interaction between the `pt/fsmonitor-linux` and
`hn/git-checkout-m-with-stash` topics in `seen`, several debugging
techniques proved essential and were not previously documented.
The investigation required bisecting the first-parent history of `seen`
while temporarily merging the fsmonitor topic at each step. This
revealed that `GIT_TEST_SPLIT_INDEX=yes` corrupts the bisect
machinery's own index operations unless it is unset before cleanup
checkouts. It also revealed that `fprintf(stderr, ...)` instrumentation
in Git's C code is swallowed by the test framework, making Trace2 the
correct instrumentation approach.
A key insight was that the bug appeared Linux-specific only because
`linux-TEST-vars` is the sole CI job setting `GIT_TEST_SPLIT_INDEX=yes`;
there is no macOS or Windows equivalent. The actual root cause (the
`index.skipHash=true` + split-index interaction producing a null
`base_oid` in the shared index) is platform-independent.
Add four documentation sections capturing these learnings: bisecting
`seen` interactions, reproducing with exact CI variables, verifying CI
platform coverage before concluding platform-specificity, and using
Trace2 for instrumentation inside the test framework.
Assisted-by: Claude Opus 4.6
Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
AGENTS.md: add pre-commit checklist for lint checks
Bundle the existing ASCII-only, 80-column, and whitespace validation
recipes into a "pre-commit checklist" block that agents should run
before every commit. The individual recipes already existed in the
Coding Conventions section but were presented as reference material
rather than as an actionable workflow step.
Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
Assisted-by: Claude Opus 4.6
AGENTS.md: document rebase, staging, and log -L tricks for AI agents
Add practical recipes for three workflows that are particularly useful
when AI agents work with Git:
Non-interactive "interactive" rebases using `sed -i 1ib` as a sequence
editor to insert a `break` command, then editing the todo file directly
via the path from `git rev-parse --git-path rebase-merge/git-rebase-todo`.
This avoids the impossible task of driving an interactive editor from an
AI agent.
Scripted hunk staging via `printf '%s\n' s y q | git add -p`, which
feeds predictable keystrokes to the add-patch protocol to stage
individual hunks without human interaction.
The `git log -L <start>,+<count>:<file>` trick for finding which commit
last touched specific lines, enabling an `hg absorb`-like workflow where
the agent can identify the right fixup! target surgically rather than
grepping through full diffs.
Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
Assisted-by: Claude Opus 4.6
AGENTS.md: add upstream contribution and worktree guidance
Add sections covering the GitGitGadget workflow for contributing to
upstream Git, commit message conventions specific to the upstream
project, how to manage patch series with dependencies (branch
thickets), effective worktree usage including --update-refs for
history rewrites, and techniques for analyzing merge-structured
topic branches with git replay.
These learnings come from a session contributing the
safe.bareRepository test preparation patches via GitGitGadget.
Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
Assisted-by: Claude Opus 4.6
This adds an extensive section about resolving merge conflicts during
rebases, which happens quite often in Git for Windows' day-to-day.
Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
In this time and age, AI is everywhere. However, it's sometimes not very
easy to use. For green-field projects it works quite a bit better than
for existing legacy projects. And Git's source code is _quite_ as legacy
code as they come... 😁
Now, the only way how AI can be used efficiently with legacy code
is by providing enough information by way of prompt context for the
AI to have a chance to make any sense of the code. The structure and
the architecture is, after all, not designed for AI, but rather the
opposite: By virtue of having grown organically over two decades, there
is no design that AI coding models would readily grasp.
So here is a document that describes all kinds of aspects about this
project. The idea is to help AI by providing information that it does
not have ingrained in its weights. The idea is to provide information
that a human prompter might take for granted, but no coding model will
have been trained on specifically.
Assisted-by: Claude Opus 4.5
Signed-off-by: Johannes Schindelin <johannes.schindelin@gmx.de>
