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Concurrent Branches

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kajisho5
concurrent-branches

Resolve conflicts and merges when several branches are open against one repo at the same time — the hotspot files every change must touch (registry manifests, a single version field, shared tool config, one aggregated test module, tracked build output), union-vs-recompute-vs-rebuild as three different correct resolutions, generated artifacts that must never be merged, non-deterministic serialization that makes every branch conflict, renumbered identifiers that git cannot show you, and why a clean auto-merge is not a passing test. Use when rebasing or merging a long-lived branch, resolving conflict markers, reviewing a merge commit, deciding what a repo should commit vs generate, or setting up a repo that will take parallel contributions.

Overview

Publisherkajisho5
Repositoryffmpeg-skill
Skill nameconcurrent-branches
Stars
1.8K
Forks
144
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by kajisho5 on GitHub. Read the source before you install it.

Installation

Install the Concurrent Branches AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kajisho5/ffmpeg-skill/tree/main/.claude/skills/concurrent-branches
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kajisho5/ffmpeg-skill.git /tmp/ffmpeg-skill
mkdir -p .claude/skills
cp -r /tmp/ffmpeg-skill/.claude/skills/concurrent-branches .claude/skills/concurrent-branches
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Concurrent Branches in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Concurrent Branches on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Concurrent Branches is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Merging Concurrent Branches

When several branches are open against the same repo at once, the conflicts do not land in the code you were thinking about. They land in a small, predictable set of hotspot files that nearly every change has to touch — a registry manifest, a version field, shared tool config, one aggregated test module, a tracked build output.

Each hotspot has exactly one correct resolution, and they are not the same rule. "Take ours" / "take theirs" is right for almost none of them. Worse, getting one wrong is silent: the dropped entry, the collided version, the stale bundle all merge green and are found later by someone who cannot reproduce them.

Find the hotspots before you need to resolve them

Files touched by nearly every commit are the files your branch will conflict on.

bash
# the 15 files most commits touch — your hotspot list
git log --format= --name-only -n 200 | sort | uniq -c | sort -rn | head -15

Write the resolution rule for each one into the contributing docs. Under conflict pressure, with a stale branch and a red CI run, nobody re-derives "the version field is recomputed, not merged" correctly.

Additive registries: union both sides, never choose one

A manifest that lists every component — plugin entries, module paths, exported names, a feature list. Two branches each append an entry, and the conflict spans both additions:

jsonc
<<<<<<< HEAD
    "./components/exporter",
    "./components/importer"        // theirs, already merged to main
=======
    "./components/exporter",
    "./components/validator"       // yours
>>>>>>> feature/validator

Both sides are correct and neither is a substitute for the other. Resolve by keeping every entry from both sides. Taking a side deletes a component that is still fully present in the tree — the code compiles, the tests pass, and the component is simply never registered or installed.

Nothing catches that unless you check the registry against the filesystem:

bash
# every registered path exists
jq -r '.plugins[].components[]' manifest.json | while read -r p; do
  [ -e "${p#./}" ] || echo "registered but missing: $p"
done
# every component on disk is registered
for d in components/*/; do
  grep -q "\"./${d%/}\"" manifest.json || echo "present but unregistered: $d"
done

Run it as a repo test, not as a merge-day ritual. It is the only thing standing between a mis-resolved conflict and a component that quietly does not ship.

Single-value counters: recompute from the integration branch

A version field, a sequence number, a "latest migration" pointer. Both branches bumped 2.17.0 to 2.18.0; both are wrong now, because a third branch already merged and main is at 2.18.0 too.

Union is meaningless here and picking a side reintroduces a collision. The rule is recompute from the current integration branch, not from either side of the conflict — every change that merged since you branched consumed one increment:

bash
git fetch origin main
git show origin/main:manifest.json | jq -r '.metadata.version'   # -> 2.18.0
# your branch takes 2.19.0, regardless of what your branch said before

Do this as the last step before pushing, not during the merge. If another branch lands while you resolve, you redo only one line.

Generated artifacts: rebuild, never merge

A committed minified bundle, a built PDF, a compiled schema, a checked-in snapshot. Git will happily produce a merge for the text ones and force a binary choice for the rest — and every such result is wrong, because a derived file's only correct content is whatever the merged sources generate.

Resolve the sources, then regenerate:

bash
git checkout --merge -- src/ ui/app.jsx   # resolve the real inputs first
make build                                 # regenerate the artifact
git add dist/bundle.js docs/handbook.pdf

Two things make this reviewable rather than a leap of faith:

  • Pin the generator version. If the bundler or typesetter is pinned, its output is byte-identical anywhere, and a reviewer can regenerate and diff to confirm the artifact matches the source. Unpinned, the artifact diff is a mix of your change and a tool upgrade, and nobody can tell them apart.
  • Verify only the expected regions moved. A rebuilt document reflows: a one-paragraph edit typically spreads across two or three consecutive pages. Raster-diff or diff the rebuilt artifact against one built from the base commit in the same environment, and say in the PR which regions changed and why. Anything else that moved is your merge, not your edit.

Non-deterministic serialization turns every branch into a conflict

If a tracked file is written by iterating a map, set, or dict whose order is not stable, the whole file is rewritten on every run. Then every branch conflicts on every line, diffs are unreviewable, and real conflicts hide inside the churn.

Sort before writing:

python
# BAD — map iteration order; the file is rewritten differently every time
records = [to_record(k, v) for k, v in index.items()]

# GOOD — a stable key. ISO-8601 dates sort chronologically as plain strings,
# so no date parsing is needed to get a deterministic, reviewable file.
records = sorted((to_record(k, v) for k, v in index.items()), key=lambda r: r["date"])

This applies to any code path that rebuilds a committed file from an unordered collection, including the ones added later. Once one path forgets, the file is churny again.

Shared tool config: take the base file, re-apply your delta

pyproject.toml, package.json, lint config, a CI workflow — files where your branch added three lines and four other branches added their own. Reconciling hunks by hand is where dev-dependency pins and tool sections get silently dropped.

Take the integration branch's copy wholesale — it already carries everything that landed while you were away — then re-add only the lines your branch introduced:

bash
git checkout origin/main -- pyproject.toml
# re-add just your delta, then confirm nothing else moved
git diff origin/main -- pyproject.toml

That last git diff should show your addition and nothing else. If it shows a pin reverting or a tool section disappearing, you took a side by accident.

Aggregated test modules: keep both suites, then check for shadowing

Two branches append cases to the same test file. Union them — but a union can produce two tests with the same name, and in Python and JavaScript the later definition simply replaces the earlier one. The file looks longer, the suite count looks plausible, and one branch's case never runs.

bash
grep -oE '^\s*(def test_[A-Za-z0-9_]+|it\(.[^,]*|test\(.[^,]*)' tests/test_thing.py \
  | sort | uniq -d

The same shape bites production code: two branches independently add the same module-level helper, the resolution keeps both, and the second shadows the first.

Append; never renumber a shared identifier

Repos that carry stable identifiers cited from elsewhere — claim IDs in a source ledger, migration numbers, fixture keys, snapshot names — break in a way conflict markers cannot show you. Renumbering C7-C12 to make room is a clean, conflict-free edit that invalidates every citation of those IDs in files your merge never touched.

Add new identifiers by continuing the numbering past the highest one that exists anywhere, and never reuse or renumber one. If two branches both claimed C17, renumber yours and fix your own references; do not renumber the side that already merged.

A clean auto-merge is not verification

Two branches editing far-apart regions of one large file merge cleanly and can still be semantically wrong: one side's change depends on a helper the other removed, or both added equivalent logic under different names, or one side's edit now sits inside a branch the other made unreachable.

After any non-trivial merge, confirm both sides survived — grep the merged tree for a distinctive string from each:

bash
git grep -n "computes the retry budget"     # a phrase only their change added
git grep -n "REDACTION_PLACEHOLDER"         # a symbol only yours added

Present and consistent, not just present. Then read the two regions together.

Match the repo's integration style, and re-run the real gate

bash
git log --graph --oneline -20    # merge commits, or a linear rebased history?

Follow whichever the history already uses. Then run the repo's actual CI commands locally before pushing: a resolved merge is code that has never existed anywhere before, and neither branch's CI run covered it. A green check on your branch and a green check on main say nothing about their union. See reproducing-ci-locally for deriving the commands the runner actually uses.

Checklist

  • Hotspot files identified from git log --name-only before branching
  • Registry/manifest conflicts resolved by union; every entry from both sides kept
  • Registry checked against the filesystem in both directions, in CI
  • Version/counter fields recomputed from origin/main, not taken from either side
  • Generated artifacts rebuilt from merged sources, never text- or binary-merged
  • Generator version pinned so a reviewer can regenerate and diff
  • Files serialized from maps/sets sorted by a stable key
  • Shared tool config taken from base, delta re-applied, git diff shows only your lines
  • Merged test modules checked for duplicate test names
  • New shared identifiers appended; none reused or renumbered
  • A distinctive string from each side found in the merged tree
  • Repo's real CI gate run locally on the merged result before pushing

Note for this repository (ffmpeg-skill)

This is exactly the pattern seen across the PR #28-#37 merge cascade for the 0.10.0 release: CHANGELOG.md was the hotspot file nearly every PR touched, and the correct resolution every time was "union both sides, never choose one" (keep every bullet, verify no leftover conflict markers). package.json's version field is the single-value-counter case: it should be recomputed once, after all merges land, not carried through each individual merge.

Learn More

  • reproducing-ci-locally (also added to this repo's .claude/skills/) — running the runner's real commands on the merged tree

Source: wdm0006/python-skills (MIT).

Frequently asked questions

What does the Concurrent Branches AI skill do?

Resolve conflicts and merges when several branches are open against one repo at the same time — the hotspot files every change must touch (registry manifests, a single version field, shared tool config, one aggregated test module, tracked build output), union-vs-recompute-vs-rebuild as three different correct resolutions, generated artifacts that must never be merged, non-deterministic serialization that makes every branch conflict, renumbered identifiers that git cannot show you, and why a clean auto-merge is not a passing test. Use when rebasing or merging a long-lived branch, resolving c...

Why use Concurrent Branches on TypingMind?

Because you install it once and use it with any model. Concurrent Branches is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Concurrent Branches in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kajisho5/ffmpeg-skill/tree/main/.claude/skills/concurrent-branches. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Concurrent Branches?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Concurrent Branches?

As many as you like. As long as a model supports skills, you can use Concurrent Branches with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Concurrent Branches AI skill free?

Yes. It is published on GitHub by kajisho5 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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