evaluate-local-commits-via-cherry-pick-dry-run

Technique to identify which ahead commits contain real changes vs. already-merged or ephemeral content

5 stars

Best use case

evaluate-local-commits-via-cherry-pick-dry-run is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Technique to identify which ahead commits contain real changes vs. already-merged or ephemeral content

Teams using evaluate-local-commits-via-cherry-pick-dry-run should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/evaluate-local-commits-via-cherry-pick-dry-run/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/workspace-hub/learned/evaluate-local-commits-via-cherry-pick-dry-run/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/evaluate-local-commits-via-cherry-pick-dry-run/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How evaluate-local-commits-via-cherry-pick-dry-run Compares

Feature / Agentevaluate-local-commits-via-cherry-pick-dry-runStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Technique to identify which ahead commits contain real changes vs. already-merged or ephemeral content

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Evaluate Local-Only Commits via Cherry-Pick Dry-Run

When you have ahead commits on a local branch relative to origin, use `git cherry-pick --dry-run` against each commit to classify them: exit 0 with no changes = already in origin; exit 0 with staged changes = real delta; conflicts = real delta needing sequencing. This lets you quickly filter ephemeral commits (like daily regenerations or auto-syncs) from substantive changes worth preserving or upstreaming.

## Follow-on triage when upstream already landed similar work

If the dry-run or a real cherry-pick attempt shows the work is already effectively upstream:

1. Do not assume the local branch is worthless or safe to cherry-pick wholesale.
2. Preserve the branch as forensic/reference evidence until compared.
3. Compare only the high-signal files against `origin/main`, for example:
   - regression tests
   - canonical skill files touched by the work
   - any review artifacts explaining what changed
4. Separate the local-only delta into:
   - clean, reusable learnings worth salvaging
   - unrelated drift / contamination from the duplicate execution context
5. If the branch contains mixed signal + noise, prefer creating a focused follow-up GitHub issue describing the salvage candidates instead of cherry-picking the whole branch.
6. In that issue, explicitly capture:
   - what landed upstream already
   - what extra local-only checks/content appear useful
   - why direct cherry-pick is unsafe
   - the narrow acceptance criteria for selective salvage

### Example reusable pattern

This was useful when a duplicate implementation branch for an already-landed skills dedup issue contained:
- a stronger regression test with broader dangling-reference surface checks
- a much larger alternate skill draft with possible overlap/noise

The right move was:
- keep upstream as authoritative
- preserve the duplicate branch for reference
- inspect targeted file deltas only
- create a narrow follow-up issue for selective salvage rather than replaying the branch

## Pitfall

A preserved duplicate branch often contains unrelated edits from the worktree/session. Treat it as a source of candidate learnings, not as a merge-ready patch set.

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