evaluate-local-commits-via-cherry-pick-dry-run
Technique to identify which ahead commits contain real changes vs. already-merged or ephemeral content
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/evaluate-local-commits-via-cherry-pick-dry-run/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How evaluate-local-commits-via-cherry-pick-dry-run Compares
| Feature / Agent | evaluate-local-commits-via-cherry-pick-dry-run | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.
Related Skills
gtm-site-readiness-audit-local-vs-production
Audit GTM feature work by separating local artifact readiness from production deployment state, then fix common blockers in aceengineer-website and GTM collateral.
artifact-inline-plan-review-for-local-draft-revisions
Prevent false MAJOR plan-review findings when Codex/Gemini review stale remote/main artifacts instead of the revised local draft.
artifact-inline-local-plan-rereview
Prevent stale Codex/Gemini findings by rerunning plan review against the exact revised local artifact inline when summary prompts keep anchoring on remote/main plan content.
re-review-local-plan-artifact-grounding
Prevent stale adversarial re-reviews by forcing Codex/Gemini to review the exact revised local plan artifact instead of stale GitHub issue text or default-branch content.
hermes-local-configuration
Class-level Hermes local configuration and setup workflows, including config audit gotchas and Windows installation.
test-oversized-skill
A test fixture skill that exceeds 200 lines with multiple H2/H3 sections for split testing.
interactive-report-generator
Generate interactive HTML reports with Plotly visualizations from data analysis results. Supports dashboards, charts, and professional styling.
data-validation-reporter
Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.
agent-os-framework
Generate standardized .agent-os directory structure with product documentation, mission, tech-stack, roadmap, and decision records. Enables AI-native workflows.
OrcaFlex Specialist Skill
```yaml
repo-ecosystem-hygiene
Interpret the daily read-only repo ecosystem hygiene audit and route remediation through approved workflows.
domain-knowledge-sweep
Systematic multi-source research of an engineering domain. Spawns parent issue → 6 research subissues (Standards, Academic, Industry, LinkedIn-marketing, Code-audit, Synthesis) → gap implementation subissues. Replaces LinkedIn-only extraction with defensible comprehensive sourcing.