local-code-review
Review local code changes for correctness, regressions, missing tests, and Databao-specific risks.
Best use case
local-code-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Review local code changes for correctness, regressions, missing tests, and Databao-specific risks.
Teams using local-code-review 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/local-code-review/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How local-code-review Compares
| Feature / Agent | local-code-review | 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?
Review local code changes for correctness, regressions, missing tests, and Databao-specific risks.
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.
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SKILL.md Source
# Local Code Review You are reviewing code changes for Databao CLI with NO prior context. ## Scope Review scope: $ARGUMENTS (default: `branch`) - `staged` -- `git diff --cached` - `branch` -- diff against main - `files:<path>` -- specific files/directories ## Steps ### 1. Discover changes Run `git status --short`, then inspect the relevant diff. Read diffs before reading large surrounding files. ### 2. Review 1. Read the diff carefully. 2. Read surrounding implementation for changed logic. 3. Check related tests -- identify where tests should have changed but did not. #### Databao-specific priorities Pay extra attention when changes touch: - CLI/API/UI behavior (defaults, help text, argument handling, output) - Agent/executor/model-provider wiring (provider defaults, tool contracts) - MCP/plugin/integration boundaries - Config/build/init flows, datasource/schema/context logic - Packaging/deps/lockfile (extras, entrypoints, transitive impact) - Test coverage for changed behavior #### Validation Run non-mutating checks only: - `uv run pytest <targeted paths>` or full suite if practical - `uv run ruff check <paths>`, `uv run ruff format --check <paths>` - `uv run mypy <paths>`, `uv lock --check` Never run `--fix` or mutating formatters in review mode. ### 3. Report findings Order by severity. Each finding: - Severity label + concise title - Why it matters - File and line reference - Remediation direction (brief) Then: open questions, testing gaps, validation performed. No findings? Say so, mention residual risk or untested surface. ## Guardrails - Short code snippets for fixes, not full rewrites. - Do not bury findings under summaries. - Do not claim tests passed unless you ran them. - Prefer evidence from the diff over style opinions.
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