local-ci-code-review — for Claude Code local-ci-code-review, skill-system-foundry, community, for Claude Code, ide skills, codex-ai-code-review-action, copilot-instructions.md, markdown.instructions.md, scripts.instructions.md, reasoning

v1.0.0

このスキルについて

適した場面: Ideal for AI agents that need local ci code review skill. ローカライズされた概要: Meta-skill for building AI-agnostic skill systems with a two-layer architecture of skills and roles, templates, validation tools, and cross-platform authoring guidance based on the Agent Skills specification. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

機能

Local CI Code Review Skill
Step 1: Identify the Changes
Determine the diff to review:
Changes on current branch vs main (default)
git diff main...HEAD

# Core Topics

milanhorvatovic milanhorvatovic
[1]
[1]
Updated: 4/7/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 10/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
10/11
Quality Score
70
Canonical Locale
en
Detected Body Locale
en

適した場面: Ideal for AI agents that need local ci code review skill. ローカライズされた概要: Meta-skill for building AI-agnostic skill systems with a two-layer architecture of skills and roles, templates, validation tools, and cross-platform authoring guidance based on the Agent Skills specification. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

このスキルを使用する理由

推奨ポイント: local-ci-code-review helps agents local ci code review skill. Meta-skill for building AI-agnostic skill systems with a two-layer architecture of skills and roles, templates, validation tools, and cross-platform

おすすめ

適した場面: Ideal for AI agents that need local ci code review skill.

実現可能なユースケース for local-ci-code-review

ユースケース: Applying Local CI Code Review Skill
ユースケース: Applying Step 1: Identify the Changes
ユースケース: Applying Determine the diff to review:

! セキュリティと制限

  • 制約事項: Requires repository-specific context from the skill documentation
  • 制約事項: Works best when the underlying tools and dependencies are already configured

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is local-ci-code-review?

適した場面: Ideal for AI agents that need local ci code review skill. ローカライズされた概要: Meta-skill for building AI-agnostic skill systems with a two-layer architecture of skills and roles, templates, validation tools, and cross-platform authoring guidance based on the Agent Skills specification. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

How do I install local-ci-code-review?

Run the command: npx killer-skills add milanhorvatovic/skill-system-foundry/local-ci-code-review. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for local-ci-code-review?

Key use cases include: ユースケース: Applying Local CI Code Review Skill, ユースケース: Applying Step 1: Identify the Changes, ユースケース: Applying Determine the diff to review:.

Which IDEs are compatible with local-ci-code-review?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for local-ci-code-review?

制約事項: Requires repository-specific context from the skill documentation. 制約事項: Works best when the underlying tools and dependencies are already configured.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add milanhorvatovic/skill-system-foundry/local-ci-code-review. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use local-ci-code-review immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

local-ci-code-review

Install local-ci-code-review, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Local CI Code Review Skill

Runs a deep structured code review locally, applying the same methodology used by the codex-ai-code-review-action CI pipeline. Uses repository-specific guidance from .github/codex/review-reference.md and the review methodology defined below.

Step 1: Identify the Changes

Determine the diff to review:

bash
1# Changes on current branch vs main (default) 2git diff main...HEAD 3 4# Uncommitted changes (if no branch commits yet) 5git diff HEAD 6 7# Specific commit range (if provided) 8git diff <base>..<head>

If no scope is specified, default to main...HEAD.

List the changed files and note their types — file types determine which checklists apply in Step 3.

Step 2: Load Review Context

Read the repository-specific reference material and file-type instruction rules:

  • .github/codex/review-reference.md — repository-specific review guidance (conventions, Python script rules, workflow rules)
  • .github/copilot-instructions.md — Agent Skills format compliance, repository constraints, automated validation coverage, review focus areas
  • .github/instructions/markdown.instructions.md — documentation quality rules, description quality, progressive disclosure, file reference conventions
  • .github/instructions/scripts.instructions.md — Python script conventions, stdlib-only constraint, type hints, error handling, code organization rules

The review reference file provides repository-specific conventions — apply them alongside the methodology defined in this skill. The instruction files provide supplementary file-type-specific rules: apply copilot-instructions.md to all files, markdown.instructions.md when the diff includes **/*.md, and scripts.instructions.md when it includes skill-system-foundry/scripts/**/*.py.

Step 3: Review the Diff

Apply the review methodology from the loaded context. For each changed file:

  1. Identify the file type and select the matching checklist from the reference material (Python, Shell, Markdown, Workflow YAML).
  2. Trace data flow — follow values from input through parsing, transformation, and use.
  3. Check execution order — verify validation happens before use.
  4. Verify edge cases — empty arrays, zero, negatives, boundaries, missing optional fields.
  5. Connect schema to runtime — check if documented contracts are enforced.
  6. Check error propagation — verify callers handle failures.

For each finding, write the reasoning first, then assign priority and confidence.

Priority levels

PriorityScopeExamples
P0Critical bugs, security vulnerabilitiesData loss, injection, auth bypass, crash in mainline path
P1Correctness and robustnessOff-by-one, unhandled error path, race condition
P2Maintainability and styleMisleading name, duplicated logic, missing type hint
P3Minor improvementsWhitespace, comment wording, optional simplification

Confidence scoring

RangeMeaning
0.9–1.0Certain — code is demonstrably wrong or violates documented rule
0.7–0.9High — very likely based on context, depends on intent
0.5–0.7Moderate — plausible, could be intentional
0.3–0.5Low — possible concern, may be false positive
< 0.3Speculative — flag only if severe impact

Rules

  • Flag only issues introduced by the diff. Do not flag pre-existing problems.
  • Include findings at all priority levels — do not suppress low-priority findings.
  • Do not flag known limitations listed in the reference material.
  • Read the full source file when diff context is insufficient.

Step 4: Run the Self-Review Checklist

Before producing output, verify:

  1. Every changed file in the diff has been examined.
  2. The relevant file-type checklist was applied to each file.
  3. Data flow was traced for any new parsing, transformation, or validation logic.
  4. Edge cases were checked for new conditional branches or numeric conversions.
  5. If zero findings, each file's clean status can be explained.

Step 5: Report Findings

Produce a structured review matching the CI pipeline output format.

Output format

## Summary
[1-5 sentence description of what the changes do and why]

## Changes
- [Short bullet describing each logical change]

## Files
| File | Description |
|---|---|
| path/to/file | Short description of what changed |

## Findings

### P0 — Critical
[findings or "None"]

### P1 — Correctness
[findings or "None"]

### P2 — Maintainability
[findings or "None"]

### P3 — Minor
[findings or "None"]

## Verdict
**[patch is correct | patch is incorrect]** (confidence: X.XX)
[One-sentence rationale]

Finding format

For each finding:

[P{n}] {title}{path}:{line} (confidence: {score})

{body}

<details> <summary>Reasoning</summary> {reasoning — what was observed, why it is a problem, concrete impact} </details>
suggestion
1{exact replacement code, or omit block if null}

Metadata

After the verdict, append:

---
Findings: {total} ({skipped} below confidence threshold)
Model: {self-reported model identifier}
Review scope: {diff range used}

Rules

  • Empty findings is a valid outcome — a clean diff is not a failure to review.
  • Do not manufacture findings to justify the review.
  • Do not suggest alternative implementations unless the current one is clearly wrong.
  • Do not comment on style preferences without a documented convention backing them.

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