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
GitHub

À propos de ce Skill

Scenario recommande : Ideal for AI agents that need local ci code review skill. Resume localise : 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.

Fonctionnalités

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

# Sujets clés

for Claude Code codex-ai-code-review-action copilot-instructions.md markdown.instructions.md scripts.instructions.md reasoning
milanhorvatovic milanhorvatovic
[1]
[1]
Mis à jour: 4/7/2026

Skill Overview

Start with fit, limitations, and setup before diving into the repository.

Scenario recommande : Ideal for AI agents that need local ci code review skill. Resume localise : 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.

Pourquoi utiliser cette compétence

Recommandation : 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

Meilleur pour

Scenario recommande : Ideal for AI agents that need local ci code review skill.

↓ Cas d'utilisation exploitables for local-ci-code-review

Cas d'usage : Applying Local CI Code Review Skill
Cas d'usage : Applying Step 1: Identify the Changes
Cas d'usage : Applying Determine the diff to review:

! Sécurité et Limitations

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

About The Source

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Démo Labs

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 et étapes d’installation

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

? Questions fréquentes

Qu’est-ce que local-ci-code-review ?

Scenario recommande : Ideal for AI agents that need local ci code review skill. Resume localise : 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.

Comment installer local-ci-code-review ?

Exécutez la commande : npx killer-skills add milanhorvatovic/skill-system-foundry. Elle fonctionne avec Cursor, Windsurf, VS Code, Claude Code et plus de 19 autres IDE.

Quels sont les cas d’usage de local-ci-code-review ?

Les principaux cas d’usage incluent : Cas d'usage : Applying Local CI Code Review Skill, Cas d'usage : Applying Step 1: Identify the Changes, Cas d'usage : Applying Determine the diff to review:.

Quels IDE sont compatibles avec local-ci-code-review ?

Cette skill est compatible avec 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. Utilisez la CLI Killer-Skills pour une installation unifiée.

Y a-t-il des limites pour local-ci-code-review ?

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

↓ Comment installer ce skill

  1. 1. Ouvrir le terminal

    Ouvrez le terminal ou la ligne de commande dans le dossier du projet.

  2. 2. Lancer la commande d’installation

    Exécutez : npx killer-skills add milanhorvatovic/skill-system-foundry. La CLI détectera automatiquement votre IDE ou votre agent et configurera la skill.

  3. 3. Commencer à utiliser le skill

    Le skill est maintenant actif. Votre agent IA peut utiliser local-ci-code-review immédiatement dans le projet.

! Source Notes

This page is still useful for installation and source reference. Before using it, compare the fit, limitations, and upstream repository notes above.

Upstream Repository Material

The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.

Upstream Source

local-ci-code-review

Install local-ci-code-review, an AI agent skill for AI agent workflows and automation. Explore features, use cases, limitations, and setup guidance.

SKILL.md
Readonly
Upstream Repository Material
The section below comes from the upstream repository. Use it as supporting material alongside the fit, use-case, and installation summary on this page.
Upstream Source

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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