AgentSkillsCN

review

基于边缘AST的智能上下文代码审查。适用于代码变更审查、差异对比或代码质量分析。

SKILL.md
--- frontmatter
name: review
description: Selvage AST-based smart context code review. Use when reviewing code changes, checking diffs, or analyzing code quality.
context: fork
agent: selvage-reviewer
allowed-tools:
  - Read
  - Glob
  - Grep
argument-hint: "(e.g., 'staged', 'branch main', 'commit abc1234')"

Selvage Code Review Skill

Argument Parsing

Parse $ARGUMENTS to determine the diff scope:

Inputdiff_scopetarget_branchtarget_commit
(empty)unstaged--
stagedstaged--
branch mainbranchmain-
branch developbranchdevelop-
commit abc1234commit-abc1234

If $ARGUMENTS does not match any pattern above, default to unstaged diff_scope.

Workflow

Step 1: Get Review Context

Call mcp__selvage__get_review_context with the parsed parameters.

Inspect the response and branch on the result:

Case A - Inline context (no context_id in response):

  • The review_targets field contains all file contexts inline.
  • Proceed directly to Step 2 with the full context.

Case B - Split context (context_id is present in response):

  • The context was too large to return inline.
  • The response includes context_id and file_list (list of file paths).
  • For each file in file_list, call mcp__selvage__get_file_review_context with the context_id and file_path.
  • Call these in parallel where possible to minimize latency.
  • Collect all returned review_target results, then proceed to Step 2.

Step 2: Perform Review

Using the system_prompt from Step 1 as your review guidelines:

  1. Follow the system_prompt instructions exactly - it contains the review criteria and context interpretation rules.
  2. Review each file in the collected review targets:
    • Analyze hunks (changed code sections) for bugs, security issues, performance problems, and design concerns.
    • Pay attention to context_type in each target:
      • SMART_CONTEXT: AST-analyzed related code blocks - use these to understand the broader code structure around changes.
      • FALLBACK_CONTEXT: Text-based pattern matches - still useful but less precise than AST analysis.
      • FULL_CONTEXT: Complete file content - typically for new or heavily rewritten files.
  3. Verification Principle: When you identify a potential issue but are not certain, verify it before reporting:
    • Use Read to examine the actual source file for additional context.
    • Use Grep to search for related patterns, usages, or definitions across the codebase.
    • Use Glob to find related files if needed.
    • Only report issues you are confident about after verification.

Step 3: Report Results

Present the review as free-form text (not JSON). Structure your response as:

  1. Summary: Brief overview of the changes and overall quality assessment (2-3 sentences).
  2. Issues Found: List each issue with:
    • Severity indicator: [error], [warning], or [info]
    • Category: bug, security, performance, style, or design
    • File and location reference
    • Clear description of the problem
    • Concrete suggestion for how to fix it
    • Code snippet if helpful
  3. Score: Overall code quality score from 0-10.
  4. Recommendations: Actionable next steps for the developer.

If no issues are found, state that the changes look good and provide a brief positive summary.