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GeneralParslee-ai

neo-review

Get Neo's code review with semantic pattern matching. Focuses on security vulnerabilities, edge cases, error handling, and performance issues across the target file or module.

Stars
12
Source
Parslee-ai/neo
Updated
2026-05-26
Slug
Parslee-ai--neo--neo-review
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/Parslee-ai/neo/HEAD/plugins/neo/skills/neo-review/SKILL.md -o .claude/skills/neo-review.md

Drops the SKILL.md into .claude/skills/neo-review.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

Neo Code Review

When the user invokes this skill ($neo-review <file or module>), do the following:

  1. Identify the target. It will usually be a file path (e.g. src/api/handlers.py), a module name, or a free-form description ("the payment processing code"). Use Read/Grep/Glob to resolve it to concrete file(s).

  2. Read the relevant code. Up to 5 files at a time keeps Neo's context budget healthy. Prefer the files where the actual logic lives over generated/test files.

  3. Invoke Neo with a review-framed prompt. Allow up to 5 minutes.

    neo <<'QUERY'
    Review the following code for: security vulnerabilities, edge cases, error handling, performance issues. Provide concrete suggestions with confidence scores.
    
    <paste relevant code or summarize what you read>
    QUERY
    
  4. Filter Neo's output to review-relevant findings. Group by severity. Flag any finding with confidence ≥ 0.8 as actionable; treat lower-confidence findings as worth-checking-but-verify.

  5. Cross-reference with Neo's KNOWN ISSUES IN NEARBY CODE section if present. Neo's context-assembly already surfaces TODOs, stubs, swallowed exceptions, hardcoded credentials — those overlap with review concerns and add weight to related findings.

Notes

  • Neo's confidence scores reflect both LLM self-assessment and pattern-match strength against past reviews in semantic memory.
  • For security-critical code, escalate findings the user pushes back on — Neo's memory is updated with outcomes, so consistent rejections will demote weak patterns over time.