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juliusbrussee/caveman-review

juliusbrussee

caveman-review

Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.

v1.0Latest
New~661Updated Aug 17, 2026

Write code review comments terse and actionable. One line per finding. Location, problem, fix. No throat-clearing.

Rules

Format: L<line>: <problem>. <fix>. — or <file>:L<line>: ... when reviewing multi-file diffs.

Severity prefix (optional, when mixed):

  • 🔴 bug: — broken behavior, will cause incident
  • 🟡 risk: — works but fragile (race, missing null check, swallowed error)
  • 🔵 nit: — style, naming, micro-optim. Author can ignore
  • ❓ q: — genuine question, not a suggestion

Drop:

  • "I noticed that...", "It seems like...", "You might want to consider..."
  • "This is just a suggestion but..." — use nit: instead
  • "Great work!", "Looks good overall but..." — say it once at the top, not per comment
  • Restating what the line does — the reviewer can read the diff
  • Hedging ("perhaps", "maybe", "I think") — if unsure use q:

Keep:

  • Exact line numbers
  • Exact symbol/function/variable names in backticks
  • Concrete fix, not "consider refactoring this"
  • The why if the fix isn't obvious from the problem statement

Examples

❌ "I noticed that on line 42 you're not checking if the user object is null before accessing the email property. This could potentially cause a crash if the user is not found in the database. You might want to add a null check here."

L42: 🔴 bug: user can be null after .find(). Add guard before .email.

❌ "It looks like this function is doing a lot of things and might benefit from being broken up into smaller functions for readability."

L88-140: 🔵 nit: 50-line fn does 4 things. Extract validate/normalize/persist.

❌ "Have you considered what happens if the API returns a 429? I think we should probably handle that case."

L23: 🟡 risk: no retry on 429. Wrap in withBackoff(3).

Auto-Clarity

Drop terse mode for: security findings (CVE-class bugs need full explanation + reference), architectural disagreements (need rationale, not just a one-liner), and onboarding contexts where the author is new and needs the "why". In those cases write a normal paragraph, then resume terse for the rest.

Boundaries

Reviews only — does not write the code fix, does not approve/request-changes, does not run linters. Output the comment(s) ready to paste into the PR. "stop caveman-review" or "normal mode": revert to verbose review style.

Files2
2 files · 2.6 KB

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Overall Score

83/100

Grade

B

Good

Safety

92

Quality

82

Clarity

86

Completeness

76

Summary

This skill teaches an AI agent to generate ultra-concise, actionable code review comments in a standardized format (location, severity, problem, fix) while avoiding verbose hedging and noise. It does not write code, approve PRs, or run linters—only produces comment text ready to paste into pull requests.

Detected Capabilities

code analysis and pattern detectionpull request diff readingstructured text generationread-only operation (no file writes, no command execution)

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

review this prcode reviewcaveman reviewterse feedbackpr comments

Use Cases

  • Generate terse PR review comments to reduce feedback noise
  • Flag bugs, risks, and style issues with line numbers and concrete fixes
  • Auto-escalate to verbose mode for security findings or architectural disagreements
  • Provide peer reviews that include exact problem location and actionable remediation
  • Use severity emoji (🔴🟡🔵❓) to quickly surface bug-class vs. style findings

Quality Notes

  • Clear scope boundaries — skill explicitly documents what it does NOT do (approve, request changes, run linters)
  • Well-structured rules with concrete examples contrasting bad (verbose) vs. good (terse) feedback
  • Smart escalation rule (auto-clarity) for security findings and architectural contexts shows domain awareness
  • Example output is concrete and immediately actionable
  • Trigger keywords are well-defined and discoverable (slash command, natural language phrases)
  • README reinforces skill purpose and invocation patterns
  • One minor gap: no error handling documented for edge cases like binary files, images, or auto-generated code
Model: claude-haiku-4-5-20251001Analyzed: Aug 17, 2026

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