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/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.

From plugin
stevesolun-ctx
56916 skills3 agents2 hooks
Install
$ npx -y skills add stevesolun/ctx --skill caveman-review --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/caveman-review

Context preview

The summary Claude sees to decide when to auto-load this skill.

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.

SKILL.md

caveman-review.SKILL.md
name: caveman-review
description: >
  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.

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.

Read more
Ships withstevesolun-ctx

ctx is a Python CLI and library that recommends a small, relevant set of skills, agents, and MCP servers for a repository or task. It can use your organization's local/private knowledge or the shipped graph.

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