rr

rr is a skill for Claude Code, Codex from dgk-dev/dgk-gpt. It costs 51 tokens per session (643 once invoked), scanned A, original, MIT.

A code-review workflow that sends changes to a Z.AI model for a second review, then checks the reported issues against the actual code.

In plain words
What is it for?
Use it to review staged changes, a commit, a pull request, or a focused set of files.
Why use it?
It adds another review of the change while filtering out findings that do not hold up when checked directly.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/dgk-dev/dgk-gpt/rr
Any agent
npx skills add dgk-dev/dgk-gpt --skill rr
Clone the repo
git clone --depth 1 https://github.com/dgk-dev/dgk-gpt

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for rr

README.md
[![agentmods](https://agentmods.dev/badge/skills/dgk-dev/dgk-gpt/rr.svg)](https://agentmods.dev/skills/dgk-dev/dgk-gpt/rr)
Your own site
<a href="https://agentmods.dev/skills/dgk-dev/dgk-gpt/rr"><img src="https://agentmods.dev/badge/skills/dgk-dev/dgk-gpt/rr.svg" alt="Measured on agentmods" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 643 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00051 $0.00643
Opus 5 $0.00026 $0.00321
Sonnet 5 $0.00010 $0.00129
Haiku 4.5 $0.00005 $0.00064

Measured 5d ago against content hash c56a7d1b2248, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

rr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/rr/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/rr

Use glm-review as a second reviewer for the current change set, then validate the reported issues against the actual code before you trust or repeat them.

Default Flow

  1. Determine the exact change scope first.
  • If the user points to a commit, prefer commit mode.
  • If the user says staged, use staged mode.
  • If the user says pr, use PR mode.
  • If the workspace has mixed unrelated changes, build a focused diff file for only the intended files.
  1. Run a quick connectivity check only when the environment looks suspect:
glm-review --health
  1. Run the review with the narrowest correct input:
glm-review
glm-review --mode staged
glm-review --mode pr
glm-review --mode commit --ref <COMMIT_HASH>
glm-review --diff-file /tmp/glm-review-diff.patch
  1. Treat the output as a candidate issue list, not ground truth.
  2. Re-open the referenced code and verify each claim.
  3. Report only valid issues, ordered by severity.
  4. If fixing issues is in scope, fix them and rerun the closest relevant verification.

Choosing Review Input

Prefer the most specific path that isolates the current task:

  • committed single change:
glm-review --mode commit --ref <COMMIT_HASH>
  • committed subset of files:
glm-review --mode commit --ref <COMMIT_HASH> --files src/a.ts src/b.ts
  • custom focused diff for multi-session or mixed worktrees:
GIT_ROOT=$(git rev-parse --show-toplevel)
cd "$GIT_ROOT" && git diff HEAD -- <file1> <file2> ... > /tmp/glm-review-diff.patch
glm-review --diff-file /tmp/glm-review-diff.patch

If the diff is empty, stop and say there is nothing to review.

Validation Rules

  • Do not parrot glm-review output without checking the code.
  • Drop false positives explicitly instead of forwarding them.
  • Distinguish between confirmed bugs, arguable style comments, and already-fixed issues.
  • If the review claims a regression, inspect the relevant file and the actual diff before accepting it.

Error Handling

Read the full file on GitHub · 90 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 90 lines · 51 tokens per session scan A c56a7d1b2248

Subscribe to this mod's changes

rr is a skill published in the GitHub repository dgk-dev/dgk-gpt (53 stars, last pushed 5mo ago), licensed MIT. It adds 51 tokens to every session and 643 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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