RAD_framework: Skill for Claude Code

.agents/skills/quality-review/SKILL.md

quality-review is a skill for Claude Code, Codex from seanrreid/RAD_framework. It costs 55 tokens per session (345 once invoked), scanned A, original, MIT.

A read-only code review skill that sends changed files to a quality reviewer. The review checks security, error handling, missing-value handling, input checks, naming, and project instructions such as AGENTS.md.

In plain words
What is it for?
Reviewing all files changed from the default Git branch or a specified list of files, then reporting the reviewer's findings.
Why use it?
It can expose common implementation risks and convention violations before code is merged, without changing the files under review.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions CLAUDE.md; mentions subagents; reads ~/.codex paths.

This is seanrreid/RAD_framework's own configuration. It tells Claude Code and Codex how to work on RAD_framework itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything RAD_framework configures →

Reuse

Borrowing it

Nothing to install: this file belongs to seanrreid/RAD_framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/seanrreid/RAD_framework/main/.agents/skills/quality-review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/seanrreid/RAD_framework

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 quality-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/seanrreid/rad_framework/quality-review/github.svg)](https://agentmods.dev/skills/seanrreid/rad_framework/quality-review)
Your own site
<a href="https://agentmods.dev/skills/seanrreid/rad_framework/quality-review"><img src="https://agentmods.dev/badge/skills/seanrreid/rad_framework/quality-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for quality-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/seanrreid/rad_framework/quality-review"><img src="https://agentmods.dev/badge/skills/seanrreid/rad_framework/quality-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 345 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00055 $0.00345
Opus 5.5 $0.00022 $0.00138
Sonnet 5.5 $0.00011 $0.00069
Haiku 4.5 $0.00006 $0.00034

Measured 23d ago against content hash e23541e29013, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

quality-review 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 23d 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.

.agents/skills/quality-review/SKILL.md · 39 lines

What it actually says

quality-review

Run a read-only review of security, error handling, null safety, input validation, naming, and project conventions.

Input

Read the text the user wrote after the skill name (optional):

  • Empty: review every file changed since branching from the default branch.
  • File paths: review only those files.

Resolve the default branch and the changed files with:

BASE=$(scripts/get-default-branch.sh)
git diff "$BASE"...HEAD --name-only

If scripts/get-default-branch.sh exits non-zero, report its stderr and stop.

Process

  1. Build the file list: the files the user named, or the changed files above.
  2. Spawn the quality-reviewer sub-agent, defined at .codex/agents/quality-reviewer.toml (it runs with sandbox_mode = "read-only"). Give it the file list and the default branch as context.
  3. Wait for it to finish and report its findings report as written.

Rules

  • Never edit, create, or delete files. Never commit or push.
  • If the sub-agent cannot be spawned, say so and stop; do not review the files yourself in its place.
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. 23d ago First seen · 39 lines · 55 tokens per session scan A e23541e29013

Subscribe to this mod's changes

quality-review is a skill published in the GitHub repository seanrreid/RAD_framework (5 stars, last pushed 4d ago), licensed MIT. It adds 55 tokens to every session and 345 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-06.

Related

Other skills, from other repositories

autoreview

Structured code review through the shared OpenClaw agent-skills installation.

openclaw/clawhub · 17 tokens

omh-code-review

This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

rlaope/oh-my-hermes · 48 tokens

revdiff-plan

Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…

umputun/revdiff · 84 tokens

explore-unknowns

Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…

dzhng/skills · 162 tokens

re0-merge

Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…

LilMGenius/paperthin · 70 tokens

asplos-writing-style

Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…

brycewang-stanford/Awesome-Journal-Skills · 65 tokens