quality-update

A guided workflow for applying code-quality improvements listed in docs/quality_review.md. It makes one logical change at a time, tests it, and records each change in a separate commit.

In plain words
What is it for?
Use it to apply quick wins and higher-priority quality fixes, add tests when needed, and track completed improvements in the review document.
Why use it?
It turns a long review document into a manageable sequence of fixes. Running tests after each change helps reveal problems early.

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/codenamev/claude_memory/quality-update
Any agent
npx skills add codenamev/claude_memory --skill quality-update
Clone the repo
git clone --depth 1 https://github.com/codenamev/claude_memory

Made for: Claude Code, Codex.

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,664 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 $0.00031 $0.01664
Opus 5 $0.00015 $0.00832
Sonnet 5 $0.00006 $0.00333
Haiku 4.5 $0.00003 $0.00166

Measured 2d ago against content hash 7b32af4fc03b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

quality-update 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 2d 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.

.claude/skills/quality-update/SKILL.md · 230 lines

How it starts

The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Quality Update - Incremental Implementation

Systematically implement code quality improvements from the review document, making tested, atomic commits for each fix.

Process Overview

  1. Read the quality review from docs/quality_review.md
  2. Prioritize improvements (start with Quick Wins, then High Priority)
  3. Implement fixes incrementally (one logical change at a time)
  4. Run tests after each change to ensure nothing breaks
  5. Make atomic commits that capture the change and its purpose
  6. Update review document to track progress

Detailed Steps

Step 1: Read and Parse Review

# Read the current quality review
Read docs/quality_review.md

Focus on these sections in priority order:

  1. Appendix B: Quick Wins - Start here (fast, low risk)
  2. High Priority (This Week) - Critical improvements
  3. Medium Priority (Next Week) - Important but not urgent
  4. Skip Low Priority items for now

Step 2: Select Next Improvement

Choose improvements based on:

  • Risk: Low risk first (refactoring, style fixes)
  • Dependencies: Prerequisites before dependent work
  • Atomicity: Each commit should be one logical change
  • Test coverage: Ensure tests exist or add them

Step 3: Implement the Fix

For each improvement:

  1. Read affected files to understand current state
  2. Make the change using Edit or Write
  3. Run linter to ensure style compliance:
    bundle exec rake standard:fix
    
  4. Run tests to verify correctness:
    bundle exec rspec
    
  5. Fix any test failures before proceeding

Step 4: Make Atomic Commit

Commit Message Format:

[Quality] Brief description of what was fixed

- Specific change made (e.g., "Extract DatabaseCheck from DoctorCommand")
- Why this improves quality (e.g., "Improves SRP and testability")
- Expert principle applied (e.g., "Sandi Metz: Single Responsibility")

Addresses: docs/quality_review.md [section reference]

Read the full file on GitHub · 230 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 2d ago First seen · 230 lines · 31 tokens per session scan A 7b32af4fc03b

Subscribe to this mod's changes

quality-update is a skill published in the GitHub repository codenamev/claude_memory (24 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 1,664 once invoked, about $0.0002 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens