review-implementation

A read-only review of the changes made for a completed task, using separate checks for architecture, unnecessary complexity, and reusable code.

In plain words
What is it for?
Use it after implementation to inspect the task's diff, apply safe fixes when appropriate, and record findings beside the task progress tracker.
Why use it?
It catches real quality problems in the finished changes without turning the review into a general audit of the whole codebase.

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/ayoubben18/ab-method/review-implementation
Any agent
npx skills add ayoubben18/ab-method --skill review-implementation
Clone the repo
git clone --depth 1 https://github.com/ayoubben18/ab-method

Made for: Claude Code, Codex.

Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,389 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 $0.00102 $0.01389
Opus 5 $0.00051 $0.00694
Sonnet 5 $0.00020 $0.00278
Haiku 4.5 $0.00010 $0.00139

Measured 3d ago against content hash 650660f2271a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

review-implementation 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 3d 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/review-implementation/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review Implementation (post-implementation)

Review a completed task's diff through three lenses, each a read-only critic subagent. The counterpart to ../critique-plan/SKILL.md: that guards the plan before coding; this guards the result after.

Silence is the default output. A clean diff produces no findings — that is the normal case. A finding survives only if a senior engineer, looking at this diff, would actually make the change. State each as a concrete cost, not a preference. Never invent refactors to look thorough.

ALWAYS check .ab-method/structure/index.yaml FIRST for paths (task location, domain model, review.md). Review only what this task changed — the cohesive diff across its missions (git diff for the commit range). This is not a whole-codebase audit; that's /improve-codebase-architecture.

Process

1. Gather diff + context

The changed files and their git diff, plus (read only what exists): UBIQUITOUS_LANGUAGE.md / CONTEXT.md (canonical terms, spotting reinvented concepts), docs/architecture/* (the documented way things are built here), docs/adr/ (don't propose what an ADR settled), and the task's unresolved-questions.md if it has one.

Black boxes are deliberate — seed every critic with the file. A TODO(UQ-n) seam whose entry is recorded there is a decision the user signed off on: ship a placeholder rather than guess. No lens may flag it as slop, a shallow module, a speculative abstraction, or dead code, and none may propose "just implement it properly" — the answer isn't theirs to pick. Two things are fair game and should be reported: a TODO(UQ-n) marker with no matching entry (an orphan black box nobody recorded), and a placeholder that leaked past its named seam into several call sites — the seam was supposed to contain it, and containing it again is a safe fix.

2. Spin up THREE read-only critics — in parallel

Spawn three subagents in one batch, named exactly:

Read the full file on GitHub · 100 lines

Files

What ships with it

3 files 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. 3d ago First seen · 100 lines · 102 tokens per session scan A 650660f2271a

Subscribe to this mod's changes

review-implementation is a skill published in the GitHub repository ayoubben18/ab-method (187 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 1,389 once invoked, about $0.0005 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

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 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