dev-review

A final review guide for software changes before they are pushed. It checks the implementation against feature plans, design documents, callers, tests, and integration risks.

In plain words
What is it for?
Use it for a holistic pre-push review, especially during the final phase of a development lifecycle, with findings tied to specific files and lines.
Why use it?
It helps catch mismatches and release risks that may be missed when reviewing individual files in isolation.

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/codeaholicguy/ai-devkit/dev-review
Any agent
npx skills add codeaholicguy/ai-devkit --skill dev-review
Clone the repo
git clone --depth 1 https://github.com/codeaholicguy/ai-devkit

Made for: Claude Code, Codex.

Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 700 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 $0.00047 $0.00700
Opus 5 $0.00023 $0.00350
Sonnet 5 $0.00009 $0.00140
Haiku 4.5 $0.00005 $0.00070

Measured yesterday against content hash 367ce32411f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dev-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 yesterday.

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/dev-review/SKILL.md · 42 lines

How it starts

The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dev Review

Run final pre-push review for configured AI docs features. Before changing docs or code, propose the concrete review plan and wait for user approval unless the user already approved the exact phase plan.

Phase Contract

  1. Run npx ai-devkit@latest lint before phase work.
  2. If working on a named feature, run npx ai-devkit@latest lint --feature <name>.
  3. Check git status -sb and git diff --stat.
  4. Read feature docs and relevant changed files before findings.
  5. Apply the verify skill before claiming readiness.
  6. If parent dev-lifecycle established usable task tracing, emit review phase, progress, blocker/finding, next-step, and final evidence/readiness events per task.

Code Review

Use for Phase 9. Take a holistic review stance: findings first, ordered by severity, grounded in file/line references.

  1. Gather context: feature description, modified files, design docs, risky areas, tests already run.
  2. Verify design alignment by summarizing architectural intent and checking implementation matches.
  3. For each modified file, grep exported names to trace callers and dependents. Read relevant signatures, call sites, and type definitions.
  4. Check consistency against 1-2 similar modules.
  5. Search for existing utilities the new code could reuse or now duplicates. Flag near-matches honestly; do not force a wrong abstraction.
  6. Verify contract integrity at API, type, config, and schema boundaries.
  7. Check boundary discipline: external data is validated at edges, internal code is not littered with redundant guards, and transport/storage/framework types do not leak through domain APIs.
  8. Check reader load: needless layers, pass-through methods, broad shallow interfaces, mutable state scope, and unclear value ownership.
  9. Check domain fit: branch growth, synchronized flags, repeated shape assumptions, temporal decomposition, and missing state models.
  10. Check dependency health, including circular dependencies or version conflicts from new imports.
  11. Check breaking changes. For public/external APIs, recommend parallel change and deprecation over in-place mutation. For in-repo-only callers, all callers should be migrated and legacy APIs deleted.
  12. Check rollback safety, especially irreversible migrations or one-way data/state changes.
  13. Review file by file for correctness, logic, edge cases, redundancy, security, performance, error handling, and test coverage.
  14. Check cross-cutting concerns: naming conventions, documentation updates, missing tests, config/migration changes.
  15. Summarize blocking issues, important follow-ups, and nice-to-haves. Per finding include file, issue, impact severity, and recommendation.
  16. If task tracing is available, add blockers and set blocked for blocking findings; if review passes with final evidence, close the task per task.
  17. Complete final checklist: design match, no logic gaps, security addressed, integration points verified, tests cover changes, docs updated.

Read the full file on GitHub · 42 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. yesterday First seen · 42 lines · 47 tokens per session scan A 367ce32411f9

Subscribe to this mod's changes

dev-review is a skill published in the GitHub repository codeaholicguy/ai-devkit (1,601 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 700 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

tmux

Remote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output.

HKUDS/nanobot · 22 tokens

summarize

Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).

HKUDS/nanobot · 32 tokens

foundry-config-setup

Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.

microsoft/agent-framework · 65 tokens

complete-partial-pr

Evaluate and complete an issue or PR where the submitted patch fixes only a narrow symptom of the reported pain point. Use when a contribution may miss adjacent integration surfaces, provider/spec semantics, roundtrip behavior, tests, docs, or historical maintainer decisions.

pydantic/pydantic-ai · 55 tokens

hive.chart-creation-foundations

Required reading whenever any chart tool is available. Teaches the one-tool embedding contract (call chartrender → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no…

aden-hive/hive · 133 tokens

peon-ping-log

Log exercise reps for the Peon Trainer. Use when user says they did pushups, squats, or wants to log reps. Examples - "/peon-ping-log 25 pushups", "/peon-ping-log 30 squats", "log 50 pushups".

PeonPing/peon-ping · 64 tokens