senior-code-review

senior-code-review is a skill for Claude Code, Codex from adamdaw/claude-code-skills. It costs 108 tokens per session (1,917 once invoked), scanned A, original, MIT.

A code-review process that reads the full checked-out change and examines it for correctness, code quality, simplicity, and project standards. It gives an independent report but leaves approval to a person.

In plain words
What is it for?
Use it to review pull requests, diffs, or other code changes before merging. It helps identify bugs, unnecessary complexity, and departures from the team's standards.
Why use it?
It reduces the risk of missing problems when reviewing only the diff or relying on someone else's conclusions. It also keeps the merge decision with a human.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

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/adamdaw/claude-code-skills/senior-code-review
Any agent
npx skills add adamdaw/claude-code-skills --skill senior-code-review
Clone the repo
git clone --depth 1 https://github.com/adamdaw/claude-code-skills

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 senior-code-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/adamdaw/claude-code-skills/senior-code-review.svg)](https://agentmods.dev/skills/adamdaw/claude-code-skills/senior-code-review)
Your own site
<a href="https://agentmods.dev/skills/adamdaw/claude-code-skills/senior-code-review"><img src="https://agentmods.dev/badge/skills/adamdaw/claude-code-skills/senior-code-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,917 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 5.1 $0.00108 $0.01917
Opus 5 $0.00054 $0.00958
Sonnet 5 $0.00022 $0.00383
Haiku 4.5 $0.00011 $0.00192

Measured 5d ago against content hash f80c2fcd5f3a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

senior-code-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 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/senior-code-review/SKILL.md · 54 lines

How it starts

The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Senior code review

One review that runs several complementary passes over a change, then applies judgement and stops at the human gate. The point is an independent read, as honest as you can make it, that informs the human's call without ever substituting for it. This is the operational companion to running-a-review and review-voice; the never-approve rule and the human gate are the whole point, not a nicety.

The one idea to start with: the reviewer reads cold and reports. It doesn't approve. A human posts, and the team's approval gate clears a merge.

Run it in this order

  1. Read cold. Form the read with nobody's findings in front of you and no conclusion pre-loaded, no statement of what you're supposed to confirm. The change's own description and ticket are fair to read, since they're claims to verify; what you refuse is a briefing that tells you the verdict to reach. Brief a reviewer with a conclusion and all you get back is that conclusion.
  2. Read the checked-out code, not just the diff. First confirm what actually changed against the base (the live diff), so you aren't reviewing already-merged or phantom content (a rebased-empty branch reads as real files but is a no-op diff). Then check the branch out into a throwaway worktree and read the real files for context. The load-bearing facts are usually invisible in the patch: a default that silently coerces a missing value to zero, an access grant the change leans on, a dependency version that shifted under it. The main clone is a read-only reference; never mutate it.
  3. Walk the four passes below to gather candidate findings.
  4. Reconcile against the live thread. Re-fetch the head (the author may have pushed, even fixed the thing) and read the existing comments. Check the commit each approval and each automated finding was made against. Don't raise an earlier approval as stale: whether it survives a push is the gate's configuration, not a finding. Do raise a substantive change that landed after an approval, a rework rather than a rebase, since the approvers signed off on something else. A bot finding computed on an older commit may be moot, so verify it against the live diff before repeating it. Already raised and open? Add weight only if you have something new. Already fixed on a newer commit? Drop it. Already dispositioned as by-design? Engage the reasoning or accept it. "Do nothing" is a valid outcome.
  5. Work it finding by finding, with the human running the review. Surface each finding as it clears: what it is, the evidence, whether it blocks. Size it and decide together (drop / nit / follow-up ticket / hold / fold in), then the next. The agent proposes and the operator disposes; any back-and-forth with the author is the human's, downstream of this.
  6. Assemble the survivors into one review, then re-voice it into the posting reviewer's register before it goes out (see review-voice); the raw findings as gathered aren't postable as-is. If nothing survives, add nothing, just recommend approval.
  7. Stop at the gate. Recommend hold or approve; the human posts and holds the binding approval.

Read the full file on GitHub · 54 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 · 54 lines · 108 tokens per session scan A f80c2fcd5f3a

Subscribe to this mod's changes

senior-code-review is a skill published in the GitHub repository adamdaw/claude-code-skills (2 stars, last pushed 17d ago), licensed MIT. It adds 108 tokens to every session and 1,917 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 2026-08-31.

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

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 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

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

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens