deep-reviewer

deep-reviewer is an agent for Claude Code from rube-de/cc-skills. It costs 39 tokens per session (916 once invoked), scanned A, original, MIT.

A broad code reviewer that follows how data and control move across functions and files in a pull request.

In plain words
What is it for?
It traces function calls, error paths, asynchronous state changes, and data transformations across boundaries to identify cross-cutting problems.
Why use it?
It finds bugs that may be missed when separate reviewers each inspect only one topic, such as security or error handling.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; mentions CLAUDE.md.

Part of the ci-review plugin — 1 skill, 11 agents shipped together

Good fit It traces function calls, error paths, asynchronous state changes, and data transformations across boundaries to identify cross-cutting problems.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/rube-de/cc-skills/deep-reviewer
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.

Clone the repo
git clone --depth 1 https://github.com/rube-de/cc-skills

Made for: Claude Code.

Or install ci-review, the plugin that ships this one along with the rest of its 1 skill, 11 agents.

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 deep-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/rube-de/cc-skills/deep-reviewer/github.svg)](https://agentmods.dev/agents/rube-de/cc-skills/deep-reviewer)
Your own site
<a href="https://agentmods.dev/agents/rube-de/cc-skills/deep-reviewer"><img src="https://agentmods.dev/badge/agents/rube-de/cc-skills/deep-reviewer/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 deep-reviewer

Your own site · 80×15
<a href="https://agentmods.dev/agents/rube-de/cc-skills/deep-reviewer"><img src="https://agentmods.dev/badge/agents/rube-de/cc-skills/deep-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 916 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 $0.00039 $0.00916
Opus 5 $0.00019 $0.00458
Sonnet 5 $0.00008 $0.00183
Haiku 4.5 $0.00004 $0.00092

Measured 9d ago against content hash 32a7d50622e2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

deep-reviewer 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 9d 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.

plugins/ci-review/agents/deep-reviewer.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a deep code reviewer. You do a thorough, unconstrained review of the PR diff — no artificial scope limits, no domain restrictions.

Why You Exist

Other review agents are specialists (bugs, security, error handling, etc.) with scoped focus areas. Specialization creates gaps — bugs that span multiple domains fall through the cracks. Your job is to close those gaps by reviewing without blinders.

Your Task

You will receive:

  • A PR diff
  • PR metadata (title, body, changed files)
  • CLAUDE.md contents (if found)
  • Optional focus text directing your attention

Review Process

  1. Read the full diff and identify the riskiest changes — large rewrites, new async flows, state management changes, financial/security-critical paths.

  2. Trace control flow across boundaries — this is your primary value:

    • Follow function calls from definition to every call site. Use Grep to find callers.
    • Check if thrown errors are caught by callers. If a function throws, who catches it?
    • Trace state mutations through async operations. If state is set, then an async call happens, then state is read — can the read see stale values?
    • Follow data from user input through transformations to storage/display.
  3. Check cross-cutting concerns:

    • Cleanup/reset functions: do they actually cancel all in-flight operations, or can resolved promises mutate already-reset state?
    • React hooks: when effects depend on state set by other effects or callbacks, trace the full lifecycle. Are there stale closure risks? Can effects re-fire with stale captured values?
    • API contracts: if a client method is renamed/changed, do all callers update? Are types consistent between what the server returns and what the client expects?
  4. Read the full function — not just the diff lines. Use Read to see the complete context:

    • Is the changed code consistent with the function's invariants?
    • Are there assumptions elsewhere in the file that the change invalidates?

Read the full file on GitHub · 76 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. 9d ago First seen · 76 lines · 39 tokens per session scan A 32a7d50622e2

Subscribe to this mod's changes

deep-reviewer is an agent published in the GitHub repository rube-de/cc-skills (10 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 916 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 2026-08-31.

Related

Other agents, from other repositories

streaming-reviewer

Streaming / event-driven pre-implementation reviewer. Outputs threat model TM-{slug}.md and signs off delivery-guarantee + ordering decisions before senior-dev claims tasks.

avelikiy/great_cto · 38 tokens

dry-and-code-smells

Detect code duplication, DRY violations, and classic code smells (shotgun surgery, long methods, feature envy, data clumps) in changed and related files.

ncoevoet/claude-review-all · 39 tokens

project-auditor

Use for /audit or when no PROJECT.md exists. Auditor + Architect hybrid — stack detection, vulnerability analysis, outdated dependency scan, architectural debt, and a concrete refactoring plan.

avelikiy/great_cto · 41 tokens

legal-reviewer

Legal-services / legal-tech specialist pre-implementation reviewer for legal archetype (law firms, solo practitioners, legal-SaaS). Outputs threat model TM-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.

avelikiy/great_cto · 52 tokens

accounting-reviewer

Bookkeeping / general-ledger / financial-close specialist pre-implementation reviewer for fintech and enterprise-saas archetypes. Outputs threat model TM-accounting-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.

avelikiy/great_cto · 52 tokens

edtech-reviewer

Education-technology specialist pre-implementation reviewer for edtech archetype. Specialises in COPPA verifiable parental consent, FERPA student-data handling, GDPR-K (digital age of consent), Section 508 + WCAG 2.2 AA accessibility, child-safety content moderation (CSAM hash, NCMEC reporting), and US state…

avelikiy/great_cto · 112 tokens