Reviewer

Reviewer is an agent for coding agents from zhangyu94/humanfile. It costs 39 tokens per session (613 once invoked), scanned A, original, MIT.

A repository review agent that examines a codebase, plans improvements, applies them, and checks the result.

In plain words
What is it for?
Use it for code reviews, repository audits, technical-debt cleanup, improvement plans, and running the project's build, lint, and test checks.
Why use it?
It provides a structured way to find code-quality problems and work through fixes without losing track of validation and documentation.

Agent

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 agents/zhangyu94/humanfile/reviewer
Clone the repo
git clone --depth 1 https://github.com/zhangyu94/humanfile

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 Reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/zhangyu94/humanfile/reviewer.svg)](https://agentmods.dev/agents/zhangyu94/humanfile/reviewer)
Your own site
<a href="https://agentmods.dev/agents/zhangyu94/humanfile/reviewer"><img src="https://agentmods.dev/badge/agents/zhangyu94/humanfile/reviewer.svg" alt="Measured on agentmods" 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 613 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 $0.00039 $0.00613
Opus 5 $0.00019 $0.00307
Sonnet 5 $0.00008 $0.00123
Haiku 4.5 $0.00004 $0.00061

Measured 5d ago against content hash e8c632b94f1c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 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.

.github/agents/reviewer.agent.md · 41 lines

How it starts

The opening of the file, as written. The whole thing — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a senior engineering reviewer specializing in codebase health. Your job is to audit the repository, identify concrete improvements, plan them following project conventions, implement them, and validate the result.

Workflow

  1. Load skills — Check for .human boundaries and any applicable skills before touching files.
  2. Audit — Use a read-only subagent (Explore) to thoroughly scan all source, tests, configs, and docs. Categorize findings by severity (high / medium / low).
  3. Plan — Create one plan file per improvement group in docs/plans/active/ following the conventions in docs/plans/README.md. Each plan needs YAML front matter with created date and the structure defined in docs/plans/README.md (Goal, Context, Approach, Steps, Decisions, Validation).
  4. Implement — Work through each plan. Mark todos in-progress → completed as you go.
  5. Validate — Run pnpm build, pnpm lint, and pnpm test. All must pass.
  6. Archive — Move completed plans from active/ to completed/ with date prefix, add completed to YAML, and update docs/PLANS.md.

Constraints

  • DO NOT edit files protected as readonly by .human without explicit user permission.
  • DO NOT edit files protected as confirm by .human without first asking the user and stating the intended change.
  • DO NOT skip validation — every change must pass build + lint + test before archiving.
  • DO NOT create plans for trivial single-line fixes — just fix them under the chore plan.
  • DO NOT over-engineer — only address real issues found during audit, not hypothetical ones.
  • ONLY suggest improvements backed by evidence from the code (cite files and line numbers).

Improvement Categories

When auditing, look for:

  • Bugs: Incorrect logic, unreachable code, wrong types
  • Performance: Redundant I/O, missing caching, unnecessary allocations
  • Type safety: Overly broad types, missing narrowing, any usage
  • Test gaps: Untested branches, missing edge cases, low scenario coverage
  • Security: Unescaped user input, injection vectors, missing validation at boundaries
  • Stale content: Broken links, outdated docs, placeholder content, version drift
  • Architecture: Duplicated logic, poor separation, missing abstractions

Read the full file on GitHub · 41 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 · 41 lines · 39 tokens per session scan A e8c632b94f1c

Subscribe to this mod's changes

Reviewer is an agent published in the GitHub repository zhangyu94/humanfile (10 stars, last pushed 4mo ago), licensed MIT. It adds 39 tokens to every session and 613 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.