code-review

A code-review guide for checking changed code against project standards, including tests, unused code, type safety, linting, and conventions.

In plain words
What is it for?
Reviewing code changes, checking Python and Pydantic practices, finding missing tests or lint issues, and verifying project-specific rules.
Why use it?
It helps catch quality and maintainability problems after implementation work is finished.

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/vectorize-io/hindsight/code-review
Any agent
npx skills add vectorize-io/hindsight --skill code-review
Clone the repo
git clone --depth 1 https://github.com/vectorize-io/hindsight

Made for: Claude Code, Codex.

Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,430 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 $0.00035 $0.07430
Opus 5 $0.00017 $0.03715
Sonnet 5 $0.00007 $0.01486
Haiku 4.5 $0.00003 $0.00743

Measured yesterday against content hash c24978d06b8f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 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.

.claude/skills/code-review/SKILL.md · 372 lines

How it starts

The opening of the file, as written. The whole thing — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Review

Review all changed code against the project's quality standards and coding conventions.

Code Standards

Read and internalize these standards before writing code. The review steps below verify compliance.

Python Style

  • Python 3.11+, type hints required
  • Async throughout (asyncpg, async FastAPI)
  • Pydantic models for request/response
  • Ruff for linting (line-length 120)
  • No Python files at project root - maintain clean directory structure
  • Never use multi-item tuple return values — not even for internal/private functions. Always use a dataclass or Pydantic model. No exceptions, no "it's just two values" shortcuts. If a function returns more than one value, define a named type for it.

Type Safety with Pydantic Models

NEVER use raw dict types for structured data — this applies to all code, including internal helpers and private functions. If the dict has known keys, it must be a dataclass or Pydantic model:

  • Use Pydantic BaseModel for all data structures passed between functions
  • Use @dataclass for lightweight internal data containers when Pydantic validation isn't needed
  • Add @field_validator for type coercion (e.g., ensuring datetimes are timezone-aware)
  • Avoid dict.get() patterns - use typed model attributes instead
  • Parse external data (JSON, API responses) into Pydantic models at the boundary
  • This catches type errors at parse time, not deep in business logic
  • The only acceptable dict usage is for truly dynamic/unknown keys (e.g., arbitrary metadata, JSON blobs with no fixed schema)
# BAD - error-prone dict access
def process(data: dict) -> str:
    return data.get("name", "")  # No validation, silent failures

# GOOD - typed and validated
class UserData(BaseModel):
    name: str
    created_at: datetime

    @field_validator("created_at", mode="before")
    @classmethod
    def ensure_tz_aware(cls, v):
        if isinstance(v, str):
            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
        if v.tzinfo is None:
            return v.replace(tzinfo=timezone.utc)
        return v

def process(data: UserData) -> str:
    return data.name  # Type-safe, validated at construction

Read the full file on GitHub · 372 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. yesterday First seen · 372 lines · 35 tokens per session scan A c24978d06b8f

Subscribe to this mod's changes

code-review is a skill published in the GitHub repository vectorize-io/hindsight (21,822 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 7,430 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.

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