fleet-memory: Skill for Claude Code

.claude/skills/code-review/SKILL.md

code-review is a skill for Claude Code from Holetron-lab/fleet-memory. It costs 35 tokens per session (2,246 once invoked), scanned A, original, MIT.

A code-review guide focused on project standards, tests, unused code, types, linting, and conventions. It includes specific rules for Python projects and structured data models.

In plain words
What is it for?
Use it to review changed code for missing tests, type-safety issues, lint errors, dead code, and violations of the project's coding rules.
Why use it?
It helps catch quality and consistency problems after implementation, before code is shared or released.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

This is Holetron-lab/fleet-memory's own configuration. It tells Claude Code how to work on fleet-memory itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything fleet-memory configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is ./scripts/hooks/lint.sh.

Reuse

Borrowing it

Nothing to install: this file belongs to Holetron-lab/fleet-memory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Holetron-lab/fleet-memory/main/.claude/skills/code-review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Holetron-lab/fleet-memory

Made for: Claude Code.

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 code-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/holetron-lab/fleet-memory/code-review/github.svg)](https://agentmods.dev/skills/holetron-lab/fleet-memory/code-review)
Your own site
<a href="https://agentmods.dev/skills/holetron-lab/fleet-memory/code-review"><img src="https://agentmods.dev/badge/skills/holetron-lab/fleet-memory/code-review/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 code-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/holetron-lab/fleet-memory/code-review"><img src="https://agentmods.dev/badge/skills/holetron-lab/fleet-memory/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,246 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 $0.00035 $0.02246
Opus 5.5 $0.00014 $0.00898
Sonnet 5.5 $0.00007 $0.00449
Haiku 4.5 $0.00003 $0.00225

Measured 1mo ago against content hash 18da725508bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo 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.

.claude/skills/code-review/SKILL.md · 206 lines

How it starts

The opening of the file, as written. The whole thing — 206 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 · 206 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. 1mo ago First seen · 206 lines · 35 tokens per session scan A 18da725508bb

Subscribe to this mod's changes

code-review is a skill published in the GitHub repository Holetron-lab/fleet-memory (0 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 2,246 once invoked, about $0.0001 per session on Opus 5.5. 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

pre-pr

Prepare a Honcho change for a pull request to plastic-labs/honcho. Invoke before opening a PR, when drafting a PR body, when asked if a branch is PR-ready, or when filling the pull request template. Checks the linked issue, required tests and docs, then writes Description / Proofs / Fixes.

plastic-labs/honcho · 69 tokens

attest

Verifying spec compliance: extracts ACs from specs, adversarially checks conformance, generates BDD scenarios and traceability matrices. Use when impl must be proven to match a PRD/SRS/AC.

simota/agent-skills · 46 tokens

guardian

Gatekeeping Git/PR by classifying change essence and recommending granularity, naming, and strategy; the ship recipe also executes PR delivery (create, CI watch, merge, cleanup) with Ask First on merge. Use when PR preparation, commit strategy, or gated PR delivery is needed.

simota/agent-skills · 62 tokens

atlas

Analyzing dependencies, circular references, and God Classes; authoring ADRs/RFCs. Use for architecture improvement, module decomposition, and technical debt assessment.

simota/agent-skills · 34 tokens

judge

Reviewing code via multi-engine orchestration (Claude + Codex) on three axes — secure, correct, and lean — shipping only findings worth fixing. Use for PR review or pre-commit. Complements Zen.

simota/agent-skills · 46 tokens

quill

Adding JSDoc/TSDoc, updating READMEs, replacing any types with proper definitions, and adding high-value comments to complex logic. Use for documentation gaps or type safety.

simota/agent-skills · 40 tokens