code-review

A code-review guide for checking Semiotic pull requests, which are proposed GitHub code changes, for behavior problems and missing proof that they work.

In plain words
What is it for?
It is for reviewing TypeScript and React charts, browser and server rendering, documentation, AI schemas, generated files, tests, and CI results.
Why use it?
It helps find user-visible bugs, regressions, broken public interfaces, and unsupported claims without merely repeating the code changes.

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

Made for: Claude Code, Codex.

Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,204 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 $0.00097 $0.01204
Opus 5 $0.00048 $0.00602
Sonnet 5 $0.00019 $0.00241
Haiku 4.5 $0.00010 $0.00120

Measured 2d ago against content hash ecf49283f0ab, 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 2d 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/skills/code-review/SKILL.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review Semiotic pull requests

Produce a findings-first review grounded in executable behavior. Treat AGENTS.md as the repository authority. Do not restate the diff or praise the change; identify defects that the author can act on.

Establish the review contract

  1. Read the pull request description, changed files, and relevant sections of AGENTS.md.
  2. Classify the change surface: chart HOC, Stream Frame/runtime, browser interaction, SSR/server, package surface, docs/example, AI/schema, generated artifact, test infrastructure, or custom lint.
  3. Identify the user-visible or package-level behavior the change claims to preserve or introduce.
  4. Use GitHub MCP when the pull request references an issue, incident, prior pull request, or failing check. Read the referenced acceptance criteria and compare them with the implementation. Do not infer requirements from an identifier without retrieving it.
  5. Inspect available CI results through GitHub MCP when they can confirm a suspected regression or reveal an unverified path. Distinguish a product defect from a stale generated baseline or an unstable-rule disagreement.

Trace behavior, not files

Follow changed values across component boundaries. A local implementation can be type-correct while breaking a downstream scene, renderer, or package contract.

For chart HOCs and Stream Frames, check:

  • Accessor, grouping, style, tooltip, hover, selection, legend, margin, and frameProps precedence through the final Stream Frame props.
  • Line-object versus flat-row normalization and preservation of parent series metadata.
  • Static mode versus push mode. Static and serialized paths require real data; React push mode omits data. data={[]} is not push mode.
  • Controlled updates, refs, rAF scheduling, cleanup, transition continuity, stable configuration identity, and retained-scene invalidation.
  • Coordinate-space assumptions involving margins, legends, scales, responsive dimensions, device pixel ratio, canvas bounds, and pointer type.
  • Browser, SSR, hydration, static renderer, and serialized/MCP parity when the shared behavior crosses those paths.

Read the full file on GitHub · 121 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 2d ago First seen · 121 lines · 97 tokens per session scan A ecf49283f0ab

Subscribe to this mod's changes

code-review is a skill published in the GitHub repository nteract/semiotic (2,691 stars, last pushed yesterday), licensed Apache-2.0. It adds 97 tokens to every session and 1,204 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 2026-08-30.

Related

Other skills, from other repositories

anyviz

Use when 用户请求选择、创建、改进或统一数据可视化,并需按数据与意图选择图表、主题及 D3.js、ECharts、Mapbox、Three.js、Matplotlib、Plotly 或 ggplot2 技术栈。.

TseringYuu/anyviz · 59 tokens

chart-designer

Design effective data visualizations and charts. Generate chart configurations for ECharts, Chart.js, and other libraries. Create dashboards and reports.

claude-office-skills/skills · 31 tokens

observer

Run a study session over a video or podcast the user wants to understand: transcribe it, prepare the concepts and the visuals before they press play, then answer whatever they ask at the exact moment they pause, with charts you build on the spot. Use this whenever the user shares a YouTube or podcast link and wants it…

hec-ovi/observer-skill · 110 tokens

observer

Run a study session over a video or podcast the user wants to understand: transcribe it, prepare the concepts and the visuals before they press play, then answer whatever they ask at the exact moment they pause, with charts you build on the spot. Use this whenever the user shares a YouTube or podcast link and wants it…

hec-ovi/observer-skill · 110 tokens

fixing-streamlit-ci

Analyze and fix failed GitHub Actions CI jobs for the current branch/PR. Use when CI checks fail, PR checks show failures, or you need to diagnose lint/type/test errors and verify fixes locally.

streamlit/streamlit · 47 tokens

improving-python-coverage

Runs Python unit tests with coverage, analyzes coverage reports, and implements meaningful tests to increase coverage by 0.2%. Use when you want to systematically improve Python test coverage with high-value test cases.

streamlit/streamlit · 47 tokens