Semiotic is a React library for building data visualizations such as charts, network graphs, streaming displays, and coordinated dashboards. It is used by developers who need to render data-driven interfaces, including with AI coding assistants. Its schemas and MCP server support the catalogue's add-ons for generating chart code.
Borrowing it
Nothing to install: this file belongs to nteract/semiotic. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nteract/semiotic/main/.github/skills/code-review/SKILL.mdgit clone --depth 1 https://github.com/nteract/semioticWrote 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.
[](https://agentmods.dev/skills/nteract/semiotic/code-review)<a href="https://agentmods.dev/skills/nteract/semiotic/code-review"><img src="https://agentmods.dev/badge/skills/nteract/semiotic/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.
<a href="https://agentmods.dev/skills/nteract/semiotic/code-review"><img src="https://agentmods.dev/badge/skills/nteract/semiotic/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00097 | $0.01204 |
| Opus 5.5 | $0.00039 | $0.00482 |
| Sonnet 5.5 | $0.00019 | $0.00241 |
| Haiku 4.5 | $0.00010 | $0.00120 |
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 2mo 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.
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
- Read the pull request description, changed files, and relevant sections of
AGENTS.md. - 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.
- Identify the user-visible or package-level behavior the change claims to preserve or introduce.
- 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.
- 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
framePropsprecedence 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.
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.
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.
- 2mo ago First seen · 121 lines · 97 tokens per session scan A ecf49283f0ab
code-review is a skill published in the GitHub repository nteract/semiotic (2,717 stars, last pushed today), licensed Apache-2.0. It adds 97 tokens to every session and 1,204 once invoked, about $0.0004 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 2026-08-30.
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