MiMoCode is a terminal-based AI coding assistant that reads and writes code, runs commands, manages Git, and remembers project context across sessions. Developers use it to work with software projects through a command-line interface and connect it to language-model providers; the catalogue includes skills and instructions for it.
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.
npx skills add XiaomiMiMo/MiMo-Code --skill visualize-datagit clone --depth 1 https://github.com/XiaomiMiMo/MiMo-CodeWrote 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/xiaomimimo/mimo-code/visualize-data)<a href="https://agentmods.dev/skills/xiaomimimo/mimo-code/visualize-data"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/visualize-data/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/xiaomimimo/mimo-code/visualize-data"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/visualize-data.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 | $0.00032 | $0.00585 |
| Opus 5.5 | $0.00013 | $0.00234 |
| Sonnet 5.5 | $0.00006 | $0.00117 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
visualize-data 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.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visualize Data
Create quantitative visuals that are analytically sound, immediately readable, and appropriate for their destination. Treat a chart as evidence for a takeaway, not decoration.
Workflow
- State the analytical question and intended takeaway.
- Verify the data grain, measures, dimensions, units, missing values, filters, time window, sample size, and source.
- Choose the chart family from the analytical relationship:
- Change over ordered time: line or area chart.
- Category comparison: bar chart.
- Distribution: histogram, box plot, or density plot.
- Relationship between numeric measures: scatter plot.
- Composition: stacked bar/area; use pie only for a small set of meaningful parts.
- Funnel progression: funnel or ordered bars with stage conversion.
- Contribution to change: waterfall.
- Exact lookup values: table, optionally with small bars or sparklines.
- Define explicit encodings, sorting, aggregation, grouping, scales, labels, units, colors, annotations, and uncertainty treatment.
- Render with the destination's native system when one is selected. Otherwise prefer reproducible Python/Matplotlib or SVG for files and inline artifacts. Notebook-native plotting is appropriate for notebooks.
- Inspect the final output in context when the host provides image, browser, document, or notebook inspection.
Do not install or require React, Recharts, Vite, MCP widgets, or proprietary UI renderers. For self-contained HTML, use inline SVG/canvas or a static image plus semantic fallback data. Avoid remote scripts unless the user explicitly accepts the dependency.
Visual quality
- Use an answer-oriented title and a subtitle only when it adds a distinct takeaway.
- Label axes and units; show legends or direct labels for every visible group.
- Use consistent scales for comparisons and avoid misleading truncated axes unless clearly justified.
- Prefer direct labeling and restrained color. Reserve semantic colors for meaning such as positive, warning, or negative states.
- Keep annotations selective and evidence-backed.
- Make dense charts readable through aggregation, faceting, filtering, or a table rather than shrinking text.
- Provide accessible contrast, text alternatives, and a data table when the destination supports them.
- Put provenance in a source note rather than cluttering the title.
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.
- 1mo ago First seen · 48 lines · 32 tokens per session scan A 335a70547627
visualize-data is a skill published in the GitHub repository XiaomiMiMo/MiMo-Code (13,664 stars, last pushed yesterday), licensed MIT. It adds 32 tokens to every session and 585 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 2026-08-30.
Other skills, from other repositories
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
atmos-config
Atmos root configuration: atmos.yaml discovery, precedence, deep merging, basepath, imports, minimal bootstrap, and routing to narrower Atmos skills.
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…
restore-internals-seams-in-finally-blocks-after-each-test
When delegating a task affected by this skill, include.