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 validate-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/validate-data)<a href="https://agentmods.dev/skills/xiaomimimo/mimo-code/validate-data"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/validate-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/validate-data"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/validate-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 | $0.00040 | $0.02679 |
| Opus 5.5 | $0.00016 | $0.01072 |
| Sonnet 5.5 | $0.00008 | $0.00536 |
| Haiku 4.5 | $0.00004 | $0.00268 |
Grade A, and why
validate-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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Related Skills
Use $analyze-data-quality when validation depends on whether the underlying data is trustworthy, comparable, fresh, or at the right grain.
Use $product-business-analysis when the task asks for a recommendation or decision after the validation pass.
Validate Data Analysis
Validate an analysis before it is shared with stakeholders. Focus on whether the question, data, methodology, calculations, visuals, claims, caveats, and recommendations are trustworthy enough for the stated audience and decision. This skill is for analysis QA, not raw dataset profiling alone. When validation depends on dataset reliability checks such as freshness, grain, missingness, duplicates, join coverage, or source mismatches, use $analyze-data-quality as a companion.
Workflow
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Inventory the artifact and claims.
Identify the report, notebook, spreadsheet, SQL, dashboard, chart, pasted analysis, or recommendation being validated. Inspect source artifacts when a path, link, query, notebook, spreadsheet, or dashboard is referenced. Extract the main question, audience, decision, key claims, headline numbers, data sources, time windows, populations, filters, comparison baselines, and stated caveats. Verify that every metric or KPI requested by the user appears in the analysis or is explicitly marked unavailable, not applicable, or out of scope.
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Validate the question, methodology, and assumptions.
Confirm that the analysis answers the stated business or product question, not a nearby easier question. Check whether the population, eligibility rules, exclusions, sampling, metric definitions, formulas, units, denominators, timezones, cohorts, comparison periods, and baselines match the stakeholder decision. Flag hidden exclusions, inconsistent definitions, partial-period comparisons, and causal wording that lacks experimental or otherwise credible causal evidence.
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Validate data selection and quality risks.
Confirm that the chosen tables, files, dashboards, or extracts are appropriate and current enough for the decision. Check freshness or "as of" date, expected partitions, segment coverage, row/category completeness, null handling, deduplication, filter logic, join coverage, and source mismatches when those risks could change the conclusion. Use
~~structured_datafor source metadata, schema checks, sample rows, query history, or SQL spot checks through the relevant source connector when available. Use~~operations_logsfor table freshness, lineage, or pipeline context.
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 · 190 lines · 40 tokens per session scan A 7aa5c77b22b9
validate-data is a skill published in the GitHub repository XiaomiMiMo/MiMo-Code (13,676 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 2,679 once invoked, about $0.0002 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.