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 deep-researchgit 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/deep-research)<a href="https://agentmods.dev/skills/xiaomimimo/mimo-code/deep-research"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/deep-research/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/deep-research"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00109 | $0.01137 |
| Opus 5.5 | $0.00044 | $0.00455 |
| Sonnet 5.5 | $0.00022 | $0.00227 |
| Haiku 4.5 | $0.00011 | $0.00114 |
Grade A, and why
deep-research 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Orchestrate parallel research sub-agents, then write one coherent cited report. Research is parallel; writing is single-point — never let multiple agents write report sections.
Step 0 — Always first
- Run
date +%Y-%m-%dvia Bash. Never assume the current year from training data. - Triage:
- Answerable with 1-2 searches? → STOP, just use WebSearch directly. Do not use this skill.
- Enumeration task (N items × M fields, e.g. "compare 20 frameworks")? → still this skill, but use table-oriented decomposition (one sub-agent per item batch).
- Open-ended investigation? → continue below.
- Pick depth (default standard; user can override with words like "quick"/"exhaustive"):
| Mode | Sub-agents (round 1) | Max follow-up rounds | Sources target |
|---|---|---|---|
| quick | 2-3 | 0 | 8+ |
| standard | 3-5 | 1 | 15+ |
| deep | 5-8 | 2 | 25+ |
These are hard budgets. Reflection (Phase 4) can spend them but never exceed them.
Workspace
All state lives on disk at ./research/<slug>/ — never only in context (survives compaction):
research/<slug>/
├── brief.md # research brief — the single contract for all phases
├── findings/ # F1.md, F2.md ... one per sub-agent, structured evidence
└── REPORT.md # final deliverable
On resume: re-read brief.md + list findings/, skip completed angles, continue.
Phase 1 — Scope
Ask at most one round of clarifying questions (AskUserQuestion), only if genuinely ambiguous: audience, time frame, region, decision at stake. If the user said "just run it" or intent is clear, skip asking and write assumptions into the brief instead.
Then write brief.md: refined question, scope boundaries (in/out), assumptions, depth mode, today's date. This brief — not the raw conversation — is what every later phase measures against.
Phase 2 — Plan
Decompose the brief into 3-8 independent research angles. Pull from these lenses as applicable: core facts/definitions · recent developments (last 12 months) · quantitative data/benchmarks · counter-arguments & failure cases · practitioner experience (forums, issues) · academic work · key players/alternatives.
What ships with it
3 files 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 Changed · -26 lines b965542c8a45
- 1mo ago First seen · 107 lines · 109 tokens per session scan A 0656515ea92f
deep-research is a skill published in the GitHub repository XiaomiMiMo/MiMo-Code (13,664 stars, last pushed yesterday), licensed MIT. It adds 109 tokens to every session and 1,137 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.
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.