MiMo-Code

Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite.

Use this skill whenever the user wants to find, read, cite, track, download, or analyze academic papers on arXiv. That includes: searching papers by topic, author, category, or arXiv ID; fetching abstracts or full metadata; generating BibTeX citations; downloading PDFs; listing the latest submissions in a field (e.g. cs.AI daily digest); checking a paper's citation impact; finding who cites a paper, what it references, or related-paper recommendations. Trigger on mentions of 'arXiv', an arXiv ID (e.g. 2601.02780 or hep-th/0601001), an arxiv.org URL, 'paper search', 'literature review', 'find papers about X', 'cite this paper', or 'what's new in cs.LG'.

Build a source-backed dashboard or monitoring view with clear metrics, filters, visual hierarchy, definitions, refresh context, and QA.

Build a durable analytical report with an answer-first narrative, evidence-backed findings, charts or tables, caveats, recommendations, and source context.

Operate Claude Code CLI (v2.1+) via the terminal. Load only when the user explicitly authorizes this skill or names Claude Code / claude-code. Before any command, confirm the `claude` binary is available; if missing, stop and give install or auth guidance. Covers print mode (-p), interactive tmux sessions, and background (--bg) orchestration.

Operate OpenAI Codex CLI in non-interactive headless environments. Load only when the user explicitly authorizes this skill or names Codex / codex. Before any command, confirm the `codex` binary is available; if missing, stop and give install or auth guidance. Covers `codex exec` automation in Bash or PowerShell, native Windows or WSL2, scripts, CI/CD, Docker, Kubernetes, remote servers, agent harnesses, sandbox and approval modes, JSONL events, session resume, and stdin prompts.

Use for multi-step feature work, bug fixes, or refactors where requirements need to settle, a feature document should carry design + tasks + delivery evidence, and the change deserves independent review before merge. Use it only when the user explicitly requests this workflow, whether with `/compose-next`, by name, or in any other clear natural language; do not infer the request from task complexity alone. Not for one-shot edits, single-file tweaks, or answering questions — those need no orchestration overhead.

Create, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite.

Use this skill for quantitative product or business analysis: data quality checks, metric diagnostics, KPI design and reporting, dashboards, analytical reports, charts, notebooks, market sizing, semantic layers, and evidence-backed recommendations. Also use it whenever Data Analytics is explicitly invoked.

Deep research on any topic using parallel sub-agents, the available search tool (websearch or a search MCP server), WebFetch and free APIs. Use when the user asks for a thorough multi-source investigation with a cited report — "深度调研X"、"deep research"、"帮我全面研究一下"、"多方求证"、"写一份调研报告". NOT for simple lookups (a single search suffices) and NOT for academic literature surveys (use auto-research skill instead).

Design KPI frameworks, metric definitions, targets, guardrails, and measurement plans for product or business decisions. Use when success metrics, drivers, guardrails, targets, or the measurement approach need to be defined or improved.

Use this skill whenever a Microsoft Word (.docx) file is being produced, opened, transformed, or read. That includes: drafting reports, letters, contracts, RFPs, technical documents, or any long-form written deliverable; extracting text or structure from an existing Word file; filling a Word template with values; converting Word to PDF or plain text; splitting or merging documents; inspecting styles, headings, sections, tables, images, comments, or tracked changes. Trigger on mentions of 'Word doc', 'DOCX', 'Office document', a filename ending in .docx, or requests like 'turn this into a Word report'.

Use when developing or testing the MiMoCode repository and you need to programmatically drive another MiMoCode (mimo) process. Supports headless `mimo run` with JSON events and interactive TUI via tmux for behavior, integration, and visual regression testing. Covers an installed `mimo` binary or a dev build launched from source with `bun dev`; do not use it for ordinary MiMoCode task execution.

Answer questions about the Effect framework

Gather business context from connected or provided sources so downstream analysis starts with the right framing. Use when an analytical question depends on missing context, such as what a metric means, what changed recently, or which sources should be checked. If the same prompt asks for diagnosis, recommendation, or a deliverable, gather context first and continue to the focused skill.

Operate the Grok Build CLI (`grok`), including interactive and headless runs, authentication, sessions, worktrees, permissions, sandboxing, MCP servers, plugins, inspection, updates, and automation output. Load only when the user explicitly authorizes this skill or names Grok Build / grok-build. Before any command, confirm the `grok` binary is available; if missing, stop and give install or auth guidance.

Route broad Data Analytics requests to the appropriate quantitative analysis, visualization, dashboard, report, notebook, KPI, market-sizing, validation, or semantic-layer workflow.

Create, edit, or validate reproducible SQL or Python notebooks. Use for notebooks, SQL/Python scratchpads, reproducible exploration, audit trails, or runnable companions where the analysis should be reviewable or rerunnable.

Prepare KPI readouts, scorecards, WBR/MBR/QBR updates, and executive summaries from quantitative business or product metrics; use when the task is to report status, compare against targets, explain validated drivers, and state operating implications.

Turn an uploaded PDF, paper, book chapter, document, URL, or user-provided topic into a structured, interactive learning course. Use when the user wants to learn, study, understand, master, review, or practice a subject chapter by chapter; asks for a tutorial or curriculum from source material; wants exercises, quizzes, answer grading, hints, spaced review, or a final assessment; or says "teach me this", "learn this PDF", "分章节教学", "带我学", or similar; or returns to continue a previous course ("continue my course", "接着上次学") or supplies a saved course-state file or state block. Adapt explanations and practice to the learner's level, preserve page or section references for documents, and teach incrementally rather than dumping all content at once.