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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/XiaomiMiMo/MiMo-Codenpx agentmods add skills/xiaomimimo/mimo-code/review-rep-call-trendsWrote 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/review-rep-call-trends)<a href="https://agentmods.dev/skills/xiaomimimo/mimo-code/review-rep-call-trends"><img src="https://agentmods.dev/badge/skills/xiaomimimo/mimo-code/review-rep-call-trends.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 17 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00055 | $0.02270 |
| Opus 5 | $0.00028 | $0.01135 |
| Sonnet 5 | $0.00011 | $0.00454 |
| Haiku 4.5 | $0.00006 | $0.00227 |
Grade A, and why
review-rep-call-trends 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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review-rep-call-trends — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Rep Call Trends
Context-Gathering Intake
Whenever this skill asks for context, strongly prefer using the answers-ask-user-input skill and the ask_user_input tool over other tools such as request_user_input; otherwise ask directly in the conversation.
Turn a representative time-based call sample into objective coaching guidance. This skill compares older and newer call evidence to identify genuine improvement, regression, and stable patterns. It owns the readout only; it does not create trackers, send feedback, post messages, or save artifacts.
Common Skill Instructions
MANDATORY: If not already in context, read and adhere closely to plugins/sales/skills/index/SKILL.md## Cross-Skill Best Practices.
Key Dependency Categories
These categories are particularly important for this workflow; use other sources only when they materially improve sampling or interpretation.
- [Blocking] ~~Meeting Transcripts for time-sliced call search, summaries, transcript moments, speaker context, dates, companies, and clickable call links. It blocks the default live-source trend path; explicit older/newer call evidence already in context satisfies the need.
- ~~Knowledge & Files for user-provided or exported transcripts, call summaries, manager-provided call sets, and prior coaching context
Call summaries or transcripts are required for trend claims. Manager notes, CRM outcomes, and generic impressions may provide context, but cannot substitute for older-versus-newer call evidence. If the sample is small or skewed, return a limited trend readout rather than overgeneralizing.
Reference Loading
SKILL.md owns the normal time-sliced trend workflow and output format. Load references only when their extra detail matters:
- Use references/request-schema.yaml for structured input normalization.
- Use references/rubric.md when the compact behavior categories are insufficient for classifying evidence, especially technical-success or meeting-craft signals.
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.
- 4d ago First seen · 161 lines · 55 tokens per session scan A 93dbaf5c3f21
review-rep-call-trends is a skill published in the GitHub repository XiaomiMiMo/MiMo-Code (12,961 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 2,270 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
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