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.
git clone --depth 1 https://github.com/MN-Lizard-Team/aiyu-multi-agentWrote 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/commands/mn-lizard-team/aiyu-multi-agent/data)<a href="https://agentmods.dev/commands/mn-lizard-team/aiyu-multi-agent/data"><img src="https://agentmods.dev/badge/commands/mn-lizard-team/aiyu-multi-agent/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/commands/mn-lizard-team/aiyu-multi-agent/data"><img src="https://agentmods.dev/badge/commands/mn-lizard-team/aiyu-multi-agent/data.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 | $0.00000 | $0.00471 |
| Opus 5.5 | $0.00000 | $0.00188 |
| Sonnet 5.5 | $0.00000 | $0.00094 |
| Haiku 4.5 | $0.00000 | $0.00047 |
Grade A, and why
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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/data
Data pipeline design, ML model development, and analytics. Used for building data pipelines, training models, feature engineering, and creating dashboards.
⚠️ CURSOR OUTPUT CONTRACT
You MUST start your FIRST response with this exact agent activation line:
🤖 **Active Agent: `data-scientist`** | Skills: `clean-code, python-patterns, database-design, api-patterns`
If this line is missing from your response, you are violating the protocol. Add it before any other content.
Required Behavior
- Read the agent's full instructions from
.windsurf/agents/data-scientist.md(or.cursor/rules/agents/data-scientist.mdc) - Apply the Socratic Gate: ask clarifying questions before coding if requirements are unclear
- Follow clean-code principles: concise, no over-engineering, self-documenting
/data — Data Science & ML
$ARGUMENTS
🤖 Agent Activation
MANDATORY: Before starting any work, announce the active agent to the user.
🤖 **Active Agent: `data-scientist`** | Skills: `clean-code, python-patterns, database-design, api-patterns`
Task
Design data pipelines, build ML models, and create analytics dashboards.
Steps:
-
Problem Definition
- What is the business question?
- Define success metrics (F1, RMSE, conversion lift)
- Identify data sources
-
Data Pipeline
- Collect + clean data
- ETL/ELT architecture
- Data quality checks
-
Exploratory Analysis
- Distribution, correlation, outliers
- Feature candidates
- Baseline model
-
Model Development
- Select algorithm based on problem type
- Train + validate with cross-validation
- Hyperparameter tuning
-
Deployment & Monitoring
- Model serving API
- Feature drift monitoring
- Retraining schedule
Usage Examples
/data build recommendation engine
/data design ETL pipeline for analytics
/data train fraud detection model
/data create dashboard for KPI tracking
/data analyze churn prediction
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 · 82 lines · 0 tokens per session scan A eb26727739a6
data is a command published in the GitHub repository MN-Lizard-Team/aiyu-multi-agent (8 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 471 tokens. 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 2026-08-31.
Other commands, from other repositories
dashboard-session-tell
Send a prompt to another session. Usage /dashboard:session-tell.
publish
AgentWorks: the open-source control plane for running, measuring, and improving AI agent workflows across your company.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.