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 agentmods add commands/mn-lizard-team/aiyu-multi-agent/datagit clone --depth 1 https://github.com/MN-Lizard-Team/aiyu-multi-agentWhat 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 | $0.00000 | $0.00471 |
| Opus 5 | $0.00000 | $0.00235 |
| Sonnet 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 2d 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.
- 2d 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 (7 stars, last pushed 3mo 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-flow-auto
Toggle autonomous mode for a session's flow. Usage /dashboard:flow-auto.
dashboard-git-branches
List git branches for the current dir (current marked ). Runs locally, no LLM.
budget
Check the Network-AI federated token budget and recent spend.
run-action
Execute a learned Maestro flow ("action") by name with optional -e KEY=VALUE parameters. Looks the flow up via packages/rn-dev-agent-core/dist/learned-actions.js (same inventory as /rn-dev-agent:list-learned-actions), then replays it via cdprunaction — auto-repair-aware orchestration with structured RunRecords (GH.
nav-graph
Extract, inspect, and query the app navigation graph — a complete map of all screens and navigators.
check
Run the full local quality gate — TypeScript check, build, dependency audit (prod-only), skill registry validate. Fast pre-commit / pre-suggestdeploy sweep.