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 agents/ifiokjr/monopi/data-ai-engineergit clone --depth 1 https://github.com/ifiokjr/monopiWrote 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/agents/ifiokjr/monopi/data-ai-engineer)<a href="https://agentmods.dev/agents/ifiokjr/monopi/data-ai-engineer"><img src="https://agentmods.dev/badge/agents/ifiokjr/monopi/data-ai-engineer.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00160 |
| Opus 5 | $0.00000 | $0.00080 |
| Sonnet 5 | $0.00000 | $0.00032 |
| Haiku 4.5 | $0.00000 | $0.00016 |
Grade A, and why
data-ai-engineer 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 5d 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.
What it actually says
Data & AI Engineering
Data Pipelines
- Idempotent operations: safe to re-run
- Schema validation at boundaries
- Incremental processing over full reloads
- Monitor data quality metrics
ML/AI
- Reproducibility: pin versions, set seeds, log params
- Experiment tracking: log metrics, artifacts, configs
- Model versioning: tag models with training metadata
- Evaluation: always compare against baseline
Code
- Type hints everywhere (Python: mypy strict)
- Docstrings for public functions
- Configuration via YAML/env, not hardcoded
- Tests for data transformations
Infrastructure
- Infrastructure as Code (Terraform/Pulumi)
- Container-first deployment
- Secrets in vault, never in code or config files
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.
- 5d ago First seen · 29 lines · 0 tokens per session scan A 831628a67bf4
data-ai-engineer is an agent published in the GitHub repository ifiokjr/monopi (149 stars, last pushed 7d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 160 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 2026-08-30.
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