What is ML-MLOps-Portfolio AGENTS.md?

A repository guide for an ML service template, a system for building and running machine-learning applications in production. It documents the technology stack, cloud setup, data handling, monitoring, deployment, and specialist-agent responsibilities.

It helps create and maintain Python machine-learning services, validate and version data, train models, deploy them on Google Cloud or AWS Kubernetes, and monitor them.

It gives coding agents the project’s architectural boundaries and required production practices before they change the system.

How DuqueOM/ML-MLOps-Portfolio configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to DuqueOM/ML-MLOps-Portfolio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/DuqueOM/ML-MLOps-Portfolio/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/DuqueOM/ML-MLOps-Portfolio

Made for: Codex, OpenCode.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/duqueom/ml-mlops-portfolio/agents-md/github.svg)](https://agentmods.dev/instructions/duqueom/ml-mlops-portfolio/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/duqueom/ml-mlops-portfolio/agents-md"><img src="https://agentmods.dev/badge/instructions/duqueom/ml-mlops-portfolio/agents-md/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.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/duqueom/ml-mlops-portfolio/agents-md"><img src="https://agentmods.dev/badge/instructions/duqueom/ml-mlops-portfolio/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 8,452 This file is loaded in full into every session.

When invoked 8,452 The same file — it is already loaded in full.

Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.08452	$0.08452
Opus 5.5	$0.03381	$0.03381
Sonnet 5.5	$0.01690	$0.01690
Haiku 4.5	$0.00845	$0.00845

Measured 5d ago against content hash 1d72dc6fbdc1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade C, and why

ML-MLOps-Portfolio AGENTS.md scanned grade C with 1 finding 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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

| Scaffold new service (`new-service.sh`) | AUTO | Reversible via `rm -rf` |

AGENTS.md · 546 lines

How it starts

The opening of the file, as written. The whole thing — 546 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — ML Service Template

Project Identity

ML Service Template: Agent-driven framework for building and maintaining production-grade ML systems with multi-cloud deployment (GKE + EKS), comprehensive observability, and enterprise CI/CD. Every architectural decision documented in ADRs with measured trade-offs.

Stack: Python 3.13 (one uv workspace + uv.lock, ADR-019), scikit-learn, XGBoost, LightGBM, FastAPI, Docker, Kubernetes, Terraform, GitHub Actions
Clouds: GCP (primary) + AWS (secondary parity)
Tracking: MLflow (self-hosted on K8s)
Monitoring: Prometheus + Grafana + AlertManager + Evidently
Data: DVC (GCS + S3 remotes), Pandera validation

Agent Architecture

LAYER 1: ORCHESTRATOR
  → Receives high-level requests ("create a new ML service for [domain]")
  → Determines which specialist agents are needed and in what order
  → Manages task dependencies (cannot deploy before training completes)
  → Calibrates engineering level to project scale (no under/over-engineering)

LAYER 2: SPECIALIST AGENTS (build phase)
  ├── Agent-EDAProfiler       Dataset exploration, baseline distributions, leakage pre-audit
  ├── Agent-DataValidator     Pandera schemas, DVC versioning, leakage checks
  ├── Agent-MLTrainer         Training pipeline, model selection, Optuna tuning
  ├── Agent-APIBuilder        FastAPI app, async inference, SHAP integration
  ├── Agent-DockerBuilder     Optimized Dockerfile, init container pattern
  ├── Agent-K8sBuilder        K8s manifests, HPA, Kustomize overlays
  ├── Agent-TerraformBuilder  IaC for GCP + AWS resources
  ├── Agent-CICDBuilder       GitHub Actions workflows
  ├── Agent-SecurityAuditor   Secret scans, IAM least-privilege, image signing, SBOM
  ├── Agent-MonitoringSetup   Prometheus metrics, Grafana dashboards, alerts
  ├── Agent-DriftSetup        PSI thresholds, CronJob, heartbeat alerts
  ├── Agent-DocumentationAI   ADRs, READMEs, runbooks
  └── Agent-TestGenerator     Unit, integration, regression, load tests

LAYER 3: MAINTENANCE AGENTS (operate phase)
  ├── Agent-DriftMonitor      PSI scores → alerts → retraining triggers
  ├── Agent-RetrainingAgent   Executes retraining with quality gates
  ├── Agent-CostAuditor       Reviews costs against budget
  └── Agent-DocUpdater        Keeps documentation in sync with code

Read the full file on GitHub · 546 lines

Changes

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 Changed · -1 lines · +12 tokens per session 1d72dc6fbdc1
2mo ago First seen · 547 lines · 8,440 tokens per session scan C 2ca22e64d2cf

Subscribe to this mod's changes

ML-MLOps-Portfolio AGENTS.md is an instructions file published in the GitHub repository DuqueOM/ML-MLOps-Portfolio (7 stars, last pushed yesterday), licensed MIT. It adds 8,452 tokens to every session, about $0.0338 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

MLOps aws docker kubernetes machine-learning mlflow

ML-MLOps-Portfolio AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
ML-MLOps-Portfolio AGENTS.mdDuqueOM/ML-MLOps-Portfolio	Instructions file	7	yesterday	MIT	C	8,452	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

ML-MLOps-Portfolio: Instructions file for Codex