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.
curl -O https://raw.githubusercontent.com/DuqueOM/ML-MLOps-Portfolio/main/.devin/skills/model-retrain/SKILL.mdgit clone --depth 1 https://github.com/DuqueOM/ML-MLOps-PortfolioWrote 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/duqueom/ml-mlops-portfolio/model-retrain)<a href="https://agentmods.dev/skills/duqueom/ml-mlops-portfolio/model-retrain"><img src="https://agentmods.dev/badge/skills/duqueom/ml-mlops-portfolio/model-retrain.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 5.1 | $0.00014 | $0.01761 |
| Opus 5 | $0.00007 | $0.00881 |
| Sonnet 5 | $0.00003 | $0.00352 |
| Haiku 4.5 | $0.00001 | $0.00176 |
Grade A, and why
model-retrain 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 8d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Retraining
Authorization Protocol
This skill spans three authorization layers, aligned with the Agent Behavior Protocol (AGENTS.md):
| Phase | Mode | What happens |
|---|---|---|
| Training (MLflow run) | AUTO | Agent may run training, log to MLflow, produce artifacts |
Transition to Staging |
CONSULT | Agent presents metrics + quality gates + drift diff; human approves via MLflow UI or /promote-model PR |
Transition to Production |
STOP | Never transitions directly. Opens PR, waits for Tech Lead approval via GitHub Environment production |
Automatic STOP escalation
Even in AUTO phase, escalate to STOP if the new model exhibits any of:
- Primary metric > 0.99 without explanation (D-06 — investigate leakage)
- Fairness DIR in
[0.80, 0.85](marginal — human judgment required) - Metric regression > 5% vs current production
- Any quality gate fails
Emit structured signal:
[AGENT MODE: STOP]
Operation: Model retraining for {service}
Reason: Fairness DIR = 0.82 (marginal, requires human review)
Waiting for: Engineer inspection + either ADR documenting decision OR retraining with fairness-aware loss
Step 1: Validate Retraining Trigger
Before retraining, confirm the trigger:
- Drift alert: PSI ≥ threshold on critical feature (check Prometheus/Grafana)
- Metric degradation: Rolling metric below quality gate (check monitoring)
- Scheduled: Periodic retraining per policy
- Manual: Engineer-initiated (document reason)
Step 2: Download Fresh Data
# Download latest production data
gsutil cp gs://{data-bucket}/{service}/production_data_latest.csv data/raw/
# Or from AWS:
aws s3 cp s3://{data-bucket}/{service}/production_data_latest.csv data/raw/
Step 3: Validate Data Before Training
python -c "
from src.{service}.schemas import ServiceInputSchema
import pandas as pd
import pandera as pa
df = pd.read_csv('data/raw/production_data_latest.csv')
ServiceInputSchema.validate(df)
print(f'Validation passed: {len(df)} rows, {len(df.columns)} columns')
"
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.
- 8d ago First seen · 202 lines · 14 tokens per session scan A 2eba210371c8
model-retrain is a skill published in the GitHub repository DuqueOM/ML-MLOps-Portfolio (5 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 1,761 once invoked, about $0.0001 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 2026-08-31.
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