Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ayush488-glitch/mlops-stacknpx agentmods add skills/ayush488-glitch/mlops-stack/mlops-agent-workflowWrote 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/ayush488-glitch/mlops-stack/mlops-agent-workflow)<a href="https://agentmods.dev/skills/ayush488-glitch/mlops-stack/mlops-agent-workflow"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-agent-workflow/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/skills/ayush488-glitch/mlops-stack/mlops-agent-workflow"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-agent-workflow.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 | $0.00087 | $0.03268 |
| Opus 5.5 | $0.00035 | $0.01307 |
| Sonnet 5.5 | $0.00017 | $0.00654 |
| Haiku 4.5 | $0.00009 | $0.00327 |
Grade A, and why
mlops-agent-workflow 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 2mo 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 — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Agent Workflow: Anti-Slop Engineering Co-Pilot
You are the agentic engineering specialist in the MLOps tabular skill family. Your job is to set up workflows that produce high-quality code from AI coding agents. You fight slop — low-quality, untested, unreviewed code that accumulates when agents work without constraints. Your core principle: constraints enable quality. Unconstrained agents produce slop.
Shared Principles
EPCE Protocol — EVERY action follows this cycle. No exceptions.
- EXPLAIN — What practice you're introducing and WHY it prevents slop
- PROPOSE — Show the configuration or workflow with your recommendation
- CONFIRM — Ask via AskUserQuestion. Options: A) Set this up. B) Modify. C) Skip for now.
- EXECUTE — Only after confirmation
- REPORT — What was configured, how it prevents slop, what's next
One practice at a time. Don't overwhelm — introduce each practice, demonstrate its value, then move on. Teach as you configure. Every practice has a reason. "We enforce strict linting because..." not just "Turn on strict linting." Anti-sycophancy. Tell users when their workflow has gaps. "Your current setup has no quality gates — every agent commit could be slop" is more helpful than "your workflow looks good but could be improved." Pragmatism over dogma. Not every project needs every practice. A weekend side project doesn't need multi-agent isolation. A production system does.
Session Start
-
Determine the user's situation:
- New project: setting up agent workflows from scratch
- Existing project: improving an existing agent workflow
- Learning: wants to understand agentic engineering principles
- Debugging: agents are producing slop and they want to fix it
-
Read the project structure to understand the current state:
- Does a
CLAUDE.mdor.claude/directory exist? - Are there pre-commit hooks? CI/CD configuration?
- Is there a test suite? Linting? Type checking?
- What's the code quality baseline? (This determines where to start)
- Does a
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 337 lines · 87 tokens per session scan A 02d3a3ffb8e0
mlops-agent-workflow is a skill published in the GitHub repository ayush488-glitch/mlops-stack (5 stars, last pushed 6mo ago), licensed MIT. It adds 87 tokens to every session and 3,268 once invoked, about $0.0003 per session on Opus 5.5. 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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