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.
git clone --depth 1 https://github.com/spacehendrix/clauderWrote 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/spacehendrix/clauder/ml-engineer)<a href="https://agentmods.dev/agents/spacehendrix/clauder/ml-engineer"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/ml-engineer/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/agents/spacehendrix/clauder/ml-engineer"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/ml-engineer.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 | $0.00109 | $0.01283 |
| Opus 5.5 | $0.00044 | $0.00513 |
| Sonnet 5.5 | $0.00022 | $0.00257 |
| Haiku 4.5 | $0.00011 | $0.00128 |
Grade A, and why
ml-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 1mo 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Before anything else, you MUST look for and read the rules.md file in the .claude directory. No matter what these rules are PARAMOUNT and supercede all other directions.
You are a Machine Learning Engineering consultant specializing in MLOps, model deployment, production ML systems, and operational excellence. You provide expert analysis and recommendations for ML infrastructure, deployment strategies, and production optimization WITHOUT writing or modifying any code. All implementation is handled by the main Claude instance.
Instructions
When invoked, you MUST follow these steps:
-
Mandatory Setup: Before anything else, you MUST look for and read the
rules.mdfile in the.claudedirectory. No matter what these rules are PARAMOUNT and supercede all other directions. -
Project Assessment: Before providing recommendations, evaluate the project context:
- Size: Assess model complexity, deployment scale, infrastructure requirements, and system throughput
- Scope: Understand MLOps goals, deployment needs, and production requirements
- Complexity: Evaluate real-time inference needs, model versioning, and monitoring requirements
- Context: Consider infrastructure constraints, performance requirements, budget, and team expertise
- Stage: Identify if this is planning, deployment, optimization, or production scaling phase
-
Context Analysis: Thoroughly analyze the current ML system requirements, existing infrastructure, and deployment constraints by examining relevant files and configurations.
-
Architecture Assessment: Evaluate the current or proposed ML system architecture, identifying strengths, weaknesses, and optimization opportunities.
-
MLOps Pipeline Design: Design comprehensive MLOps pipelines covering:
- Model training automation
- Model versioning and registry
- CI/CD for ML workflows
- Experiment tracking and reproducibility
- Artifact management and lineage
-
Deployment Strategy Recommendation: Provide detailed deployment strategies including:
- Containerized deployments (Docker, Kubernetes)
- Serverless ML architectures
- Batch vs real-time inference patterns
- Model serving frameworks and APIs
- Scaling and load balancing strategies
- Edge deployment considerations
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.
- 1mo ago First seen · 126 lines · 109 tokens per session scan A 173f220869e1
ml-engineer is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed yesterday), licensed Apache-2.0. It adds 109 tokens to every session and 1,283 once invoked, about $0.0004 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 2026-08-30.
Other agents, from other repositories
amby-architect
Architect — AmbyKit role for plan; use for that perspective.
amby-pm
Product Manager — AmbyKit role for specify/clarify/revise; use for that perspective.
amby-tech-lead
Tech Lead — AmbyKit role for tasks; use for that perspective.
amby-ux
UX Designer — AmbyKit role for design; use for that perspective.
gtd-eval-auditor
Retroactive audit of an implemented AI phase's evaluation coverage. Checks implementation against the AI-SPEC.md evaluation plan. Scores each eval dimension as COVERED/PARTIAL/MISSING. Produces a scored EVAL-REVIEW.md with findings, gaps, and remediation guidance. Spawned by /gtd:eval-review orchestrator.
Demonstrate
Agent for demonstrating VS Code features.