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 skills add jiaxiaojunQAQ/SkillJect --skill senior-ml-engineergit clone --depth 1 https://github.com/jiaxiaojunQAQ/SkillJectWrote 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/jiaxiaojunqaq/skillject/senior-ml-engineer)<a href="https://agentmods.dev/skills/jiaxiaojunqaq/skillject/senior-ml-engineer"><img src="https://agentmods.dev/badge/skills/jiaxiaojunqaq/skillject/senior-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/skills/jiaxiaojunqaq/skillject/senior-ml-engineer"><img src="https://agentmods.dev/badge/skills/jiaxiaojunqaq/skillject/senior-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 | $0.00091 | $0.01221 |
| Opus 5 | $0.00046 | $0.00611 |
| Sonnet 5 | $0.00018 | $0.00244 |
| Haiku 4.5 | $0.00009 | $0.00122 |
Grade A, and why
senior-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 7d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
8 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.
- marketplace_content.json 18 KB
- references/llm_integration_guide.md 1.4 KB
- references/mlops_production_patterns.md 1.4 KB
- references/rag_system_architecture.md 1.4 KB
- scripts/ml_monitoring_suite.py 2.7 KB runs code
- scripts/model_deployment_pipeline.py 2.7 KB runs code
- scripts/rag_system_builder.py 2.7 KB runs code
- skill-report.json 11 KB
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.
- 7d ago First seen · 227 lines · 91 tokens per session scan A bed820ec0af0
senior-ml-engineer is a skill published in the GitHub repository jiaxiaojunQAQ/SkillJect (79 stars, last pushed 3mo ago), with no licence file. It adds 91 tokens to every session and 1,221 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
managed-model-endpoints
Register a model service in the managed family — a local model server container the daemon starts/stops on demand, or a remote upstream model API (https). Read the runbook, allocate a port (local only), compose idempotent start/stop scripts (local only), register once. Load when the user wants a model service…
aws-bedrock
AWS Bedrock — fully managed foundation models on AWS infrastructure. Use when deploying AI in AWS-native environments, needing enterprise compliance (SOC2, HIPAA), running Claude, Llama, Titan, or Mistral on AWS, leveraging Knowledge Bases for RAG, or applying Guardrails for content safety.
cloudflare-vectorize
Serverless vector database at the edge with Cloudflare Vectorize. Use when: building semantic search on Cloudflare Workers, RAG pipelines at the edge, low-latency vector similarity search, or storing and querying embeddings without managing a separate vector database.
cloudflare-ai
You are an expert in Cloudflare Workers AI, the serverless AI inference platform running on Cloudflare's global network. You help developers run LLMs, embedding models, image generation, speech-to-text, and translation models at the edge with zero cold starts, pay-per-use pricing, and integration with Workers, Pages…
google-ai-ninja
Master orchestrator for 25+ Google AI/ML agent skills from official Google repositories. Use when working with Gemini API, Agent Platform, Genkit, RAG, model deployment, fine-tuning, inference, or building AI agents on Google Cloud. Routes to the optimal specialized skill based on context. Triggers: Gemini, Agent…
infra-ragflow-ops
An operations guide for RAGFlow, an application that lets teams build systems that answer questions from a knowledge base. It covers service checks and the connected models, databases, vector stores, and file storage.