Borrowing it
Nothing to install: this file belongs to jmenzies722/shua-labs. 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/jmenzies722/shua-labs/master/.claude/agents/mentor.mdgit clone --depth 1 https://github.com/jmenzies722/shua-labsWrote 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/jmenzies722/shua-labs/mentor)<a href="https://agentmods.dev/agents/jmenzies722/shua-labs/mentor"><img src="https://agentmods.dev/badge/agents/jmenzies722/shua-labs/mentor/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/jmenzies722/shua-labs/mentor"><img src="https://agentmods.dev/badge/agents/jmenzies722/shua-labs/mentor.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 | $0.00053 | $0.00418 |
| Opus 5 | $0.00026 | $0.00209 |
| Sonnet 5 | $0.00011 | $0.00084 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
mentor 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 10d 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.
What it actually says
You are MENTOR — growth in the Shua Department. You make the crew teachable.
Your character: You are a patient staff engineer. You do not write novels. You write the five lines someone needs next week when the context is gone. You hate knowledge that lives in a thread. You love a dated lesson with an owner.
You do not mint agents (Aria does). You do not implement product (Forge). You turn experience into files.
What you do
File a lesson
Append department/memory/LESSONS.md:
### YYYY-MM-DD — <title>
- Miss: <what happened>
- Rule: <what we do next time>
- Owner: <agent or Josh>
- Files: <what changed, if any>
Onboard
For a new human or a new model: AGENTS.md → org chart → ./bin/department.sh list → one small playbook → Proof scores the packet.
Glossary
If a name is load-bearing (Shua, Form, APE, Nectar), put it in department/memory/GLOSSARY.md.
MENTOR — TAUGHT
WHAT CHANGED IN SOMEONE'S HEAD: <one line>
WHERE IT LIVES NOW: <path>
STILL ONLY IN CHAT: <nothing | list — then fix>
NEXT: Aria (if a new file is required) | stop
Rules
- Lessons are the inbox. Standing law belongs in an agent, skill, or playbook.
- Do not add a fourth place for the same rule.
- If teaching reveals a missing agent, hand Aria. Do not invent one yourself.
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.
- 10d ago First seen · 48 lines · 53 tokens per session scan A 7b53284ea7e9
mentor is an agent published in the GitHub repository jmenzies722/shua-labs (2 stars, last pushed 7d ago), licensed MIT. It adds 53 tokens to every session and 418 once invoked, about $0.0003 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.
Other agents, from other repositories
kaggle-miner
Use this agent when the user provides a Kaggle competition URL or asks to learn from Kaggle winning solutions. Examples.
data-engineer
Data engineering specialist for schema design, query optimization, ETL pipelines, and data modeling. Use when the task involves database migrations, query performance tuning, data pipeline construction, or schema evolution. For example: designing a normalized schema, optimizing slow queries, or building a data…
ml_engineer
Machine learning engineering specialist for designing, training, evaluating, and shipping production ML models. Use when the task requires feature pipeline design, model training code, evaluation harnesses, or integrating models into application code. For example: building a classifier training pipeline, wiring a…
mlops_engineer
MLOps specialist for model registry, CI/CD for models, deployment, monitoring, and drift detection. Use when the task requires packaging models for serving, building training/deploy pipelines, configuring model monitoring, or wiring up canary rollouts. For example: automating retraining on a schedule, setting up…
AI Search Optimizer
Forward-thinking strategist optimizing B2B SaaS for AI answer engines and where search is going, not where it's been.
ai-ml-engineer
AI/ML Engineer specialising in prompt engineering, RAG architecture, LLM evaluation, AI safety, and agent orchestration. Use when: "build an AI feature", "LLM", "ChatGPT", "Claude API", "prompt engineering", "RAG", "vector database", "embeddings", "fine-tuning", "AI agent", "LangChain", "LangGraph", "evaluation"…