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 JAICHANGPARK/workshop-harness --skill colab-workshop-integratorgit clone --depth 1 https://github.com/JAICHANGPARK/workshop-harnessWrote 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/jaichangpark/workshop-harness/colab-workshop-integrator)<a href="https://agentmods.dev/skills/jaichangpark/workshop-harness/colab-workshop-integrator"><img src="https://agentmods.dev/badge/skills/jaichangpark/workshop-harness/colab-workshop-integrator/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/jaichangpark/workshop-harness/colab-workshop-integrator"><img src="https://agentmods.dev/badge/skills/jaichangpark/workshop-harness/colab-workshop-integrator.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 | $0.00074 | $0.01086 |
| Opus 5 | $0.00037 | $0.00543 |
| Sonnet 5 | $0.00015 | $0.00217 |
| Haiku 4.5 | $0.00007 | $0.00109 |
Grade A, and why
colab-workshop-integrator 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Colab Workshop Integrator Skill
Purpose
Bridges workshop-harness generated workshops with Google Colab and the Google Colab CLI (https://github.com/googlecolab/google-colab-cli). It transforms static workshop markdown docs, lab instructions, and starter/final code into interactive Jupyter Notebooks (.ipynb), injects cloud GPU verification and Secret management cells, inserts "Open in Colab" badges, and enables facilitators to run automated remote headless smoke tests via colab CLI.
🌐 Mandatory Pre-Flight Web Research Protocol: Before generating Colab notebooks, perform live web search (
search_web/workshop-web-researcher) to verify current Colab CUDA driver versions, newest PyTorch / HuggingFace Transformers / Google GenAI SDK syntax, and latest pip package names.
🚀 Key Integration Features
-
Lab to
.ipynbAuto-Generation:- Converts
workshop/03_labs/README.mdand code files (workshop/01_starter,workshop/02_final) into clean, structured Jupyter Notebooks. - Generates two flavors:
workshop_starter.ipynb: Step-by-step guides with starter code cells and attendee TODO prompts.workshop_solution.ipynb: Complete reference solution notebook ready for live demo or fallback.
- Converts
-
Zero-Setup Runtime Preparation Cells:
- Environment & Dependency Injection: Automatically adds top
%pip install -q ...cells based onpyproject.tomlor workshop tech stack. - Hardware Accelerator Verification: Injects GPU runtime verification (
!nvidia-smi/ torch CUDA check) for local LLM (Ollama, vLLM, transformers) labs. - Colab Secrets & Credential Protocol: Injects secure
google.colab.userdataAPI key handling instead of hardcoded keys:# Secure API Key Setup in Colab try: from google.colab import userdata gemini_api_key = userdata.get('GEMINI_API_KEY') except ImportError: import os gemini_api_key = os.environ.get('GEMINI_API_KEY', '')
- Environment & Dependency Injection: Automatically adds top
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 · 93 lines · 74 tokens per session scan A 2ef17c651880
colab-workshop-integrator is a skill published in the GitHub repository JAICHANGPARK/workshop-harness (5 stars, last pushed 7d ago), licensed MIT. It adds 74 tokens to every session and 1,086 once invoked, about $0.0004 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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