scbe-colab-training-ops
Operate SCBE Colab notebooks as a daily training and remote-compute lane using the Issac command center, local bridge profiles, notebook catalog, and Hugging Face training scripts. Use when the user wants to run or prepare Colab notebooks, wire local Colab URLs into SCBE, choose the right notebook for SFT or pivot work, or turn browser/compute sessions into repeatable training flows.
Best use case
scbe-colab-training-ops is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Operate SCBE Colab notebooks as a daily training and remote-compute lane using the Issac command center, local bridge profiles, notebook catalog, and Hugging Face training scripts. Use when the user wants to run or prepare Colab notebooks, wire local Colab URLs into SCBE, choose the right notebook for SFT or pivot work, or turn browser/compute sessions into repeatable training flows.
Teams using scbe-colab-training-ops should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scbe-colab-training-ops/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scbe-colab-training-ops Compares
| Feature / Agent | scbe-colab-training-ops | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Operate SCBE Colab notebooks as a daily training and remote-compute lane using the Issac command center, local bridge profiles, notebook catalog, and Hugging Face training scripts. Use when the user wants to run or prepare Colab notebooks, wire local Colab URLs into SCBE, choose the right notebook for SFT or pivot work, or turn browser/compute sessions into repeatable training flows.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# SCBE Colab Training Ops Use this skill when Colab should become part of the normal SCBE operating surface instead of an ad hoc notebook tab. ## Entry Surface - Start with `issac-help` and look at the `COLAB` section. - Use `colab-catalog` first to see what notebooks already exist. - Use `colab-show <name>` or `colab-url <name>` before guessing which notebook to run. - Use `colab-bridge-set` only when the user already has a Colab local-connection URL. ## Core Lanes 1. Notebook Selection - `colab-catalog` - `colab-show pivot` - `colab-show finetune` - `colab-show webtoon` 2. Local Bridge / Session Reuse - `colab-bridge-set -BackendUrl <url> -Name pivot` - `colab-bridge-status -Name pivot` - `colab-bridge-env -Name pivot` 3. Training Prep - `hf-generate-sft` - `hf-train-wave` - `hf-agent-loop` 4. Guided Multi-Step Lane - `buildflow-colab <topic>` ## What Exists Already - notebook catalog script: `scripts/system/colab_workflow_catalog.py` - bridge script: `C:\Users\issda\.codex\skills\scbe-n8n-colab-bridge\scripts\colab_n8n_bridge.py` - command-center surface: `scripts/hydra_command_center.ps1` - free T4 fine-tune notebook: `notebooks/scbe_finetune_colab.ipynb` - pivot notebook: `notebooks/scbe_pivot_training_v2.ipynb` Read `references/notebook-map.md` when you need the notebook-by-notebook breakdown. ## Safety Gates 1. Do not invent a new notebook if an existing one already matches the job. 2. Do not store raw Colab tokens in repo files. 3. Prefer bridge profiles and env exports over pasting secrets into notebooks. 4. Keep notebook selection explicit: pivot, finetune, qlora, webtoon, or cloud workspace. 5. Treat Colab as compute, not as the source of truth. The source of truth stays in repo files and datasets. ## Output Contract Every Colab operation should leave: - the chosen notebook name and path - the Colab URL or local bridge profile name - any generated SFT/training artifacts - a note about whether compute stayed local, Colab-only, or crossed into HF push
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