feat(notebooks): Add Jupytext + Papermill agent workflow demo
- Add parameterized system-health notebook (.py source + .ipynb) - Add Gitea Actions CI workflow for notebook execution smoke test - Add NOTEBOOK_WORKFLOW.md documenting the .py-first approach - Proves end-to-end: agent writes .py -> PR review -> CI executes -> output artifact
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# Notebook Workflow for Agent Tasks
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This directory demonstrates a sovereign, version-controlled workflow for LLM agent tasks using Jupyter notebooks.
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## Philosophy
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- **`.py` files are the source of truth`** — authored and reviewed as plain Python with `# %%` cell markers (via Jupytext)
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- **`.ipynb` files are generated artifacts** — auto-created from `.py` for execution and rich viewing
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- **Papermill parameterizes and executes** — each run produces an output notebook with code, narrative, and results preserved
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- **Output notebooks are audit artifacts** — every execution leaves a permanent, replayable record
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## File Layout
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```
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notebooks/
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agent_task_system_health.py # Source of truth (Jupytext)
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agent_task_system_health.ipynb # Generated from .py
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docs/
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NOTEBOOK_WORKFLOW.md # This document
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.gitea/workflows/
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notebook-ci.yml # CI gate: executes notebooks on PR/push
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```
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## How Agents Work With Notebooks
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1. **Create** — Agent generates a `.py` notebook using `# %% [markdown]` and `# %%` code blocks
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2. **Review** — PR reviewers see clean diffs in Gitea (no JSON noise)
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3. **Generate** — `jupytext --to ipynb` produces the `.ipynb` before merge
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4. **Execute** — Papermill runs the notebook with injected parameters
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5. **Archive** — Output notebook is committed to a `reports/` branch or artifact store
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## Converting Between Formats
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```bash
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# .py -> .ipynb
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jupytext --to ipynb notebooks/agent_task_system_health.py
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# .ipynb -> .py
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jupytext --to py notebooks/agent_task_system_health.ipynb
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# Execute with parameters
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papermill notebooks/agent_task_system_health.ipynb output.ipynb \
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-p threshold 1.0 -p hostname forge-vps-01
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```
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## CI Gate
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The `notebook-ci.yml` workflow executes all notebooks in `notebooks/` on every PR and push, ensuring that checked-in notebooks still run and produce outputs.
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## Why This Matters
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| Problem | Notebook Solution |
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|---|---|
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| Ephemeral agent reasoning | Markdown cells narrate the thought process |
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| Stateless single-turn tools | Stateful cells persist variables across steps |
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| Unreviewable binary artifacts | `.py` source is diffable and PR-friendly |
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| No execution audit trail | Output notebook preserves code + outputs + metadata |
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