| name | notebooklm-teacher-workflow |
| description | Use when Codex should consult a fixed NotebookLM notebook before answering paper-backed questions, preserve NotebookLM citations, report no-solution when sources do not support an answer, and then combine the teacher's answer with local coding or analysis work. |
NotebookLM Teacher Workflow
Use this skill when a task depends on paper-backed knowledge stored in a fixed
NotebookLM notebook.
This skill is notebook-agnostic. Bind it to your own teacher notebook by:
- passing
--notebook <alias-or-id> to the bundled script, or
- setting
NLM_TEACHER_NOTEBOOK=<alias-or-id> in the environment
What This Skill Does
- Identifies which parts of the task need source-backed knowledge.
- Queries the fixed NotebookLM teacher before answering from memory.
- Preserves NotebookLM citation markers such as
[1][2].
- Surfaces
no_solution when the notebook cannot support an answer.
- Uses the teacher result to drive local coding, experiments, file search, or
summarization.
Query Workflow
Use the bundled wrapper:
uv run ~/.codex/skills/notebooklm-teacher-workflow/scripts/ask_teacher.py \
--notebook "<NOTEBOOK_ALIAS_OR_ID>" \
--question "What does the notebook say about this method?"
Or set a default notebook once:
export NLM_TEACHER_NOTEBOOK="<NOTEBOOK_ALIAS_OR_ID>"
uv run ~/.codex/skills/notebooklm-teacher-workflow/scripts/ask_teacher.py \
--question "What does the notebook say about this method?"
Reuse --conversation-id only for follow-up questions on the same thread.
Required Rules
- Ask the teacher before answering source-backed questions from memory.
- Keep NotebookLM citation markers unchanged in your own response.
- If the wrapper returns
status=no_solution, say the teacher does not support
the answer and do not fabricate details.
- Do not write notes, code, or experiment results back into NotebookLM.
Output Contract
For research-engineering tasks, prefer this delivery skeleton:
## Teacher Says
- source-backed findings with original citations
## What I Did
- code, commands, experiments, or local findings
## Conclusion
- final answer grounded in teacher citations when relevant
## Not Covered by the Teacher
- unresolved or weakly supported points
Resources
scripts/ask_teacher.py: normalized wrapper around nlm notebook query
references/workflow.md: behavior and failure conditions
references/prompt-template.md: reusable NotebookLM persona template