wrapup

An end-of-session rule that summarizes completed work, decisions, lessons, and unfinished items, then saves the record to the project's NotebookLM notebook. NotebookLM is Google's tool for asking questions about information stored in notebooks.

In plain words
What is it for?
Use it to create a dated session log and add it to the project's configured NotebookLM notebook when you say “wrap up” or use @wrapup.
Why use it?
It keeps useful project context from being lost when a coding session ends and makes it available for later reference.

Cursor rule

Install

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.

agentmods
npx agentmods add rules/ibaifernandez/notebooklm-skill/wrapup
Clone the repo
git clone --depth 1 https://github.com/ibaifernandez/notebooklm-skill
Per session 45 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 433 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00045 $0.00433
Opus 5 $0.00023 $0.00217
Sonnet 5 $0.00009 $0.00087
Haiku 4.5 $0.00005 $0.00043

Measured 2d ago against content hash ba952ecf754b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

wrapup 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 2d 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.

adapters/cursor/wrapup.mdc · 81 lines

What it actually says

Session Wrap-Up

Runs at end of session to capture decisions, save context, and push a log to NotebookLM.


Step 1 — Find target notebook

Check for project notebook:

cat "$(pwd)/.notebooklm/config.json" 2>/dev/null

If found, use that notebook ID. Otherwise check ~/.notebooklm/registry.json for the current path. If neither exists, ask the user to run @notebooklm init first.


Step 2 — Review the session

Look back through the conversation and identify:

  • Work completed — what was built, fixed, or configured
  • Decisions made — what was decided and why
  • Key learnings — non-obvious insights
  • Open threads — unfinished items for next session

Step 3 — Write session log

Create a markdown file at .notebooklm/sessions/session-YYYY-MM-DD.md:

# Session Summary — YYYY-MM-DD

**Project:** <project name>

## What We Did
- ...

## Decisions Made
- ...

## Key Learnings
- ...

## Open Threads
- ...

## Tools & Systems Touched
- ...

Step 4 — Push to NotebookLM

NOTEBOOK_ID=$(python3 -c "import json,pathlib; print(json.loads(pathlib.Path('.notebooklm/config.json').read_text())['notebook_id'])")
notebooklm source add .notebooklm/sessions/session-YYYY-MM-DD.md --notebook "$NOTEBOOK_ID"

Step 5 — Confirm

Tell the user: what was captured, where it was saved, and any open threads to pick up next time. One short paragraph.

Changes

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.

  1. 2d ago First seen · 81 lines · 45 tokens per session scan A ba952ecf754b

Subscribe to this mod's changes

wrapup is a cursor rule published in the GitHub repository ibaifernandez/notebooklm-skill (5 stars, last pushed 4mo ago), licensed MIT. It adds 45 tokens to every session and 433 once invoked, about $0.0002 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.