Claude-Mem, now presented as Grok Mem, records an agent's work, compresses it with AI, and brings relevant notes into later sessions so the agent can remember decisions and next steps. It is intended for persistent context across agent conversations and supports multiple coding-agent environments. The catalogue add-ons provide the workflows and integrations used to operate this memory system.
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 thedotmack/claude-mem --skill wowerpointgit clone --depth 1 https://github.com/thedotmack/claude-memWrote 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/thedotmack/claude-mem/wowerpoint)<a href="https://agentmods.dev/skills/thedotmack/claude-mem/wowerpoint"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/wowerpoint/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/thedotmack/claude-mem/wowerpoint"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/wowerpoint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00053 | $0.00938 |
| Opus 5.5 | $0.00021 | $0.00375 |
| Sonnet 5.5 | $0.00011 | $0.00188 |
| Haiku 4.5 | $0.00005 | $0.00094 |
Grade A, and why
wowerpoint 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 4d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wowerpoint
One doc in, one deck out. Slide-deck only — videos and podcasts from the same engine are noticeably worse and out of scope; refer the user to the notebooklm CLI directly if they want those.
Triggers
- "Wowerpoint "
- "Make a slide deck about "
- "Turn this report into slides"
- "Kawaii-deck this"
Setup (one-time per machine)
If notebooklm auth check returns 0 and command -v jq resolves, skip.
uv tool install --with playwright --force notebooklm-py
$(uv tool dir)/notebooklm-py/bin/playwright install chromium
jq is required by the workflow's JSON parsing; install if missing (brew install jq on macOS, or your distro's package manager).
Then the user authenticates interactively — do not script. Tell them to type ! notebooklm login so the OAuth ENTER lands in their terminal.
Workflow
1. The source doc
You need exactly one source doc. If it doesn't exist or is too thin to carry a deck, write it first — use mem-search and sequential thinking to make it comprehensive (long-form, narrative, several thousand words is normal). Do not paper over a weak source by adding more sources.
2. Auth pre-flight
notebooklm auth check 2>&1 | tail -5
Exit 1 with Run 'notebooklm login' to authenticate. = halt and tell the user.
3. Create notebook, add the source
NOTEBOOK_ID=$(notebooklm create "<title>" --json | jq -r .notebook.id)
notebooklm source add "<doc-path>" --notebook "$NOTEBOOK_ID" --json
Title: H1 of the source doc, or its filename stem; append a date for dated work.
create returns the id at .notebook.id; a wrong key gives an empty string and a silent downstream failure.
4. Generate the deck
notebooklm generate slide-deck "<PROMPT>" --format detailed --length default --notebook "$NOTEBOOK_ID" --json --retry 2
5. End your turn
Print the notebook URL:
https://notebooklm.google.com/notebook/<NOTEBOOK_ID>
NotebookLM sends the user a notification when the deck is ready.
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.
- 4d ago Changed · -33 lines · +2 tokens per session 38884c17fc7b
- 10d ago Changed · -78 lines · -4 tokens per session 1a3ab069d7f5
- 1mo ago First seen · 206 lines · 55 tokens per session scan A 0ed6e8855448
wowerpoint is a skill published in the GitHub repository thedotmack/claude-mem (102,560 stars, last pushed today), licensed Apache-2.0. It adds 53 tokens to every session and 938 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
PDF files: create, read, merge, fill, OCR, edit text.
save-as-pdf
Reformat the current HTML design into a paginated, paper-ready PDF. The "Instant" export already gives the user a PDF at the design's native pixel size — this path is for when they want real pages.
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and…
docs-artifact
Use when creating, editing, or reviewing a document for the user (Word, Google Docs, or a document delivered as PDF), or when the task calls for a standalone written deliverable such as a memo, report, proposal, or brief. Also use to answer questions that require inspecting an existing document; not for a short chat…
form-filling
Fill out online forms (Google Forms, Typeform and similar) or PDF, Word and DocuSign forms; prepare a reviewable draft and get approval before submitting or sending.
hive.pdf
Read, write, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python (pypdf, pdfplumber, reportlab, pypdfium2) and command-line tools (poppler-utils, qpdf). Use when the user asks to extract text/tables/images from a PDF, create or modify a PDF, combine or split PDFs, OCR a scanned PDF…