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.svg" alt="Measured on agentmods" 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 | $0.00055 | $0.02350 |
| Opus 5 | $0.00028 | $0.01175 |
| Sonnet 5 | $0.00011 | $0.00470 |
| Haiku 4.5 | $0.00006 | $0.00235 |
Grade A, and why
wowerpoint scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
UPLOAD_JSON=$(curl -sS --connect-timeout 10 --max-time 30 -X POST "$WOWERPOINT_API_BASE/api/decks" \ How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wowerpoint
One doc in, one PDF 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)
SOURCE_ID=$(notebooklm source add "<doc-path>" --notebook "$NOTEBOOK_ID" --json | jq -r .source.id)
Title: H1 of the source doc, or its filename stem; append a date for dated work.
JSON envelope keys differ — create → .notebook.id, source add → .source.id, generate → .task_id. Wrong key = empty string = silent downstream failure.
4. Spawn the subagent
Generation takes ~10 minutes; never block on it. Use the template below with run_in_background: true.
5. End your turn
Print the notebook URL so the user can watch live:
https://notebooklm.google.com/notebook/<NOTEBOOK_ID>
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.
- 9d ago First seen · 206 lines · 55 tokens per session scan A 0ed6e8855448
wowerpoint is a skill published in the GitHub repository thedotmack/claude-mem (93,434 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 2,350 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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