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.
git clone --depth 1 https://github.com/JHdehao/gemini-mem-v3Wrote 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/commands/jhdehao/gemini-mem-v3/mem-prune)<a href="https://agentmods.dev/commands/jhdehao/gemini-mem-v3/mem-prune"><img src="https://agentmods.dev/badge/commands/jhdehao/gemini-mem-v3/mem-prune/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/commands/jhdehao/gemini-mem-v3/mem-prune"><img src="https://agentmods.dev/badge/commands/jhdehao/gemini-mem-v3/mem-prune.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.00042 |
| Opus 5 | $0.00000 | $0.00021 |
| Sonnet 5 | $0.00000 | $0.00008 |
| Haiku 4.5 | $0.00000 | $0.00004 |
Grade A, and why
mem-prune 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 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.
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.
This is a copy
100% identical to mem-prune — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/mem-prune [maxAgeDays] [importanceFloor]
Removes old low-importance memories.
Equivalent command:
npm run mem:prune -- 30 2
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 · 10 lines · 0 tokens per session scan A f3f0afb9f2c2
mem-prune is a command published in the GitHub repository JHdehao/gemini-mem-v3 (1 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 42 tokens. A static security scan graded it A with 0 findings. It is 100% identical to mem-prune, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
weekly
Weekly memory report — facts learned, procedures, repeated mistakes prevented.
forget
Delete a memory permanently. Asks for confirmation first.
graph
Show memories related to a given one — knowledge-graph traversal up to 3 hops.
export
GDPR Article 20 — export all memories you own as portable JSON.
memory-search
Search agent memory + learned patterns for cross-session context relevant to $ARGUMENTS.
save-memory
You are saving what you learned this session into the megamemory knowledge graph. This is YOUR memory — record anything valuable for future sessions: what you understood about the project, what you built, decisions that were made, patterns you noticed, or intent the user shared.