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 memtomem/memtomem --skill memtomem-searchgit clone --depth 1 https://github.com/memtomem/memtomemWrote 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/memtomem/memtomem/memtomem-search)<a href="https://agentmods.dev/skills/memtomem/memtomem/memtomem-search"><img src="https://agentmods.dev/badge/skills/memtomem/memtomem/memtomem-search/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/memtomem/memtomem/memtomem-search"><img src="https://agentmods.dev/badge/skills/memtomem/memtomem/memtomem-search.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 | $0.00034 | $0.00209 |
| Opus 5 | $0.00017 | $0.00105 |
| Sonnet 5 | $0.00007 | $0.00042 |
| Haiku 4.5 | $0.00003 | $0.00021 |
Grade A, and why
memtomem-search 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 8d 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.
What it actually says
Search memories
Derive the search query from the current user request.
If the request does not clearly specify the search query, ask before calling a tool — and
in a non-interactive context (a subagent or scripted run with nobody to ask), do not
stall and do not guess: stop and report insufficient_input naming the missing search query.
A request that does specify the search query proceeds normally in either context.
Call mem_search with the requested topic and use the compact output unless machine-readable details are necessary.
Present the strongest matches concisely with their source path, heading, and relevance score. Explain that memtomem uses BM25 by default and adds dense retrieval only when embeddings are configured. If nothing matches, suggest a broader query or the status workflow; do not write or index anything automatically.
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.
- 8d ago First seen · 16 lines · 34 tokens per session scan A 9299982943f3
memtomem-search is a skill published in the GitHub repository memtomem/memtomem (13 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 209 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 2026-08-30.
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