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 recallgit 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/recall)<a href="https://agentmods.dev/skills/memtomem/memtomem/recall"><img src="https://agentmods.dev/badge/skills/memtomem/memtomem/recall/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/recall"><img src="https://agentmods.dev/badge/skills/memtomem/memtomem/recall.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 | $0.00033 | $0.00273 |
| Opus 5.5 | $0.00013 | $0.00109 |
| Sonnet 5.5 | $0.00007 | $0.00055 |
| Haiku 4.5 | $0.00003 | $0.00027 |
Grade A, and why
recall 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 21d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- memtomem-recall — 89% identical, 6 lines differ
What it actually says
Recall memories
Use $ARGUMENTS as the date range or recent-memory request.
If the request does not clearly specify the date range or recent-memory request, 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 date range or recent-memory request.
A request that does specify the date range or recent-memory request proceeds normally in either context.
Translate relative dates such as “last week” into since and until values when possible, then call mem_recall. Preserve any source, namespace, scope, or result-limit constraint from the request.
Present memories newest first with their creation date and source. Ask for a date range only when the request provides no usable temporal constraint. Use topic search, not recall, when the user is asking what a memory says rather than when it was created.
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.
- 21d ago Changed c45cb3e74a94
- 1mo ago First seen · 18 lines · 33 tokens per session scan A b0a6bd4b3133
recall is a skill published in the GitHub repository memtomem/memtomem (19 stars, last pushed today), licensed Apache-2.0. It adds 33 tokens to every session and 273 once invoked, about $0.0001 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
openclaw-memory-tencentdb-setup
A setup guide for installing, configuring, and checking a TencentDB memory plugin in OpenClaw, an agent environment.
cortex-distill
A skill for turning raw session records into organised notes and project documents. It uses a personal vault, which is a folder for storing and retrieving knowledge.
cortex-broadcast
Propagate insights from a distilled Raw into related existing Notes/Projects pages through conversational per-page edits.
cortex-query
A search tool for finding and retrieving information from Cortex, an external personal memory made up of stored notes and project pages.
cortex-evolve
A tool that saves technical notes and project decisions to a Cortex vault, which is an Obsidian note collection stored as files.
using-cortex
A guide for deciding when to search a Cortex vault, the user's personal store of notes, projects, weekly reports, and session records. It sends searches through the Cortex query process when the request refers to earlier work or an explicitly listed topic.