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 mnemopay/mnemopay-sdk --skill recallgit clone --depth 1 https://github.com/mnemopay/mnemopay-sdkWrote 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/mnemopay/mnemopay-sdk/recall)<a href="https://agentmods.dev/skills/mnemopay/mnemopay-sdk/recall"><img src="https://agentmods.dev/badge/skills/mnemopay/mnemopay-sdk/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/mnemopay/mnemopay-sdk/recall"><img src="https://agentmods.dev/badge/skills/mnemopay/mnemopay-sdk/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 | $0.00031 | $0.00138 |
| Opus 5 | $0.00015 | $0.00069 |
| Sonnet 5 | $0.00006 | $0.00028 |
| Haiku 4.5 | $0.00003 | $0.00014 |
Grade A, and why
recall scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Recall
Use the MnemoPay MCP recall tool to search persistent memory.
When the user asks to recall or find stored information:
- Call
recallwith the query from "$ARGUMENTS" - Present the matching memories with their timestamps and tags
- If no matches found, suggest the user store the information first with
/mnemopay:remember
You can also use memory_integrity_check to verify the hash chain hasn't been tampered with.
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 · 16 lines · 31 tokens per session scan A c46326356284
recall is a skill published in the GitHub repository mnemopay/mnemopay-sdk (8 stars, last pushed 21d ago), licensed Apache-2.0. It adds 31 tokens to every session and 138 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 2026-08-31.
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memanto-companion
Inspect and manage the cross-session engineering memory that Memanto maintains for your Claude Code skills. Use when the user asks what Memanto remembers, wants to see their engineering profile, manually recall context for a skill, or store a decision. The automatic lifecycle hooks handle capture/injection on their…
system-monitor
Monitor system health: CPU, memory, disk, processes, and network. Alert on thresholds via any channel.
mnemon
Persistent memory CLI for LLM agents. Store facts, recall past knowledge, link related memories, manage lifecycle.
mnemon
Persistent memory for MiniMax Code. Recall durable context, store important facts and decisions, and link related memories with the mnemon CLI.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.