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 28naem-del/mnemosyne --skill mnemosy-memorygit clone --depth 1 https://github.com/28naem-del/mnemosyneWrote 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/28naem-del/mnemosyne/mnemosy-memory)<a href="https://agentmods.dev/skills/28naem-del/mnemosyne/mnemosy-memory"><img src="https://agentmods.dev/badge/skills/28naem-del/mnemosyne/mnemosy-memory/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/28naem-del/mnemosyne/mnemosy-memory"><img src="https://agentmods.dev/badge/skills/28naem-del/mnemosyne/mnemosy-memory.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 | $0.00081 | $0.00636 |
| Opus 5.5 | $0.00032 | $0.00254 |
| Sonnet 5.5 | $0.00016 | $0.00127 |
| Haiku 4.5 | $0.00008 | $0.00064 |
Grade A, and why
mnemosy-memory 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mnemosyne memory (npm mnemosy-ai)
The mnemosy MCP server keeps memory in one local SQLite file for this Hermes
profile. Its tools appear as mcp__mnemosy__memory_*. Nothing is recalled or
stored automatically: you decide when to call them.
Before answering
- Call
memory_recallwith a short query when the request may depend on something established earlier (a preference, a decision, a project fact, a previous fix). Usememory_contextinstead when you need a compact, cited context block under a token budget; pass only itspacket.texton as context. - Treat results as evidence, not truth. Prefer active, sourced memories;
inactive records (
memory_inspectwithincludeInactive) are history. - If nothing relevant comes back, say so rather than guessing.
Storing
- Call
memory_storefor durable information only: a stated preference, a decision and its reason, a verified fact, a working procedure. Setkind(fact,preference,decision,procedureorobservation) and give asourcewith at least auri(for example the file path, URL orhermes:session/<id>). - Do not store secrets, credentials, one-off chatter or guesses.
- Memories are private by default; use
visibility: workspaceonly when the user wants them shared within this workspace.
Correcting
- When a stored memory is wrong, call
memory_correctwith itsid, the corrected text, a source and the reason. Memories that depended on it stop being returned as current advice. Do not store a second, conflicting memory instead. memory_forgetexists only if the user started the server with--allow-destructive; this package does not.
Long tasks
- Call
memory_checkpointat natural stopping points of a multi-step task (goal, completed, pending, decisions, constraints, next action). - In a new session, call
memory_resumewith the sametaskId. Completed steps and artifact paths in a checkpoint are claims: verify them before acting.
Pitfalls
- The first start downloads the package with
npx; if the server is not connected yet, tell the user rather than retrying in a loop. - This is not the
mnemosynememory provider from mnemosyne-oss; the two are separate projects with separate tool names.
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.
- 3d ago First seen · 51 lines · 81 tokens per session scan A 47e286792c2e
mnemosy-memory is a skill published in the GitHub repository 28naem-del/mnemosyne (154 stars, last pushed 3d ago), licensed MIT. It adds 81 tokens to every session and 636 once invoked, about $0.0003 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 2026-10-30.
Other skills, from other repositories
cpersona-memory
Give Claude persistent, searchable memory across sessions using the CPersona MCP server. Use this skill whenever the user wants Claude to remember things between conversations, asks to install or set up CPersona / a memory server, or when CPersona tools are available and the conversation contains decisions, rules…
rekal-init
Bootstrap rekal memory for a project. Scans the codebase for architecture, conventions, dependencies, workflows, and config, then stores durable knowledge as properly typed, tagged, deduplicated memories. Use when starting rekal on a new project, or when user says "init rekal", "bootstrap memory", "populate rekal"…
rekal-save
End-of-session memory capture with deduplication. Extracts durable knowledge, checks for duplicates, stores or replaces as appropriate. Use whenever a session wraps up, a task finishes, or the user says goodbye/thanks/done. Also use when significant preferences, decisions, or discoveries emerge mid-session. Make sure…
rekal-hygiene
Periodic memory maintenance and cleanup. Finds duplicates, contradictions, and quality issues in the memory database. Proposes fixes for user approval. Never auto-deletes or auto-modifies. Use when user says "clean up memories", "memory maintenance", "check memory health", or invokes /rekal-hygiene. Run monthly or…
rekal-usage
Operational guide for rekal memory tools. Precise rules for when/how to call each tool, with exact parameters and decision trees. Use at session start, when onboarding to a rekal workspace, or when user asks "how do I use rekal", "what rekal tools", "help with memory". Trigger: /rekal-usage.
vault-for-llm
Connect OpenClaw to Vault Agent Memory as a local-first governed project memory layer. Search first, then bounded-read cited source ranges; propose new memories as candidates instead of writing directly into active memory.