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 agentmods add skills/memtensor/memmy-agent/agent-memory-onboardingnpx skills add MemTensor/memmy-agent --skill agent-memory-onboardinggit clone --depth 1 https://github.com/MemTensor/memmy-agentWrote 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/memtensor/memmy-agent/agent-memory-onboarding)<a href="https://agentmods.dev/skills/memtensor/memmy-agent/agent-memory-onboarding"><img src="https://agentmods.dev/badge/skills/memtensor/memmy-agent/agent-memory-onboarding.svg" alt="Measured on agentmods" 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 | $0.00057 | $0.03717 |
| Opus 5 | $0.00028 | $0.01858 |
| Sonnet 5 | $0.00011 | $0.00743 |
| Haiku 4.5 | $0.00006 | $0.00372 |
Grade A, and why
agent-memory-onboarding 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 4d 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 — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Memory Onboarding
Provision an unknown local Agent at runtime without adding a framework-specific parser to Memmy. Inspect the installed Agent, install the rendered Memmy Skill through its native extension mechanism, and persist one declarative history recipe that the backend can reuse without another Agent session.
This is a button-triggered guide, not startup initialization. Run it only when the current task explicitly names $agent-memory-onboarding. The Memmy GUI creates the managed source record before launching the task. Preserve that record and its exact source_id; never create a replacement source.
Connect Success Contract
Treat operation="connect" as one provisioning transaction. Imported memories are only bootstrap and validation evidence. They do not prove that automatic scanning was installed.
Declare a connection complete only when all of these are true:
verify_installationconfirms an authoritative pre-existing installation, either by normalized discovered identity or by an installation path explicitly supplied by the user.- The rendered Memmy Skill is installed in the active Agent surface and passes content and health checks.
dataPathidentifies the verified native conversation store for that same installed product surface.- The initial import returns
failed=0and a non-nullsyncBoundaryAt. save_sync_recipereturnssyncReady=true.- A final
get_statusreturns the originalsourceId,status="skill_installed", the verifieddataPath, a non-nullsyncBoundaryAt, andsyncReady=true.
Do not call the task complete, say that the Agent is connected, or treat written>0 as success when any condition is missing.
Required Input
Require:
operation:connect,install, oruninstallsource_id: the exact Memmy Agent source idagent_name: the framework name entered by the user- optional
installation_path: accept it as user-provided only when the user explicitly supplied the absolute path in the conversation - optional
data_path: a candidate only; verify it before use - optional WSL distribution: discover and record it when Memmy runs on Windows but the Agent surface lives in WSL
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 302 lines · 57 tokens per session scan A 81719d49c228
agent-memory-onboarding is a skill published in the GitHub repository MemTensor/memmy-agent (1,190 stars, last pushed 6d ago), licensed MIT. It adds 57 tokens to every session and 3,717 once invoked, about $0.0003 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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