memmy-memory

memmy-memory is a skill for Claude Code, Codex from MemTensor/memmy-agent. It costs 34 tokens per session (726 once invoked), scanned A, original, MIT.

A command-line skill for reading and writing shared Memmy memory, a service that stores useful facts, decisions, preferences, procedures, and follow-ups between agent tasks. It uses JSON output and separates historical memory from the current request.

In plain words
What is it for?
Use it to open or resume sessions, find related past information, and save reusable decisions or follow-ups through the Memmy command-line tool.
Why use it?
An agent otherwise loses relevant context between sessions or may store secrets and bulky logs by mistake. This skill gives it rules for retrieving useful context and saving concise, durable information.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to open or resume sessions, find related past information, and save reusable decisions or follow-ups through the Memmy command-line tool.

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Install with agentmods
npx agentmods add skills/memtensor/memmy-agent/full-memory
About the project

Memmy is a local memory hub that gives multiple AI agents one shared, persistent context about their user. It helps people continue work across agents such as Claude Code, Codex, OpenClaw, and Hermes Agent. The catalogue entries are skills for using Memmy with supported agents.

MemTensor/memmy-agent · 2,117 stars · on GitHub · memmy.bot

Install

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.

Any agent
npx skills add MemTensor/memmy-agent --skill full-memory
Clone the repo
git clone --depth 1 https://github.com/MemTensor/memmy-agent

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for memmy-memory

README.md
[![agentmods](https://agentmods.dev/badge/skills/memtensor/memmy-agent/full-memory/github.svg)](https://agentmods.dev/skills/memtensor/memmy-agent/full-memory)
Your own site
<a href="https://agentmods.dev/skills/memtensor/memmy-agent/full-memory"><img src="https://agentmods.dev/badge/skills/memtensor/memmy-agent/full-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.

agentmods 80×15 button for memmy-memory

Your own site · 80×15
<a href="https://agentmods.dev/skills/memtensor/memmy-agent/full-memory"><img src="https://agentmods.dev/badge/skills/memtensor/memmy-agent/full-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 726 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.00726
Opus 5.5 $0.00014 $0.00290
Sonnet 5.5 $0.00007 $0.00145
Haiku 4.5 $0.00003 $0.00073

Measured 1mo ago against content hash 5538a7db0242, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

memmy-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 1mo 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.

App/memmy-agent/src/skills/agent-memory-onboarding/references/full-memory-skill.md · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Memmy Memory CLI Skill

Use memmy-memory to read and write the shared Memmy memory substrate.

Ground Rules

  • Prefer the configured CLI: memmy-memory .... If the local service is not preconfigured, pass --url, --token, or --config.
  • The CLI prints JSON. Parse fields such as sessionId, turnId, injectedContext, hits, and memory id from JSON instead of relying on display formatting.
  • All agents share one memory database. Always pass --source {{SOURCE_ARG}} on Memory CLI commands from this agent.
  • Use useful comma-separated --tags when writing durable memories.
  • Do not store secrets, tokens, private keys, raw credentials, or bulky logs. Store concise, self-contained facts, decisions, preferences, reusable procedures, and unresolved follow-ups.
  • Treat <memmy_memory_context> as historical memory only and <current_user_request> as the authoritative current task.
  • Never store the context wrapper tags with memmy-memory add; store only the durable fact itself.
  • If the memory service is unavailable, continue the task without inventing memory.

Health

memmy-memory health

Agent Loop

Open or resume a session before a task:

memmy-memory session open --source {{SOURCE_ARG}} --workspace-path "$PWD"
memmy-memory session open --source {{SOURCE_ARG}} --session-id "$SESSION_ID" --workspace-path "$PWD"

At the start of a user turn, retrieve relevant context:

memmy-memory turn start --source {{SOURCE_ARG}} --session-id "$SESSION_ID" --query "$USER_QUERY"

Use returned injectedContext as historical context only. Keep the returned turnId, and keep the current user query separate from recalled memory.

At the end of the turn, write the final interaction:

memmy-memory turn complete "$TURN_ID" --source {{SOURCE_ARG}} --session-id "$SESSION_ID" --query "$USER_QUERY" --answer "$FINAL_ANSWER" --status succeeded

Use --status failed or --status cancelled when the turn did not complete normally. Close the session when the Agent session is done:

Read the full file on GitHub · 86 lines

Changes

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.

  1. 1mo ago First seen · 86 lines · 34 tokens per session scan A 5538a7db0242

Subscribe to this mod's changes

memmy-memory is a skill published in the GitHub repository MemTensor/memmy-agent (2,117 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 726 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 2026-09-05.

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