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/evermind-ai/everme/memory-mcpnpx skills add EverMind-AI/EverMe --skill memory-mcpgit clone --depth 1 https://github.com/EverMind-AI/EverMeWrote 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/evermind-ai/everme/memory-mcp)<a href="https://agentmods.dev/skills/evermind-ai/everme/memory-mcp"><img src="https://agentmods.dev/badge/skills/evermind-ai/everme/memory-mcp.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 | $0.00083 | $0.01118 |
| Opus 5 | $0.00042 | $0.00559 |
| Sonnet 5 | $0.00017 | $0.00224 |
| Haiku 4.5 | $0.00008 | $0.00112 |
Grade A, and why
everme-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 5d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What it gives you
When this plugin is loaded, the host has these MCP tools available:
| tool | input | output (markdown / JSON) |
|---|---|---|
mem_context |
{ forceRefresh? } (query deprecated/ignored) |
markdown Profile snapshot — Profile ONLY, never a search |
mem_search |
{ query, topK? } |
markdown search results across all memory buckets |
mem_save_fact |
{ fact | messages, sessionKey?, flush? } |
{ saved, accepted, status, extracted, profileUpdated, … } |
mem_save_turn |
{ role, text | messages, sessionKey?, toolCallId?, flush? } |
{ saved, accepted, status, messageCount, flushed, profileStatus, profileUpdated } |
Recommended usage
Call these tools autonomously — the moment a trigger fires, not only
when the user explicitly asks you to "remember" or "recall". Each
tool's MCP description also carries its trigger, so hosts that don't
surface the server instructions still get the same guidance.
Dedupe with native injection first. Some hosts inject
<everme_profile> at session start and <everme_recall> before each
prompt via native hooks. When a non-empty, relevant block is already in
your context, do not fetch the same data again through these tools; when
the block is missing or clearly unrelated and the task depends on
history, call the tools proactively.
At the start of a session — mem_context
If no <everme_profile> block was injected, call mem_context once
before answering the first user message. It returns the user's durable
Profile ONLY — no semantic search, no episodes, no raw transcript. Call
it once per session; pass forceRefresh: true only when the user
explicitly asks to refresh. Never use it as a fallback for recalling
past decisions or task context — that is mem_search's job.
What ships with it
12 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.
- bin/mcp-http-server.js 102 B runs code
- bin/mcp-server.js 1.9 KB runs code
- LICENSE 11 KB
- package.json 1.4 KB
- README.md 5.6 KB
- src/http-server.js 13 KB runs code
- src/mcp.js 38 KB runs code
- tests/http-server.test.js 28 KB runs code
- tests/instructions.test.js 3.6 KB runs code
- tests/mcp.test.js 2.5 KB runs code
- tests/resources.test.js 36 KB runs code
- tests/schema.test.js 7.0 KB runs code
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.
- 5d ago First seen · 96 lines · 83 tokens per session scan A 9f1587ac83dd
everme-memory is a skill published in the GitHub repository EverMind-AI/EverMe (58 stars, last pushed yesterday), licensed Apache-2.0. It adds 83 tokens to every session and 1,118 once invoked, about $0.0004 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.
Other skills, from other repositories
dashboard
Open OwnMem Console, the local dashboard for this repository's memory. Use when the user asks to open the dashboard, see memory metrics, check adoption or recall quality, or set up the optional embedding lane. Requires a repository initialized with the dashboard layer.
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
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.
cargo-content
Manage the knowledge a Cargo workspace holds — upload files (PDF, CSV, text), rename and organize them, and build native or connector-backed libraries that sync from an external source, so agents can retrieve them (RAG). Triggers: "upload this PDF", "add these docs as knowledge", "build a knowledge base", "sync our…
immortal-skill
通用数字永生框架：从聊天记录、社交媒体、文档等多平台数据中蒸馏任何人的数字分身——支持自己、同事、导师、亲人、伴侣/前任、朋友、公众人物 7 种角色模板，接入国内外 12+ 数据平台。.
chen-yun-demo
蒸馏陈韵的全维度数字分身：产品设计方法、沟通风格、人生经历与价值观。仅用于个人备份与辅助回忆，非对外冒充。.