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-toolsnpx skills add EverMind-AI/EverMe --skill memory-toolsgit clone --depth 1 https://github.com/EverMind-AI/EverMeWhat 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 | $0.00071 | $0.00727 |
| Opus 5 | $0.00036 | $0.00364 |
| Sonnet 5 | $0.00014 | $0.00145 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
memory-tools 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 2d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EverMe Memory Tools
You have four MCP tools (from the everme-memory MCP server) for memory EverMe persists across past Kimi Code sessions.
Recall:
mem_search— semantic + keyword hybrid search over the user's memory store (episodic, profile, agent cases/skills, recent raw transcript). Rows under "Recent unextracted transcript" are provisional, not established facts.mem_context— the user's durable Profile snapshot ONLY. It never searches and never returns episodes; do not use it to recall past decisions or task context.
Write:
mem_save_fact— save a durable user fact (preference, habit, trait, long-term decision). Call it proactively the moment the user states one — do NOT wait for the user to say "remember this". Onlyextracted: true/profileUpdated: truein the result means the profile really updated; onno_extractionsay so plainly, do not auto-retry, and do not claim success.mem_save_turn— persist a complete task trajectory worth reusing (rarely needed given the automatic SessionEnd write). Chat-dual-write backends may also update the user's Profile; checkprofileUpdated. Usemem_save_factfor a deliberate durable fact.
The plugin's UserPromptSubmit hook already injects relevant memory automatically before each prompt (wrapped in <everme_recall>...</everme_recall>); native hooks also save the session automatically. When native recall exists and is relevant, do not duplicate it; when it is missing and the task depends on history, call the tools proactively.
When to use these tools
Do call when:
- The
<everme_recall>block is missing, empty, or clearly unrelated AND the user references something discussed before ("last time", "remember when", "we decided to use X", "continue where we left off") - The user asks about a project pattern, decision, or convention you have no inline context for
- You're debugging an error message that may have been seen + resolved before
- The user explicitly asks you to "search my memory" / "recall" / "look up"
- The user states a durable fact about themselves — call
mem_save_facteven without being asked
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.
- 2d ago First seen · 43 lines · 71 tokens per session scan A 06ad983e27df
memory-tools is a skill published in the GitHub repository EverMind-AI/EverMe (57 stars, last pushed 5d ago), licensed Apache-2.0. It adds 71 tokens to every session and 727 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
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.
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.
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
Router for the Cargo CLI skill bundle — load first for anything Cargo, and whenever a task spans two Cargo domains. Explains what each skill owns, declarative workspace-as-code (cargo-cdk) vs the imperative CLI, the UUID and slug flow between skills, async polling of runs and batches, end-to-end use cases, and the…
cargo-orchestration
Make Cargo actually run something, or show what it would run — execute one connector action, run a multi-step workflow, trigger a batch across a whole segment or model, message an AI agent, build or edit a node graph, draw a workflow, tool or play as a diagram, and query the runtime tables (runs, batches, spans…
cargo-ai
Build and configure AI agents inside Cargo — create an agent, choose its model and temperature, write its prompt, attach knowledge for retrieval (RAG), connect MCP tool servers, manage memories, and deploy releases. Triggers: "create an agent", "make an agent that", "give the agent our docs", "attach this knowledge…