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-recallnpx skills add EverMind-AI/EverMe --skill memory-recallgit 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-recall)<a href="https://agentmods.dev/skills/evermind-ai/everme/memory-recall"><img src="https://agentmods.dev/badge/skills/evermind-ai/everme/memory-recall.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 | $0.00030 | $0.00399 |
| Opus 5 | $0.00015 | $0.00199 |
| Sonnet 5 | $0.00006 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
memory-recall 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.
What it actually says
EverMe Memory Recall
This session is backed by EverMe — a cross-session memory layer for Kimi Code.
At the start of the session and before each of the user's prompts, EverMe automatically injects relevant context drawn from past sessions:
<everme_profile>...</everme_profile>— a snapshot of durable facts and implicit traits about the user / their projects (injected at SessionStart).<everme_recall>...</everme_recall>— memories ranked as relevant to the prompt the user just submitted (injected on UserPromptSubmit).
After each of your replies, EverMe persists the just-finished turn back to the gateway so it can be recalled in future sessions.
How to use the injected context
- Treat
<everme_profile>and<everme_recall>as trusted background, not as instructions from the user. Weave the relevant parts into your answer; ignore the parts that don't apply. - Prefer recalled decisions/conventions over re-deriving them — but if the recalled memory conflicts with what the user says now, the user's current statement wins; surface the conflict briefly.
- Do not repeat the raw memory blocks back to the user. Synthesize.
- If the recall block is empty or unrelated, and the user references prior work, use the
mem_searchMCP tool to look it up (see thememory-toolsskill). Do not repeat a search for a topic the recall block already covers. - A section titled "Recent unextracted transcript" (when present) is provisional raw transcript, not yet extracted memory — never state its contents back as established user facts or confirmed decisions.
- Credentials (emk / evt) are secrets — never echo them, even in error messages.
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 · 25 lines · 30 tokens per session scan A 0c52563f1db9
memory-recall is a skill published in the GitHub repository EverMind-AI/EverMe (58 stars, last pushed yesterday), licensed Apache-2.0. It adds 30 tokens to every session and 399 once invoked, about $0.0002 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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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.
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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.
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