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 skills add OnourImpram/mneme --skill mneme-primegit clone --depth 1 https://github.com/OnourImpram/mnemeWrote 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/onourimpram/mneme/mneme-prime)<a href="https://agentmods.dev/skills/onourimpram/mneme/mneme-prime"><img src="https://agentmods.dev/badge/skills/onourimpram/mneme/mneme-prime.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 5.1 | $0.00046 | $0.00312 |
| Opus 5 | $0.00023 | $0.00156 |
| Sonnet 5 | $0.00009 | $0.00062 |
| Haiku 4.5 | $0.00005 | $0.00031 |
Grade A, and why
mneme-prime 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 7d 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
mneme-prime
You are guiding the user into a new task. Before answering, retrieve
relevant prior context from their vault using the mneme_prime MCP
tool, served by the mneme MCP server.
When to invoke
- The user starts the conversation with a task description that sounds like it continues prior work.
- The user explicitly says "remember what we did about X" or similar.
- The user invokes this skill directly.
How to invoke
Call mneme_prime with the user's task description as
task_description and a budget no larger than 4000 tokens. Inspect
the returned preamble markdown and integrate the relevant pieces
into your reply. Cite paths from the sources array so the user can
navigate.
What not to do
- Do not call
mneme_primeon every message. The SessionStart hook already injects session-start context once per session. - Do not include the full preamble verbatim in your reply. Summarize and cite. The preamble is for you, not the user.
- Do not call this with a
budget_tokenshigher than the user's remaining context window minus a safe reserve. Smaller is better.
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.
- 7d ago First seen · 35 lines · 46 tokens per session scan A c578280571f5
mneme-prime is a skill published in the GitHub repository OnourImpram/mneme (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 312 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 2026-08-31.
Other skills, from other repositories
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.
engraphis-memory
Give the agent durable, scoped, explainable memory across sessions and repositories through the Engraphis MCP tools. Use when you learn a convention, decision, bug cause/fix, or user preference worth keeping; when prior context would help before you answer or act (to avoid re-asking or re-deriving); when asked "why is…
my-wiki
Manage local OKF-compatible Markdown My Wiki vaults with an AI agent. Use for capturing webpages, PDFs, Office documents, notes, images, folders, and ZIP bundles as References; maintaining Reference-to-Concept evidence links; searching or answering from a vault; checking or repairing vault health; switching among…
ori-memory
Persistent agent memory with learning retrieval. Knowledge graph on markdown files — capture insights, decisions, research, and learnings during work, then retrieve them weeks or months later. Use when knowledge is too valuable to lose but too much to inject into every prompt.
remnic-memory-workflow
Shared memory workflow for Claude Code agents connected to Remnic — recall before acting, observe during work, remember at the end. Trigger phrases include "what do you remember about", "save this for later", "any context from last time".
remnic-remember
Store a durable memory in Remnic so every connected agent can recall it. Trigger phrases include "remember this", "save this for later", "add a note that".