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 eleboucher/memini --skill memorygit clone --depth 1 https://github.com/eleboucher/meminiWrote 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/eleboucher/memini/memory)<a href="https://agentmods.dev/skills/eleboucher/memini/memory"><img src="https://agentmods.dev/badge/skills/eleboucher/memini/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.
<a href="https://agentmods.dev/skills/eleboucher/memini/memory"><img src="https://agentmods.dev/badge/skills/eleboucher/memini/memory.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00056 | $0.02019 |
| Opus 5.5 | $0.00022 | $0.00808 |
| Sonnet 5.5 | $0.00011 | $0.00404 |
| Haiku 4.5 | $0.00006 | $0.00202 |
Grade A, and why
memory scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
and they resolve the namespace for you. The curl recipes here are the fallback The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 1mo ago First seen · 183 lines · 56 tokens per session scan A 1f4747d28edb
memory is a skill published in the GitHub repository eleboucher/memini (25 stars, last pushed 3d ago), licensed AGPL-3.0. It adds 56 tokens to every session and 2,019 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). 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 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.
mind-mem
User manual for the mind-mem persistent memory CLI (mm). Use when an agent needs to recall prior decisions, tasks or project facts from a mind-mem workspace, pack memory into context, check workspace health, review governed proposals, install or troubleshoot mind-mem, or answer questions about how mind-mem works …
memory-recall
Search across all structured memory files. Default backend: BM25 scoring with Porter stemming and domain-aware query expansion. Optional: graph-based cross-reference boosting (--graph). Optional: vector/embedding backend (configure in mind-mem.json). Returns ranked results with block ID, type, score, excerpt, and file…
apply-proposal
Review and apply intelligence proposals generated by /scan. Uses atomic operations with rollback safety.