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/amanasmuei/amem/remembernpx skills add amanasmuei/amem --skill remembergit clone --depth 1 https://github.com/amanasmuei/amemWrote 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/amanasmuei/amem/remember)<a href="https://agentmods.dev/skills/amanasmuei/amem/remember"><img src="https://agentmods.dev/badge/skills/amanasmuei/amem/remember.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 | $0.00038 | $0.00330 |
| Opus 5 | $0.00019 | $0.00165 |
| Sonnet 5 | $0.00008 | $0.00066 |
| Haiku 4.5 | $0.00004 | $0.00033 |
Grade A, and why
remember 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 4d 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
Quick Memory Store
The user wants to store a memory. Parse their input and call the appropriate amem tool.
Instructions
-
Parse the user's request to determine:
- Content: What to remember
- Type: correction (user correcting you), decision (architecture choice), pattern (coding style), preference (tool choice), topology (codebase location), fact (general knowledge)
- Confidence: 1.0 for corrections, 0.9 for decisions, 0.7-0.8 for others
-
Call
memory_storewith the parsed fields. -
If the memory relates to an existing one, also call
memory_relateto link them.
Examples
- "remember never use any type in TypeScript" → correction, confidence 1.0
- "remember we chose PostgreSQL for ACID compliance" → decision, confidence 0.9
- "remember I prefer pnpm over npm" → preference, confidence 0.8
- "remember auth module is in src/auth/" → topology, confidence 0.7
Important
- Always use
memory_store, notmemory_extractfor single memories - Set appropriate tags based on the content
- If this sounds like a correction ("don't", "never", "stop"), type = correction, confidence = 1.0
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.
- 4d ago First seen · 33 lines · 38 tokens per session scan A f56a6e5c7b56
remember is a skill published in the GitHub repository amanasmuei/amem (2 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 330 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.
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