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 amanasmuei/amem --skill recallgit 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/recall)<a href="https://agentmods.dev/skills/amanasmuei/amem/recall"><img src="https://agentmods.dev/badge/skills/amanasmuei/amem/recall/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/amanasmuei/amem/recall"><img src="https://agentmods.dev/badge/skills/amanasmuei/amem/recall.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 | $0.00039 | $0.00235 |
| Opus 5.5 | $0.00016 | $0.00094 |
| Sonnet 5.5 | $0.00008 | $0.00047 |
| Haiku 4.5 | $0.00004 | $0.00023 |
Grade A, and why
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 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.
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 Search
The user wants to find memories. Search amem and present results clearly.
Instructions
-
Take the user's query as the search terms.
-
Call
memory_recallwith:query: the search termslimit: 10compact: true (for efficiency)
-
If results are found, present them clearly:
- Show type, content preview, and confidence
- Offer to show full details for any specific memory
-
If no results, try
memory_multi_recallfor a deeper 4-strategy search. -
If still nothing, suggest the user store some memories first.
Examples
- "what do you remember about auth?" → searches for authentication-related memories
- "recall TypeScript rules" → finds corrections and patterns about TypeScript
- "search memory for database decisions" → finds database-related decisions
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 · 32 lines · 39 tokens per session scan A 78862dd291f7
recall is a skill published in the GitHub repository amanasmuei/amem (2 stars, last pushed 6mo ago), licensed MIT. It adds 39 tokens to every session and 235 once invoked, about $0.0002 per session on Opus 5.5. 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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