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 ai-ecoverse/slicc --skill meminfogit clone --depth 1 https://github.com/ai-ecoverse/sliccWrote 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/ai-ecoverse/slicc/meminfo)<a href="https://agentmods.dev/skills/ai-ecoverse/slicc/meminfo"><img src="https://agentmods.dev/badge/skills/ai-ecoverse/slicc/meminfo.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00064 | $0.00401 |
| Opus 5 | $0.00032 | $0.00200 |
| Sonnet 5 | $0.00013 | $0.00080 |
| Haiku 4.5 | $0.00006 | $0.00040 |
Grade A, and why
meminfo 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 2d 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
meminfo — agent-cluster memory diagnostics
meminfo measures real memory usage across the agent cluster — the kernel worker plus its dedicated workers (script realms, vpod pods, ffmpeg, speech) — via the browser's performance.measureUserAgentSpecificMemory().
When to reach for it
- A command died with an out-of-memory error, or the kernel feels degraded after heavy WASM work.
- Before/after comparisons: measure, run the suspect workload, measure again, diff the attribution rows.
- Deciding whether to
vpod stop/killa heavy background unit before starting another.
Usage
meminfo # human-readable: total + per-attribution rows, largest first
meminfo --json # raw measurement for scripted diffing
Rows attribute bytes to a scope and URL (e.g. DedicatedWorkerGlobalScope …/kernel-worker.js), with types like JavaScript, DOM, Shared. Zero-byte rows are dropped; only --json shows them.
Expectations and limits
- Requires a cross-origin-isolated runtime. The hosted leader is one; embedded floats (Cherry, Electron overlay) are not and report why instead — that error is expected there, not a bug.
- The browser randomizes measurement timing (anti-fingerprinting), so a call may take a few seconds — do not treat the delay as a hang.
- The measurement covers the calling agent cluster, not the whole browser; other tabs and cross-origin iframes are out of scope.
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.
- 2d ago First seen · 35 lines · 64 tokens per session scan A f819f468175b
meminfo is a skill published in the GitHub repository ai-ecoverse/slicc (30 stars, last pushed yesterday), licensed Apache-2.0. It adds 64 tokens to every session and 401 once invoked, about $0.0003 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 2026-09-05.
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