Agentlas OS is a local-first system for creating, storing, borrowing, and running specialist AI agents and temporary agent teams through supported hosts and models. It serves people who want reusable agents that remain available across computers and model workspaces, and the catalogue contains its skills, commands, hooks, agents, instructions, plugin, and rule.
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.
git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OSWrote 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/agents/agentlas-ai/agentlas-os/agentlas-core-engine-meta-agent)<a href="https://agentmods.dev/agents/agentlas-ai/agentlas-os/agentlas-core-engine-meta-agent"><img src="https://agentmods.dev/badge/agents/agentlas-ai/agentlas-os/agentlas-core-engine-meta-agent/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/agents/agentlas-ai/agentlas-os/agentlas-core-engine-meta-agent"><img src="https://agentmods.dev/badge/agents/agentlas-ai/agentlas-os/agentlas-core-engine-meta-agent.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 | $0.00038 | $0.00269 |
| Opus 5.5 | $0.00015 | $0.00108 |
| Sonnet 5.5 | $0.00008 | $0.00054 |
| Haiku 4.5 | $0.00004 | $0.00027 |
Grade A, and why
agentlas-core-engine-meta-agent 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
Agentlas Core Engine Meta-Agent Team
Route each request to one of the three public team members:
10-single-agent-builder20-multi-agent-team-builder30-agentlas-packager
Read AGENTS.md, .agentlas/mode-map.json, and the public mode classifier
first. Use the clarify question loop when missing details change the package.
Use .agentlas auto-activation contracts when local project continuity is part
of the output. Add .agentlas/global-commands.json and runtime command files or
aliases for every generated or packaged agent. Return global_commands so the
user knows the exact command to type. Write generated runtime instructions in
English: role prompts, skills, adapters, handoff contracts, return contracts,
and operating docs. Translate Korean or other-language source material into
English agent behavior; localized public copy and routing examples may use the
target user language. Keep adapters thin. Do not store secrets in generated
files. Verify packages before release.
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 · 26 lines · 38 tokens per session scan A 8f7e9d091ec8
agentlas-core-engine-meta-agent is an agent published in the GitHub repository agentlas-ai/Agentlas-OS (1,613 stars, last pushed yesterday), licensed Apache-2.0. It adds 38 tokens to every session and 269 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 2026-08-30.
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