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.
npx skills add agentlas-ai/Agentlas-OS --skill agentlas-operationsgit 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/skills/agentlas-ai/agentlas-os/agentlas-operations)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/agentlas-operations"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/agentlas-operations.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 | $0.00067 | $0.00993 |
| Opus 5 | $0.00034 | $0.00496 |
| Sonnet 5 | $0.00013 | $0.00199 |
| Haiku 4.5 | $0.00007 | $0.00099 |
Grade A, and why
agentlas-operations 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 8d 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 시스템 운용 (One 운용 스킬)
도구의 존재와 이름은 이 문서가 아니라 INDEX.md가 정본이다(릴리스 빌드가 라이브 레지스트리에서 자동 생성 — 손 목록은 반드시 썩는다). 이 문서는 절차만 다룬다.
1. 편성 (hep-network) — 로스터 재사용이 항상 먼저
workforce.goal_context로 활성 로스터부터 확인한다. 재사용으로 충분하면 모집하지 않는다.- 진짜 공백일 때만: 축약(redacted) 워크오더 1장 →
workforce.search_candidates(sourceScope=network= local+cloud+hub 연합).- ⚠️
requiredSkills에 시드 온톨로지 ID를 걸면 실후보 전원이 가짜 결격을 단다(실측).
- ⚠️
- 선발은 호스트 LLM이 한다(연합은 점수 매기지 않는다) →
workforce.validate_selection(연합 결과 원본 그대로 — 축약본은 거절됨) →workforce.prepare_execution(projectDir 필수). - 준비 성공은 자동으로 로스터에 바인딩된다. 바인딩은 명시적
workforce.complete_goal까지 유지 — 24시간 Hub 리스는 과금 단위지 바인딩 종료가 아니다. - 소스 스코프는 정확하게: network=전체, local/cloud/hub는 제한 스코프이지 폴백 계층이 아니다.
2. 자동화 (hep-graph)
- 반복 작업은 대화로 그래프를 만들어 저장한다(
/hep-graph). 실행 중 승인 게이트는 없다 (오너 결정 2026-08-09: 승인은 만들 때 한 번). - "항상 허용"을 그래프 digest에 걸지 않는다 — digest가 바뀌면 바로 그 실행의 재개가 거부된다. 사람의 결정은 실행 밖 기록에 둔다.
3. 자산 (cargo / Agent Cloud / marketplace)
- 내 서랍:
cargo.*(드래프트·라이브러리, 로그인 필요). 오너 자산 검색은/hep-cloud. - 공개 검색:
marketplace.search_agents(로그인 불필요) — kind가cloud-callable이면get_runtime_bundle(BYOM: 내 모델이 번들을 실행, 서버는 LLM을 돌리지 않는다),install-only면get_manifest로 설치. - 도구가 안 보이면 단정 전에
agentlas_resolve_plugins— 미설치 ≠ 부재. 설치는 사용자 결정. - 서버 거절(
insufficient_credits·owner_only등)은 그 문구 그대로 보고한다. 지정 원격 에이전트를 로컬 폴백이 대신 실행한 척하지 않는다.
4. 메모리 계약 (One 워커로서)
- 작업 전
agentlas.memory.preflight— 아는 사실 재유도 금지. - durable은 직접 쓰지 않는다. 답 끝의
## Memory Events봉투가 유일한 기록 경로이고 런타임이 티켓으로 포장한다. 근거 없는 fact/decision/procedure는 hypothesis로 강등된다. - 앞선 durable을 대체하는 학습이면 candidate에
"supersedes":"<h:16hex>"를 넣는다 (회수에서 숨겨질 뿐 삭제되지 않는다). - One 서랍(
~/.agentlas/one/)은 읽기 자유·쓰기 금지(D3) — 편집 시도는 PreToolUse가 거절한다.
5. 표면별 함정 (실측 기반)
- 플러그인 MCP 서버는 세션 시작 때 캐시에서 로드된다 — 릴리스 직후엔 새 세션에서 검증.
- 원격 서버는 관대하고 로컬 Core는 엄격하다(validate에는 연합 결과 원본 전체를 넘길 것).
- Hub 발행 503
WRITE_MODE=blocked는 서버 상태이지 패키지 결함이 아니다.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 54 lines · 67 tokens per session scan A c70069629740
agentlas-operations is a skill published in the GitHub repository agentlas-ai/Agentlas-OS (1,103 stars, last pushed yesterday), licensed Apache-2.0. It adds 67 tokens to every session and 993 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 2026-08-30.
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project-init
Scaffold an unconfigured directory into a configured pi project. Interactive, profile-driven: previews the planned writes, then writes AGENTS.md, .pi/settings.json and prompt files — optionally also a knowledge base, an openspec/ scaffold, and user-global /.pi/agent/settings.json. Use on a bare directory, or when the…
kano-backlog
Prioritize and refine a GitHub Issues backlog with the Kano model — categorize every open issue as Must-be, Performance, Attractive, Indifferent, or Reverse, apply Kano + priority labels back to GitHub automatically, and recommend the single best next issue to pick up. Use this whenever the user wants to triage…
linear
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gh-issues
Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5]…
slack
Use when you need to control Slack from OpenClaw via the slack tool, including reacting to messages or pinning/unpinning items in Slack channels or DMs.