Olares is an open-source personal cloud operating system that uses Kubernetes to combine a user’s hardware, storage, networking, applications, AI agents, and language models in one self-hosted environment. Individuals and small teams use it to run agents and local AI on computers they own while accessing their resources through a browser. The catalogue skills help users manage and operate Olares.
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 beclab/Olares --skill olares-searchgit clone --depth 1 https://github.com/beclab/OlaresWrote 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/beclab/olares/olares-search)<a href="https://agentmods.dev/skills/beclab/olares/olares-search"><img src="https://agentmods.dev/badge/skills/beclab/olares/olares-search/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/beclab/olares/olares-search"><img src="https://agentmods.dev/badge/skills/beclab/olares/olares-search.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00127 | $0.01569 |
| Opus 5.5 | $0.00051 | $0.00628 |
| Sonnet 5.5 | $0.00025 | $0.00314 |
| Haiku 4.5 | $0.00013 | $0.00157 |
Grade A, and why
olares-search 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 5d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 5d ago Changed · +3 lines · +14 tokens per session c7b7b1f8075c
- 1mo ago Changed · +9 lines b988f6f2e779
- 2mo ago First seen · 59 lines · 113 tokens per session scan A bf3119ae5bb8
olares-search is a skill published in the GitHub repository beclab/Olares (5,339 stars, last pushed yesterday), licensed AGPL-3.0. It adds 127 tokens to every session and 1,569 once invoked, about $0.0005 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.
Other skills, from other repositories
reminder
Set reminders for yourself or family members. Natural language time parsing supports relative times ("in 2 hours"), absolute times ("tomorrow 9:00 am", "at 14:30"), day offsets with an explicit time ("in 2 days at 17:00"), and shorthand ("30m", "2h").
todo
Manage personal and family todo lists via chat.
intent-recognition
Load before you build any new kept workflow or Agent. It decides which one to build, or what to ask first. Classifies automation requests using two decisions: anchor (which primitive owns the top-level control flow — workflow-anchored, agent-anchored, needs-clarification, or out-of-scope) and embedsother (whether the…
debugging-executions
Debug failed or wrong-output workflow executions using executions tools. Load when the user reports execution failures, unexpected node output, empty parameter values after a successful run, or a node showing a red or failed expression error. Also load when a live run you started for the user fails with an error from…
suggest-automations
Offer the three most common automations for a user as a single-choice card, from your own knowledge of their team and their apps, then build the one they choose once they confirm, or offer more. The system preloads it on onboarding threads, where the opening card already asked the user about themselves. Use it on…
tmux
Manage long-running shell sessions with tmux — start a detached session, run a long task, reattach later, capture output. Use when a task takes longer than a single tool call (build, test, log tail).