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 agentmods add skills/agentlas-ai/agentlas-desktop/sharenpx skills add agentlas-ai/agentlas-desktop --skill sharegit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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-desktop/share)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/share"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/share.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.00247 |
| Opus 5 | $0.00014 | $0.00123 |
| Sonnet 5 | $0.00005 | $0.00049 |
| Haiku 4.5 | $0.00003 | $0.00025 |
Grade A, and why
share 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.
What it actually says
Skill Purpose
Packages and publishes a locally validated prototype to an external hosting target (e.g., Vercel, Netlify, or static bundle hosting), delivering a direct shareable URL.
Preconditions
- The prototype must have passed local validation and
$design-qa. - The user must explicitly request deployment or a public sharing link.
Steps
- Confirm Target: Determine the target platform or hosting mechanism requested by the user.
- Execute Production Build: Run
npm run buildor equivalent to ensure error-free asset compilation. - Deploy Bundle: Publish the compiled assets using deployment CLI tools or API integration to obtain a live URL.
- Deliver URL: Provide the generated URL to the user with a brief instruction on viewing the live deployment.
Outputs
- Live public prototype URL.
- Production build bundle.
Verification
- Confirm that the deployed URL is reachable and renders correctly in a web browser.
- Verify that no deployment errors or missing asset 404s occur in the live environment.
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 First seen · 26 lines · 27 tokens per session scan A 17cafcfc4568
share is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 27 tokens to every session and 247 once invoked, about $0.0001 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 2026-08-31.
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