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-desktop --skill ideategit 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/ideate)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/ideate"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/ideate/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/agentlas-ai/agentlas-desktop/ideate"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/ideate.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 | $0.00034 | $0.00677 |
| Opus 5.5 | $0.00014 | $0.00271 |
| Sonnet 5.5 | $0.00007 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
ideate 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.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Purpose
Explores product concepts by generating 3 distinct, production-quality visual design options, refines them based on user feedback, and persists the selected option as the pinned visual target (target-design.png) for faithful code implementation.
Preconditions
- The minimum design brief (target product/screen and intended user outcome) must be resolved via
$get-context. - No code scaffolding or file modifications are permitted prior to visual concept selection.
- Strictly adhere to
$critical-overrides.
Steps
- Analyze Brief & Context: Review the resolved design brief, existing design tokens, brand palettes, and any reference screenshots.
- Determine Target Dimensions:
- Mobile app:
390 x 844 - Tablet app:
834 x 1194 - Desktop app / Dashboard / SaaS:
1440 x 1024 - Landing / Marketing page:
1440wide scrollable
- Mobile app:
- Generate 3 Distinct Visual Directions:
- Construct 3 separate prompt concepts with distinct information hierarchy, layout strategies, and interaction models.
- Run generation for each concept independently using the built-in image generation tool (do not combine multiple ideas into one image).
- For mobile apps, generate content-only views without device bezels, notches, status bars, or home indicators.
- Order and Present Selection:
- Number the results strictly in the order they appear in the chat context: Option 1, Option 2, Option 3.
- Send the concise selection prompt:
"Which option should I build: 1, 2, or 3? Or tell me what you'd like to refine or personalize first."
- Incorporate Feedback Loop: If the user requests refinements or remixes between options, generate updated visual concepts before proceeding.
- Pin Target Design:
- Once the user confirms Option N, save that image file to
assets/target-design.pngas the persistent single source of truth. - Acknowledge the selection (e.g.,
"Option 2 confirmed and pinned as target design! Building the interactive prototype now...").
- Once the user confirms Option N, save that image file to
- Handoff to Build: Immediately transition to
$image-to-codewithassets/target-design.pngbound as the active visual anchor.
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 Changed · +4 lines 6a0667456e33
- 1mo ago First seen · 44 lines · 34 tokens per session scan A 0aa8663d13e6
ideate is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 677 once invoked, about $0.0001 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 2026-08-31.
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