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 user-contextgit 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/user-context)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/user-context"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/user-context/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/user-context"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/user-context.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 | $0.00023 | $0.00273 |
| Opus 5.5 | $0.00009 | $0.00109 |
| Sonnet 5.5 | $0.00005 | $0.00055 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade A, and why
user-context 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
Skill Purpose
Handles user-specific preferences, saved design tokens, brand assets, product repositories, and onboarding context across sessions.
Preconditions
- The user requests to set up, inspect, or update saved preferences, brand guidelines, product URLs, or design assets.
- Initial setup context is required before running design workflows.
Steps
- Inspect Stored State: Check for the existence of
.state/user-context.mdor saved configuration files. - Classify Intent: Identify whether the request is initial onboarding, viewing saved context, or updating specific design parameters (e.g., brand colors, default browsers, token paths).
- Update or Retrieve Context: Record or output product URLs, Figma tokens, typography preferences, and share endpoints.
- Respond Succinctly: Summarize the updated or recalled preferences and signal readiness for design tasks.
Outputs
- Maintained and updated
.state/user-context.mdstate file. - Concise summary message displayed to the user.
Verification
- Validate that all stored paths, URLs, and token configurations are well-formed.
- Ensure that setup-only requests do not trigger premature prototype builds or file scaffolding.
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 · 23 tokens per session scan A 029f4f78f9bc
user-context is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 23 tokens to every session and 273 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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