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 Lingtai-AI/lingtai --skill geminigit clone --depth 1 https://github.com/Lingtai-AI/lingtaiWrote 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/lingtai-ai/lingtai/gemini)<a href="https://agentmods.dev/skills/lingtai-ai/lingtai/gemini"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/gemini/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/lingtai-ai/lingtai/gemini"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/gemini.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 | $0.00020 | $0.00747 |
| Opus 5 | $0.00010 | $0.00374 |
| Sonnet 5 | $0.00004 | $0.00149 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
preset-skill-gemini 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 2d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gemini preset revision
Use this child for the named built-in gemini preset. geminiPreset in tui/internal/preset/preset.go:1332 uses Google's native gemini adapter, the stable gemini-3.8-flash default, GEMINI_API_KEY, web_search, skills, and a native vision capability. gemini-3-flash-preview was the previous preview default. It has no base_url or OpenAI-compatibility override.
Native inline image input is distinct from the explicit LingTai vision
capability; both use the shipped Gemini provider rather than a fallback.
The explicit LingTai vision capability is a separate tool path from native
multimodality.
Template-specific settings
Read Google's official Gemini model guide and image understanding guide. For the actual account-served list, call the official Gemini API models.list endpoint, GET https://generativelanguage.googleapis.com/v1beta/models, or use the equivalent official SDK method; follow pagination and filter for models supporting the operation used by this adapter. This is a native provider catalog, not a gateway alias. Keep preview/stability and image-input facts separate, and do not infer a LingTai MCP from native multimodality.
TUI surfaces to revise
Start at geminiPreset in tui/internal/preset/preset.go. Gemini intentionally has no providerModels picker or modelHasVision entry in tui/internal/tui/preset_editor.go, so revise only the constructor's model, credential, and vision capability unless that design changes. If capability display changes, inspect the fixed mandatoryCapRow rendering; there is no Gemini base_url surface. Follow tui/CONTRACT.md for free-text providers.
Reviewed deterministic revision
Prepare an evidence-bound manifest and explicit input, then run lingtai-tui presets revise --manifest PATH --input PATH --mode dry-run|check|apply [--output-dir PATH]. Review the JSON plan, use dry-run/check before apply, and apply only to a new explicit output directory. revision.go validates hashes, route bindings, and evidence and preserves unowned bytes. This amendment revises the constructor default to gemini-3.8-flash; the model remains free text and is intentionally absent from providerModels and modelHasVision.
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.
- 2d ago Changed · +32 lines · -1 tokens per session 364a8b03c13a
- 11d ago First seen · 34 lines · 21 tokens per session scan A 1ec5c1e05022
preset-skill-gemini is a skill published in the GitHub repository Lingtai-AI/lingtai (670 stars, last pushed yesterday), licensed Apache-2.0. It adds 20 tokens to every session and 747 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 2026-08-30.
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