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 saved-presetsgit 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/saved-presets)<a href="https://agentmods.dev/skills/lingtai-ai/lingtai/saved-presets"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/saved-presets/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/saved-presets"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/saved-presets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 51 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.01205 |
| Opus 5 | $0.00017 | $0.00602 |
| Sonnet 5 | $0.00007 | $0.00241 |
| Haiku 4.5 | $0.00003 | $0.00120 |
Grade A, and why
preset-skill-op-saved-presets 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 11d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Saved presets
Evidence: tui/internal/preset/preset.go:213-610,731-891.
Templates vs saved — the directory is the marker
~/.lingtai-tui/presets/templates/ holds the 12 TUI-owned built-ins;
~/.lingtai-tui/presets/saved/ holds user-owned clones. There is no in-band
"is_template" field — Source (SourceTemplate / SourceSaved) is set
purely from which directory a file was loaded from, and is never
serialized. Two presets with byte-identical JSON but different directories
are treated differently.
Load order — saved wins
Load(name) (preset.go:270-293) looks in saved/ first, then
templates/. A saved preset with the same name as a template wins — the
user's variant overrides. List() (preset.go:217-240) returns saved
presets first (alphabetical), then the templates. Treat the templates'
exact relative order as an implementation detail; current source does not
reliably guarantee the full BuiltinPresets() sequence.
A saved preset falls back to the template only when the saved file is
genuinely absent; a malformed saved override blocks fallback. Load
distinguishes "file doesn't exist" (errors.Is(err, fs.ErrNotExist), falls
through to the next directory) from a real failure — invalid JSON,
permission error, or a path that is a directory — which is surfaced to the
caller with the underlying cause preserved rather than collapsed into a
generic not-found. Only when both the saved and template files are
absent does Load return the not-found error.
Structural Validate() is not auto-invoked by Save or Load
Validate() (preset.go:358-410) mirrors the kernel's own load-time
validation gauntlet (description.summary non-empty; a non-empty tier
must be one of 1..5, while an empty tier is allowed;
llm.provider/llm.model non-empty; context_limit shape). It exists so
callers — notably the preset editor's commit path — can refuse to save
something the kernel would refuse to load. Neither Save nor Load calls
it automatically; a caller that writes a preset without checking
Validate() first can write a structurally invalid file. The editor calls
it explicitly (see reference/operations/availability-save-gate/SKILL.md).
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.
- 11d ago First seen · 97 lines · 34 tokens per session scan A bd4f5c3c410b
preset-skill-op-saved-presets is a skill published in the GitHub repository Lingtai-AI/lingtai (673 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 1,205 once invoked, about $0.0002 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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