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 lovstudio/skills --skill optimize-tauri-backendgit clone --depth 1 https://github.com/lovstudio/skillsWrote 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/lovstudio/skills/optimize-tauri-backend)<a href="https://agentmods.dev/skills/lovstudio/skills/optimize-tauri-backend"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/optimize-tauri-backend/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/lovstudio/skills/optimize-tauri-backend"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/optimize-tauri-backend.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 | $0.00125 | $0.02858 |
| Opus 5.5 | $0.00050 | $0.01143 |
| Sonnet 5.5 | $0.00025 | $0.00572 |
| Haiku 4.5 | $0.00013 | $0.00286 |
Grade A, and why
lov-optimize-tauri-backend 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 24d 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tauri 后端调优 · Tauri Backend Tuner
Use this skill to turn a growing Tauri backend into a smaller, more stable, more pleasant development surface. The goal is not magical Rust HMR. The goal is to reduce how often Rust changes are needed, make Rust restarts less disruptive, and keep the Tauri command boundary small, typed, and auditable.
When to Use
- The user is developing with
pnpm tauri dev/npm run tauri devand Rust file changes keep closing and reopening the app. src-tauri/src/lib.rshas become a God file.- The app exposes 100+ or 200+
#[tauri::command]functions. - Frontend code scatters raw
invoke("command_name")strings across many components. - Long-running
invoke/Channelcalls cause stale callback warnings after reload or HMR. - The user wants backend modularization, command consolidation, better dev scripts, or restart-state recovery.
Core Position
State this clearly when asked about hot reload:
| Surface | Dev behavior |
|---|---|
| Frontend Vite / React | real HMR |
| Rust / Tauri backend | recompile + restart native process |
| Best optimization | separate stable Tauri shell from volatile domain logic, then make restarts cheap |
Do not promise Rust backend HMR inside Tauri. Tauri's Rust-side "hot reload" means watch, rebuild, and restart.
Workflow
Step 1: Read Local Rules First
Before changing files, inspect project instructions and config:
pwd
find .. -name AGENTS.md -print
find .. -name CLAUDE.md -print
rg -n "beforeDevCommand|devUrl|frontendDist|tauri dev|--no-watch|invoke\\(|#\\[tauri::command\\]|generate_handler" package.json src-tauri src 2>/dev/null
Honor local constraints. If the repo says not to run pnpm build, do not run
it. Prefer rg and small file reads. Do not revert unrelated user changes.
Step 2: Baseline the Backend
Collect objective numbers before proposing or editing:
wc -l src-tauri/src/lib.rs 2>/dev/null
find src-tauri/src -maxdepth 3 -type f -name "*.rs" -print0 | xargs -0 wc -l | sort -n | tail
rg -n "#\\[tauri::command\\]" src-tauri/src | wc -l
rg -n "generate_handler!|invoke\\(" src-tauri/src src 2>/dev/null
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 24d ago Changed 057c8cbd4b37
- 1mo ago Changed 8d861345bdbe
- 1mo ago First seen · 349 lines · 125 tokens per session scan A 0bc90e87f472
lov-optimize-tauri-backend is a skill published in the GitHub repository lovstudio/skills (72 stars, last pushed 6d ago), licensed MIT. It adds 125 tokens to every session and 2,858 once invoked, about $0.0005 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 2026-09-03.
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