AC tauri-2-app
Scaffold a new Tauri 2 desktop app (Rust backend + TypeScript/React frontend) using a thin-frontend / rich-Rust-backend architecture with modular commands, trait-based platform abstractions, encrypted secrets at rest, single-instance enforcement, a self-hosted updater, and a cross-platform CI matrix. Three modes: full project scaffold, add a Tauri command slice, add a Rust module slice. Use this skill whenever the user says "create a new Tauri app", "scaffold a Tauri 2 project", "new desktop app with Tauri", "Tauri + React project", "add a Tauri command end-to-end", "add a Rust module to my Tauri app", "my Tauri conventions", or "/tauri-2-app" — even if they don't name the skill. Full good-pattern catalog and pitfall list live in the skill body.
Scaffold a new Tauri 2 desktop app (Rust backend + TypeScript/React frontend) using a thin-frontend / rich-Rust-backend architecture with modular commands…
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
✓ No critical or high findings
Medium and low: 1
-
low Secrets in code
secret-high-entropy-tokenreferences/good-patterns.md:354High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"Win3…use",
quoted
Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 10648 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 59/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 26 mutating operations with no state check
- 40Execution cost. Instruction body is 10648 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
- 85Steps. 192 steps, 2 vague phrases
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 16 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (13 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -5TODO / placeholder text left in the skill
- -226 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +3Description length 755: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 192 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.