BC visual-gen-component-test
Design, implement, calibrate, and run deterministic TensorRT-LLM VisualGen L1 operation tests and L2 component-by-feature parity tests. Use for DiT, VAE, text-encoder, scheduler, parallelism, quantization, caching, compile, or model-specific component correctness; not for perceptual model evaluation or full-pipeline LPIPS quality gates.
Promises a test framework for VisualGen components in TensorRT-LLM: DiT, VAE, text-encoder, scheduler, parallelism, quantization. Explicitly excludes perceptual evaluation and LPIPS gates. One file with 1565 tokens, no runnable scripts. Quality score 84, safety 100, but process coverage only 52%. No critical errors, clean lint.
The catch: this is a workflow description, not executable tests. You get instructions on how to write tests, not the tests themselves. Install if you're already deep in TensorRT-LLM and need a reference for L1/L2 parity checks. Otherwise it's just documentation.
Design, implement, calibrate, and run deterministic TensorRT-LLM VisualGen L1 operation tests and L2 component-by-feature parity tests.
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 52/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 30 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1565 tokens
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
- +5Description has no quoted example phrases that should trigger the skill
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 338: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 30 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.