AD max-throughput
Use when running compute-heavy work: model training, fine-tuning, evaluation, benchmarks, simulations, data preprocessing, dataset generation, compilation, test suites, or any long-running batch job, or when a job is slower than expected and needs performance tuning. Detect available CPU/GPU/memory resources, parallelize aggressively, then profile the running job to find the true bottleneck (data pipeline, CPU decode, GPU compute, VRAM, I/O) and tune batch size, DataLoader workers, pre-encoding, and precision accordingly to minimize wall-clock runtime.
Use when running compute-heavy work: model training, fine-tuning, evaluation, benchmarks, simulations, data preprocessing, dataset generation, compilation…
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 12. 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 43/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 100Steps. 22 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1773 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 558: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (2 of 5)
- +3All 3 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.