AD dataset-creation-training
End-to-end workflow for building, cleaning, annotating, splitting, and training on custom datasets. Covers data collection, quality assurance, format standardization (Alpaca, ShareGPT, ChatML, JSONL), synthetic data generation, train/val/test splitting, and training integration. Use when the user wants to "create a dataset", "prepare training data", "fine-tune a model", "clean data for ML", or "generate synthetic examples".
End-to-end workflow for building, cleaning, annotating, splitting, and training on custom datasets.
As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
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: 2. 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 47/100
- 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
- 40Consistency. Frontmatter name (dataset-creation-training) differs from the folder (dataset-creation-skill)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 16 steps
- 100Execution cost. Instruction body is 625 tokens
- 100Running it twice. No mutating operations
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
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 427: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 16 items
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.