BC threejs-landscape
Build a live Three.js landscape that stays quiet behind a subject — a noise heightfield on a polar grid so resolution follows the lens, ground coloured by slope and moisture rather than by texture, instanced GPU grass whose wind costs nothing on the CPU, scattered stones, a gradient sky dome, a star field you can actually see, and a time-of-day system that cross-fades instead of cutting. Use for hero backdrops, product stages, scroll worlds, or any scene where a building, object, or figure has to sit in a place rather than float on a gradient.
Build a live Three.js landscape that stays quiet behind a subject — a noise heightfield on a polar grid so resolution follows the lens, ground coloured by…
As a process C 51/100 · Has gaps — 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: 4. 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 51/100
- 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. 1 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 9 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1965 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
- +1No license
- +2Single-language instructions
- +3Description length 549: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 9 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.