BC figma-shaders
**MANDATORY prerequisite** — load this skill before calling `create_shader` or `update_shader`. Use when the user asks to create, author, change, fix, or iterate on a shader effect, shader fill, custom effect, custom fill, or procedural shader in Figma.
A skill for Figma shader work—mandatory before calling create and edit functions. Two files, 1321 tokens. Quality score 84, process score 59: decent instructions, weak execution logic.
No critical findings, no medium or low ones either. Catch: never run on models, never sandboxed. Checked only statically—syntax and structure. Whether it actually handles real Figma shaders remains untested.
You can install it, but treat it as an API reference, not a finished tool. Test the first call manually.
MANDATORY prerequisite — load this skill before calling createshader or updateshader.
As a process C 59/100 · 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: 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 59/100
- 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. 7 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 21 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1321 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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 253: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 21 items
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.