BC tui-tests
Use when writing, fixing, or reviewing tests that render OpenTUI (testRender, createTestRenderer, createAppFixture) in packages/tui or packages/cli. Covers frame waits (waitForFrame, waitFor, waitForVisualIdle, flush), started vs non-started renderers, injected clocks, mock keyboard and mouse input, terminal sizes, cleanup and shared paths, and the 30s budget. Also use when a TUI test fails with "Timed out waiting for frame predicate after N passes", or when a tui test file is slow. For cli subprocess and service tests, use the unit-tests skill.
A skill for testing OpenTUI in packages/tui and packages/cli — helps when writing, fixing, or reviewing tests with testRender, createTestRenderer, createAppFixture. Covers frame waits (waitForFrame, waitFor, waitForVisualIdle, flush), started vs non-started renderers, injected clocks, mock keyboard and mouse input, terminal sizes, cleanup, and shared paths. Triggers on "Timed out waiting for frame predicate after N passes" errors too.
3549 tokens in the docs. Quality 84/100, process 54/100. No critical issues, no lint errors. The skill is narrow: if you're writing exactly these tests and hit frame timeouts or slow runs — install it. For cli subprocess and service tests, use unit-tests skill instead.
Covers frame waits (waitForFrame, waitFor, waitForVisualIdle, flush), started vs non-started renderers, injected clocks, mock keyboard and mouse input…
As a process C 54/100 · 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 54/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 14 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 (bash, web) that frontmatter does not declare
- 100Steps. 41 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3549 tokens
- 100Progress reporting. Reports progress
- low The response is described with custom markup (3 tags): a typed call is more reliable
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 551: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (0 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.