AC canary-watch
Use this skill to monitor and verify a deployed URL after releases — checks HTTP endpoints, SSE streams, static assets, console errors, and performance regressions after deploys, merges, or dependency upgrades. Smoke / canary / post-deploy verification.
Monitors deployed URLs after releases: checks HTTP endpoints, SSE streams, static assets, console errors, and performance regressions. Standard smoke-test job, nothing exotic. No critical findings in the files, code quality sits at 87, process score 54. Never ran on models, no sandbox testing.
Use it for post-deploy verification if you're already doing similar checks elsewhere. Worth installing to skip the boilerplate, but don't expect breakthroughs—it's a workhorse, not a revelation.
Use this skill to monitor and verify a deployed URL after releases — checks HTTP endpoints, SSE streams, static assets, console errors, and performance…
As a process C 54/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
The same skill appears in 2 more places: RA-Skills, RA-Skills
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
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 60Tools and files. Uses tools (git) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 100Steps. 11 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 691 tokens
- 100Progress reporting. Reports progress
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)
- +1No license
- +2Single-language instructions
- +3Description length 253: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 11 items
- +3Output format is stated explicitly
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.