AC launch-sentiment-sweep
One-shot sweep of Reddit and X (formerly Twitter) reactions to a product launch, announcement, release, or news moment in a time window, read out as volume, representative quotes, themes, and notable accounts. Use whenever the user asks how people are reacting, what the sentiment or reception is, whether a launch landed, or what Reddit or X is saying about something that just happened, even if they never say "sentiment" or name a platform. For ongoing, repeated coverage of a brand or topic over time, use reddit-monitoring instead.
One-shot sweep of Reddit and X (formerly Twitter) reactions to a product launch, announcement, release, or news moment in a time window, read out as volume…
As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, consistency, 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 58/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (launch-sentiment-sweep) differs from the folder (veezee-launch-sentiment-sweep)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Steps. 14 steps
- 100Execution cost. Instruction body is 1632 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)
- +1No license
- +2Single-language instructions
- +3Description length 536: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 14 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.