BF google-calendar-automation
Lightweight Google Calendar integration with standalone OAuth authentication. No MCP server required.
Lightweight Google Calendar integration with standalone OAuth authentication.
As a process F 44/100 · Will not run — References files that are not bundled: scripts/gcal.py
This is a copy of a skill from another catalog; the rating counts the canonical one: google-calendar-automation (sickn33/agentic-awesome-skills)
The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills
How to improve
- The text references files that are not there: add them or drop the references.
For the model run — optional
- 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
- warning
missing-refreference to a missing file: scripts/gcal.py - note
frontmatter-keyunknown frontmatter key "risk" - note
frontmatter-keyunknown frontmatter key "source" - note
frontmatter-keyunknown frontmatter key "date_added"
Process rating: all ten parameters 44/100
Will not run. References files that are not bundled: scripts/gcal.py
- 0Tools and files. 1 referenced file(s) missing: scripts/gcal.py
- 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. 3 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 13 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1189 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)
- +3Description length 101: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +4Structure: 16 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (11 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.