AC meeting-cadence-optimizer
Analyze how the user's meeting load correlates with how their days actually feel -- using their own evening-debrief and morning check-in history -- and recommend an optimal meeting cadence. Use this whenever the user says things like "am I overbooked", "too many meetings", "optimize my schedule", "meeting cadence", "meeting burnout", "what's my meeting sweet spot", or wonders whether their schedule is wearing them down. Also good as a weekly (Sunday/Monday) review. Reads back Fulcra annotations and computes the numbers deterministically. Needs at least ~a week of evening debriefs to say anything useful. Do NOT use it to read raw calendar data alone or for objective health pulls.
Analyze how the user's meeting load correlates with how their days actually feel -- using their own evening-debrief and morning check-in history -- and…
As a process C 61/100 · Has gaps — weak spots: result and completion, 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: 0. 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 61/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (meeting-cadence-optimizer) differs from the folder (fulcra-meeting-cadence-optimizer)
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 12 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 823 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +3Output format is not stated: the model decides each time
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +4Description says when NOT to use the skill
- +3Description length 687: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 12 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.