AB nutrition-pro
The AI nutrition coach that actually gets to know you. Just say what you ate — nutrition-pro figures out the portion, looks it up, and logs it. No grams required. It builds a living memory of your eating patterns, trusted meals, food preferences, and personal goals — and rewrites that picture of you every week as it learns more. Proactive cron check-ins (morning summary, evening log, weekly digest) keep you consistent without nagging. Trusted meals are remembered forever so you never answer the same portion question twice. Zero installation required — no CLI, no pip, no binary. Works entirely through agent knowledge and memory. Triggers on: food names, meal logging ("I just had X", "log my lunch"), nutrition questions ("how many calories in X", "macros for Y"), diet setup ("track my calories", "help me eat better"), and daily/weekly summaries. Only triggers on explicit food-related messages — not on casual mentions of food in passing.
As a process B 70/100 · Nearly there — weak spots: result and completion, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 70/100
- 0Result and completion. Does not say what the result is
- 30Inputs and preconditions. Does not say what the process needs to start
- 70When it triggers. States when to use, but not when not to
- 70Failures and branches. 8 branches
- 70Execution cost. Instruction body is 4882 tokens
- 100Tools and files. No external tools needed
- 100Steps. 52 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 948: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +4Structure: 27 headings
- +3Step-by-step instructions: 52 items
- +4Has examples (8 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.