SKILLEMALL.ai

AB fitness-assistant

Plan daily meals and workouts from a user's age and health profile with customizable ingredients, then schedule localized plans via OpenClaw automations.

ClawHub Agent Skills author: L1MuFeng v0.1.4 MIT-0 11 files body ≈ 1 460 tokens Open the sourceclawhub.ai analyzed 3 d ago

Plan daily meals and workouts from a user's age and health profile with customizable ingredients, then schedule localized plans via OpenClaw automations.

As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions

ProcedureData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
55
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 68/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 55Failures and branches. 1 branches
  • 85Steps. 19 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1460 tokens
  • 100Running it twice. Mutating operations check current state
  • 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 153: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (7 of 8)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
The skill mostly matches its fitness-planning purpose, but it stores sensitive health/profile details for reuse, copies them into recurring automation prompts, and has a calorie safety mismatch for older adults.
LLM: suspicious (high) · 13 Sept 2026