SKILLEMALL.ai

BC health-assistant-pro

Professional health assistant with medical report analysis, evidence-based supplement recommendations (100+ supplements), machine learning personalization, external API integration, and quality verification. Features include health report storage with trend analysis, ML-powered recommendations, Examine.com and ConsumerLab integration, brand quality testing, timing optimization, interaction checking, blockchain traceability framework, and comprehensive wellness guidance.

ClawHub Agent Skills author: LuckPoppy v1.5.0 MIT-0 21 files body ≈ 12 500 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, execution cost

AnalyzerData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • 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: 20. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 12500 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 59/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 40Execution cost. Instruction body is 12500 tokens: crowds the task out of the window
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 85Steps. 595 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 20 top-level sections: this looks like several domains in one skill
  • medium 130 test cases, all positive: not one "should refuse" or "should ask first"
  • low No test case covers injection arriving through data

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)
  • -2151 emoji in the instructions: noise for the model
  • -43 reference files, but SKILL.md never points to them: the model will not open them
  • -32 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 474: enough signal without eating the budget
  • +4Structure: 139 headings
  • +3Step-by-step instructions: 595 items
  • +3Output format is stated explicitly
  • +4Has examples (19 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
This health assistant mostly fits its stated purpose, but it handles sensitive medical data with weak storage safeguards and includes broad emergency, legal, and veterinary guidance that needs review.
LLM: suspicious (high) · 28 May 2026