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AB adme-property-predictor

Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

ClawHub Agent Skills author: AIpoch v1.0.0 MIT-0 5 files body ≈ 7 093 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process B 77/100 · Nearly there — weak spots: consistency

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
B
77/100
Nearly there
Consistency w 8
40
When it triggers w 12
50
Tools and files w 18
60
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
  • 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 body-long SKILL.md body ≈ 7093 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "skill-author"

Process rating: all ten parameters 77/100

  • 40Consistency. Frontmatter name (adme-property-predictor) differs from the folder (adme-property-predictor-1)
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 7093 tokens
  • 100Steps. 234 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 6 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low 28 top-level sections: this looks like several domains in one skill

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)
  • -251 emoji in the instructions: noise for the model
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +2Single-language instructions
  • +3Description length 151: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 234 items
  • +3Output format is stated explicitly
  • +4Has examples (13 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: clean
This skill is a local small-molecule ADME prediction helper with some documentation and dependency hygiene issues, but no evidence of hidden, destructive, credential-seeking, or unrelated behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026