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AB smiles-profiling

Comprehensive SMILES profiling through SwissTargetPrediction, PubChem, ADMETlab 3.0, ChEMBL, and PK-Smart. Use when given a single SMILES to extract predicted targets, exact identity and physicochemical baselines, known analogs and mechanisms, ADMET properties, and pharmacokinetic estimates; handles salts/counterions and degrades gracefully when any source is unavailable.

ClawHub Agent Skills author: Hendrik Schmitz v1.2.0 MIT-0 7 files body ≈ 594 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process B 67/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, progress reporting

ProcedureInfrastructuretype 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
93/100
safety, quality, tests
Safety 60%
98
Quality 40%
86
Run on models
none yet
Process rating
B
67/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token scripts/run_smiles_smoke.py:26
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      PK_TEXTAREA_ID = ''.join(['$', '$', 'ID-5…one'])
      quoted
    • low Secrets in code secret-high-entropy-token scripts/run_smiles_smoke.py:27
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      PK_BUTTON_ID = ''.join(['$', '$', 'ID-f…one'])
      quoted

    Files scanned: 7. 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 67/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 594 tokens
    • 100Running it twice. No mutating operations

    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)
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 374: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 20 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: clean
    This skill does what it advertises: it profiles one SMILES string using named external chemistry services, but users should treat submitted compounds as non-private.
    LLM: benign (high) · VirusTotal: · 29 May 2026