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BC virtual-screening

Virtual screening workflows for open and proprietary chemical libraries, including transformer-based screening and docking-based screening through SciMiner.

ClawHub Agent Skills author: SciMiner v1.0.7 MIT-0 2 files body ≈ 1 801 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Virtual screening workflows for open and proprietary chemical libraries, including transformer-based screening and docking-based screening through SciMiner.

As a process C 60/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureSoftware developmentAI and agentstype 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
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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 · 0

    ✓ No critical or high findings

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "credential_files"
    • note edit-residue the text marks something as outdated (lines 25): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 60/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 52 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1801 tokens
    • 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 156: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 52 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    The skill has a legitimate SciMiner virtual-screening purpose, but it tells the agent to run invocation code derived from mutable remote Markdown while using an API credential and uploading research files.
    LLM: suspicious (high) · 16 Sept 2026